designs that lead buy at amazon excerpt--200 new landscapes changing who designs and what.pdf

DESIGNS THAT LEAD,LEADING DESIGN,

LEADERS WHO DESIGN,DESIGNING LEADERS,LEADING DESIGNERS



DESIGNS THAT LEAD

120 Creativity Models30 Creativity Sciences60 Innovation Models

Who Designs--AutomataHow Design--Creation Models

Design Limits--kinds of:engagement, sociality,

space, art impacts, com-putation types, cultures

Event Design--mass eventsLeadership Designs--various

function delivery meansSystem Design--non-linear

designings/designsMedia/Interface Design--via monastic level inno-

vationsQuality Designing--viatotalizing bodies of

knowledgeTech Cluster Design--via complete maps of

Silicon Valley dynamics

DESIGNS THAT LEAD

By Richard Tabor Greeneemail: [email protected]

amazon.com/author/richard-tabor-greeneCopyright 2015 by RTGreene, Rights Reserved

PLURAL MODELS FOUNDATIONS--a design grammarNEW WHO for DESIGN--social design automataPLURAL CREATIVITY SCIENCES & MODELS --at singularities in design processesCULTURES DESIGNING & DESIGNED--the unconscious designerEMERGENT DESIGN CONSTRAINTS--types of engagement, sociality, spaces, art aims, computation types, culturesEVENTS DESIGNING & DESIGNED--bureau to process to event and event to process to bureauLEADERS DESIGNING & DESIGNING LEADINGS--replacing fixed social classes as way to deliver directionality REPLACING ANCIENT MEDIA/INTERFACES--escaping harms/habits from prose, sitter-creating old onesQUALITY DESIGNS, DESIGNING QUALITIES--via totalizations of bodies of knowledgeDESIGNING TECH CLUSTERS--capturing the biologics and natural selection dynamics of Silicon ValleySYSTEMS DESIGNING AND DESIGNED--getting out of linear static educations, professions, toolsINDIVIDUAL MODELS OF ALL OF DESIGN--remapping it all to run on emergent new landscapes of design, thought, creativity, career, technology

CONTENTS:

54 Excellence Sciences, 30 Creativity Sciences,120 Creativity Models, 60 Innovation Models,64 Natural Selection Dynamics, Automata Designand Designing, Creativity Models as Design Grammar,Design Cultures and Design Of Cultures, Kinds ofSpace, Purposes of All Arts, Type of ComputationalSystem in Dialog, The Cultural Work of Innovating,Event Design and Design Events, Invent Events,Design of Alternatives to Leaders for Leading,Replacing Prose-Brainstorms-Meetings-Discussions,Totalizations of Bodies of Knowledge beyond Quality,Replicating Silicon Valley in Silicon Valley & Elsewhere,Non-Linear Systems Designing and Designed.

8 NEW LANDSCAPES OF

DESIGN-CREATING-LEADING

East Asian, Nordic, Santa Fe,Non-Western, Non-linear,Biologics, Evolutionary DESIGNGenres, Means, New Aims

Your Door to CreativityAre You Creative? 60 ModelsAre You Creative? 128 StepsGetting Real about CreativityYour Door to Creativity

Are You Educated?

Are You Educated, Japan, EU, USA, China--300 CapabilitiesManaging Self--128 DynamicsPower from Brain TrainingKnowledge Epitome--A New Kind of University

64 Capabilities

72 Innovation ModelsCreativity Leadership ToolsCreativity Leader 4 Year Curriculum

Your Door to Culture PowerCulture PowerGlobal Quality

Thinking DesignDesigns that Lead

Are You Effective? 100 Methods54 Excellence Sciences--Redoing PlatoSuperSelling--33 cases & MethodsManaging ComplexityTaking Place--Creative City Theory and Practice

INNOVATIONS IN INNOVATION and in 29 OTHER CREATIVITY

SCIENCES

These are the 24books I wrote be-fore I wrote thisbook you are nowreading. Left toright, top to bot-tom they cover:creativity, edu-catedness, inno-vation, culture,design, manage-ment, and all formsof creativity.

There is a flowfrom 2 bases attop through 3application areasto sheer workimpact, to gene-rating creativeoutcomes of 30different genresof creation asoutcome.

Creativity Educatedness

Innovation Culture Design

Management

Capstone Book

RICHARD’S 24 BOOKSBY CATEGORY

EMAIL: [email protected]

THERE IS A BOOK OF BOOKS by RTGreene having 50+ page EXCERPTS of ALL 24 BOOKS

1. Your Door to Creativity--42 models summarized 8 in detail--PAGE 4--86

2. Are You Creative? 60 Models--world’s most comprehensive--PAGE 88-142

3. Are You Creative? 128 Steps--to becoming creator & creating--PAGE 143-192

6. 72 Innovation Models--a grammar of changes that change history--PAGE 304-367

7. Creativity Leadership Tools--intructors’ manual & student text--PAGE 368-420

8. Creativity Leader--managing creativity of self & other, everywhere--PAGE 421-468

9. Thinking Design--160 approaches, tools, leading design, designs that lead--PAGE 469-538

10. Designs that Lead, Leaders who Design--an article collection--PAGE 539-594

11. Are You Educated?--an empirical science-based definition as 48 capabilities--PAGE 595-657

12. Are You Educated, Japan, China, EU, USA--300 capabilities from 5 models--PAGE 658-728

13. Managing Self--128 Dynamics--redoing Plato, Freud, Sartre, Kegan,Zen--PAGE 728-805

14. Power from Brain Training--exercises for 225 brain modules--PAGE 806-866

15. Knowledge Epitome--200 new face to face tools from revising ancient media--PAGE 867-933

16. Your Door to Culture Power--the shared practiced routines model--PAGE 934-989

17. Culture Power--what can be done with it, models & articles--PAGE 990-1055

18. Global Quality--24 approaches, 30 shared aims, quality soft-&-hard-ware--PAGE 1056-1129

19. Are You Effective?--100 methods from the world’s top performers--PAGE 1130-1193

20. A Science of Excellence--54 routes to the top of nearly any field--PAGE 1194-1242

21. SuperSelling--tools, methods, cases from 150 best at ALL forms of selling--PAGE 1243-1304

22. Managing Complexity--3 sources, 3 paradoxes of handling them--PAGE 1305-1370

23. Taking Place--creative city theory & practice via 288 city-fications--PAGE 1371-1431

24. Innovations in Innovation--& in 29 other creativity sciences--PAGE 1432-1491

FREE download of this entire book of books via LINK:https://drive.google.com/open?id=0B9XSvwJ-xErSSkoyMGU0azN0eFk

4. Getting Real about Creativity in Business--measures tools--PAGE 193-245

5. Your Door to Creativity, Revised--42 general 12 detailed models--PAGE 246-303

http://www.detaoma.com/Richard_Tabor_Greene

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THIS BOOK UNDERWAY--on page 1450 out of 2200 in July 2016, finish maybe October 2016WORTH THE WAIT--this is by far the most COMPREHENSIVE BOLD DETAILED book on ALLFORMS OF CREATIVITY that has ever been written, imagined, published.

66,000 A4 size pages!

No Fluff Anywhere!

No Lazy Academic

Filler K

ow-Tow Ideas

IDEAS FOR USE

Asia--EU--USA mix

No Lazy US Bias

Research--Practice

--Theory--Methods

Social automataREPLACEMENTS for

all common foundations

of thought

Dialog among 5 known

computational system

types:machine, society, biologics,

brains, mind extensions

OTHER BOOKS by Richard Tabor Greene

NEW SCIENCES

The paradox is truth, real tr

54 EXCELLENCE

SCIENCES,

30 CREATIVITY

SCIENCES,

120 CREATIVITY

MODELS,

60 INNOVATION

MODELS,

64 NATURAL

SELECTION ACTS

THE PLURAL MODEL, NEW SCIENCES

APPROACHit all starts with re-doing Plato--defining “the good” and “excellence” empirically notphilosophically, politically, religiously, ideologically. 8000 eminent people from 41nations and 63 professions were interviewed about who was best in their field and howthey got to be best. The result was an entire hierarchy of new sciences of excellence.

First we get 54 excellence sciences, routes to the top of nearly any fieldSecond we get 30 Creativity and Novelty Sciences, how the new arisesThird we get 120 model of creativityFourth we get 54 models of innovationFifth we get 64 natural selection dynamics.

One overall article, from a Springer Encyclopedia of Creativity, Design, Innovation, andEntrepreneurship, presents all the above in 22 brief pages. These constitute the con-text around any one design or act of designing--the brothers and sisters of any act ofdesigning. Apple Computer began the application of this context by simultaneousenactment of five of the 30 creativity sciences--where its competitor firms settled forone at a time.

Though this section is short, just 22 pages, it forms an enormous and vital contextaround all design work (and for that matter, all creative work in the other 21 CreativitySciences).

MULTIPLE MODELS OF CREATIVITY

Affiliation

Richard Tabor GREENEDe Tao Master's Academy, Beijing; Keio University, System Design & Management, Japan

SynonymsCreativity models, innovation models, model repertoires, fractal pages, structural cognition

The Idea of NOVELTY & CREATIVITY SCIENCESThis article introduces 1) the Novelty & Creativity Sciences (creativity, invention, innovation, design, composing, business venturing, and others), 2) the idea of multiple models of each, 3) with example multiple models (Meta-Models) of creativity and innovation, 4) and more detailed models than are usual (here a 64 item model of the most creative process known—Natural Selection). The model of creativity models that this article presents is the most comprehensive and detailed such model yet published, at the time of this writing--a prior article on 42 models and book on 60 exist (Greene, 2001, 2005). This article also presents four size scales (compared with Levels of Invention in this Encyclopedia) as contexts around and under Creativity & Novelty Sciences: Excellence Sciences (scale 1) some of which are Novelty & Creativity Sciences (scale 2), one of which is creativity (scale 3), 120 models of which are presented here, and another of which is innovation (scale 3), 54 models of which are presented here. Finally, 64 dynamics of one of those 54 innovation models (scale 4), of the single most creative process known, Natural Selection, is presented.

Rather than summarizing the other creativity models in this Encyclopedia (see Theories of Creativity, Innovation Systems and Entrepreneurship), this article presents an intellectual tradition, centered on developing large diverse repertories of models, and tools specially invented to support the development and use of such repertoires. One such tool, a proposed replacement for prose itself, is presented at this article's end (the model, from Michod, of 64 natural selection dynamics).

Academia educates creator-designer practitioners into a tradition of one right-y model, righter than all others. The multiple models tradition in this article contradicts those practices and their academic source. In the history of science (Eamon, 1996) European world-wide collecting produced collections categorized in museums, that later scholars “explained” via causal models. Modern journals refuse categorical models (and the size of article they require). This article attempts to open up: 1) multiple Novelty & Creativity Sciences and 2) multiple models of each of them, 3) highly detailed such models--as new contexts and frontiers for practice and theory. The costs of our mania for single right-y models and benefits of switching to repertoires of diverse models are included here. Figure 1 shows one pattern among the Novelty & Creativity Sciences (also see Creativity, Design, and Innovation).

Figure 1The NOVELTY & CREATIVITY SCIENCES

Study of all the ways the new gets into society in relation to each other: VERSIONS OF LEVELS OF LIBERAL/SOCIAL ARTS OFCreativity/Discovery Educated Persons (Created Selves) History of all 16 in 1st 2 cols. Design/Invention Creating Selves Literature ofInnovation Creating Careers Philosophy ofFounding Tech Ventures Creating Systems Politics ofFashion Creating Others (Leading) Culture ofEvolution Creating Cultures Design ofComposing Stories, Games, etc. Creating Quality Economics ofPerforming/Exploring Creating Knowledge Practice of

(from Greene 2011 and De Tao Master's Academy 2011)

Tools for Non-Narrow: Thinking, Professions, Academe, & OutcomesHerbert Simon wrote that exponential increase in knowledge volume meant professions, disciplines, theories, and professional people were, relative to the totality of that knowledge, becoming smaller and smaller fractions, with severe effects, namely, that all our major problems fell in the cracks between our increasingly narrow persons, professionals, and disciplines (Simon 1996). System science failed as a solution (Bartanlanffry 1969). Total quality worked better—horizontal processes replacing vertical ones, continuous improvement replacing giant innovation leaps, kansei engineering of delight quality frontiers of customer imaginings of future requirements, statistical measures replacing management by rank opinion (Greene 1993; Lillrank & Kano 1989; Ishikawa 1991; Cole 1995, 1999; also in this Encyclopedia see Creativity, Innovation, and Quality Assurance). Lately systems engineering has arisen as design of systems of systems (Maeno, Nishimura, Ohkami 2010; plus Creativity and Systems Thinking in this Encyclopedia). From these “binding”-other-fields or Meta-Fields, tools for handling plural diverse models have arisen (Greene 2010; Nakano, 2011).

Structural cognition (Zwicky 1969, Kintsch 1998, van Dijk 1997, Meyer 1982) is another Meta-Field that addresses this issue of ever narrower people and professions in a world where problems are wider and wider. Where ordinary science seeks one right-y model; structural cognition identifies diverse repertoires of models that explain a phenomena, with each model in each repertoire of models compensating for weaknesses of the other models. In doing this, Structural Cognition sees itself as midway between Asian causality (10,000 bee stings to move an elephant; and Western causality (find the one tipping point in a system where slight inputs have huge outcomes). Both aim at predictive capability---but one seeks a single model while the other seeks diverse well-balanced repertoires of models.

Structural cognition tools are especially suited to crowd-source and swarm intelligence arrangements on the web. The tools encourage application of blends of diverse models, enabling:

1. more comprehensive coverage of a phenomenon

2. more diverse aspects of a phenomenon distinguished from each other

3. more detail handled at the same time as more comprehensiveness is achieved

4. more accurate and localized diagnosis, assessment, and strategic direction done

5. more adaptive alternative responses and ways to go at crisis points

6. more common ground possibilities for reducing first apparent conflicts/differences.

A Creativity Theory Support of Many Diverse Detailed Creativity ModelsTorrance (Torrance 1974) in his famous work to measure creativity chose three mental capabilities:

1. FLUENCY---the total number of interpretable, meaningful, relevant ideas generated in response to a stimulus

2. ORIGINALITY---the statistical rarity of the responses generated

3. ELABORATION---the amount of detail in responses generated.

We might, then, ask these three traits of our models of creativity: 1) how many models do we have and use? 2) how rare are these models? how diverse from each other are they? 3) what is the level of detail of elaboration of each model? Creative modeling of creativity, it would seem, would involve us in having 1) many 2) diverse 3) highly-detailed models, not single right-y models of great abstraction lacking detail and specificity. This little exercise makes us clear that academia's aim for “rightness” of model gets in the way of “creativity” of model. Perhaps, mono-theism drifts into mono-theory-ism, culturally.

The Idea of a Novelty & Creativity Sciences SchoolColleges and corporations today split the Novelty & Creativity Sciences into different departments, centers, sites, and degree programs. No journals reach all creativity sciences. Therefore, each Novelty & Creativity Science is being learned, studied, presented, and applied on its own, for the most part. What might improve were they all studied, taught, applied together? That is the concept behind creating a Novelty & Creativity Sciences School (and Research Center).

The few initial experiments in this direction—primarily at De Tao Masters Academy in China and Keio in Japan---show these results:

• intense formats—9 Creativity & Novelty Sciences taught in one 18 day period via 2-consecutive-days per course (so interactions among them are intense)

• the graduate student insight—students get, after initial frustration of their habit of seeking one right-est model, the insight that no one model is powerful, unbiased, comprehensive, or accurate enough to be trusted

• discovery of mental hiding places—instead of all teams depending on “brainstorms” and all individuals depending on “insights” (see Brainstorming and Invention and The Role of Intuition in Creativity), dozens of particular new ways for new ideas to enter life and work are found (social design automata, stratified responding, and others)

• what one Novelty-Creativity Science teaches others—we ask designers to invent and they find “over-specification” a problem; we ask inventors to design, and they find “under-specification” a problem—each develops wider new approaches, stretched by experience someone else's ways.

Multiple Models from the Practitioner's Point of ViewIndustry CEOs find two great weaknesses in single right-y models: one, it is too risky to assume that “our” present consultant-academic's model is the right one and all others are wrong; two, the best most statistically-valid single right-y models, when fully, expensively, sincerely applied by competent private sector organizations produce laughably tiny improvements in creativity. A recent Harvard article reported copying a Japanese hit product 8 years later as the creative result from changing 40+ environment variables to create a “creative” and “innovative” environment (see Measurement of Creativity). Delayed copying is not a robust useful result. Single models however right-y are usually useless.

SIX META-MODELSBelow 4 levels of repertoires of models (meta-models) are presented, without discussion—54 Excellence Sciences (Figure 2) many of which are 18 Novelty & Creativity Sciences (Figure 3), two of which are 120 Models of Creativity (Figure 4) and 54 Models of Innovation (Figure 5), one of the innovation models being 64 Dynamics of Natural Selection (Figure 6). This demonstrates both vertical and horizontal dimensions of the structural cognition program of tools for thinking as broadly as our problems without losing specificity and application power. All the models come from 8000+ people from 41 nations and 63 professions interviewed over a 6 year period, the resulting capability models linked to nearest-match theories from 4000 research books on Novelty & Creativity Sciences. At first re-doing Plato by asking high performers in many fields who was top in their field and how they got to the top, produced 54 Excellence Sciences in the below table, many of which were Novelty & Creativity Sciences. The same data also defined capabilities of highly creative people, great innovators, great designers, and for the other sciences.

Meta-Model One: 54 Excellence Sciences, Some of which are Novelty & Creativity Sciences too

Figure 2

54 EXCELLENCE SCIENCES

Routes to the Top of 63 Professions in 41 Nations from 8000+ RespondentsCombining Tacit Knowing, Practical Intelligence, Knowledge Evolution Dynamics, Declarative & Procedural Knowledge,

Theory and Practice Knowledge. Items with an * have books presenting sets of capabilities that define them.

SOURCE De Tao Masters Academy Creativity & Novelty Sciences Studio Plan, 2012)

PersonPerfor-mance

Adaptation Diversity ReflectionCompila-

tion

BA

SIC

S

SE

LF

educatedness*

RE

AL

ITY

diversity*

(handling it)

MO

DE

L

structure* (social & cognitive)

EX

PER

IEN

CE

cases

ME

TA-K

NO

WIN

G

humanities & arts of knowing

KN

OW

LE

DG

E T

RA

ITS between

knowledge formats

effectiveness* complexity (handling it)

system* theories natural & social sciences of knowing

between knowledge categories

creativity* error (handling it) quality expertise

professions & engineering of knowing

across knowledge explicitness & consciousness

P

RO

DU

CTI

VIT

Y

M

AN

AG

E

management

functions*

PRO

DU

CE

leading & innovating*

ST

YL

E

artfulness (handling constraintlessness)

GL

OB

AL

ITY

global effectiveness (Western, Eastern, both)

ME

TA-D

ISC

IPL

INE

S humanities & arts of 1 discipline

KN

OW

LE

DG

E L

OC

AL

ES across cultures

management levels*

composing/design*

coping (handling constraints)

power types natural & social sciences of 1 discipline

across selves/personalities

management domains

performing* paradox (handling incongruency)

morality (establish solace systems)

professions & engineering of 1 discipline

across systems

TRA

NS

FOR

M

INFL

UE

NC

E

influence

DIS

CO

VE

R

data

(collecting & analysis)

LE

AR

NIN

G

entrepreneurship

sources*

CH

AN

GE

fashion

(idea/method)

ME

TA-P

RA

CT

ICE

S practice: liberation

GA

PS

across knowledge

context & size gaps

careers*(+job finding)

research (processes)

manage by events ecosystems & clusters(of ideas & practices)

practice: freedom & historic dreams

across knowledge sequence gaps

technology (social life etc.)

venture (founding)

organizational learning

practices (movements of change)

practice: novelty conserved novelty

across knowledge surprises & noticings


Meta-Model Two: 18 Novelty & Creativity SciencesThe below Figure 3 presents 18 Novelty & Creativity Sciences in rough order, showing top level relations among them. Creativity is the root of them all because it is what generates novelty into them all. The structure of the table below suggests:

1. that evolution goes on in all Novelty Sciences, that narration and other liberal-social arts of each Novelty Science derive from evolution dynamics in each

2. that multiple models of creativity apply to all other Novelty Sciences

3. that selves and knowledge together capture the levels Novelty Sciences apply to

4. that experiment & discovery, art & invention, expression & exploration capture what is applied to those levels (each of those pairs expressed via multiple models of creativity involved in them)

5. that 8 Novelty Sciences (creativity, evolution in, story-comedy-history-philosophy of, selves, knowledge, experiment & discovery, art & invention, expression & exploration) somehow support and apply to twelve other Novelty Sciences (educatedness, careers, creating others, systems, cultures, quality, innovation, ventures, design, composing, fashion, performing).

6. that it is all about Novelty in the end---each Novelty Science is about bringing the new into our world

7. that each Novelty Science differs from others in having a sort of basic direction:a. tries---in the experimenting and discovery involved in innovation and venture buildingb. builds---in the art and inventing involved when people design and composec. roles---in the expressing and exploring of self and other involved in fashion and

performanced. formats---in the flows of knowledge in systems, cultures, and quality achievemente. persons---making and made in educatedness, careering, and leadership.

Figure 3THE 18 NOVELTY & CREATIVITY SCIENCES

Creativity generates Novelty which generates Selves, Knowledge, Discovery, Invention, and Exploration


1) The Liberal Arts of Each Novelty Science Come from the Evolution, Natural Selection dynamics, of Each;2) Novelty Comes into the World via FIVE Types of

Creation and TWELVE Levels of Creation

EVOLUTION IN

(Natural Selection

dynamics of):

STORY-COMEDY-HISTORY-

PHILOSOPHY OF

CR

EATI

VIT

Y

NO

VEL

TY SELVES

Educatedness (self creating persons)

CareersCreating Others (Leadership, Parenting)

KNOW-LEDGE

SystemsCulturesQuality

EXPERI-MENT & DISCOVERY

InnovationVentures

ART & INVENTION

DesignCompose

EXPRESSION & EXPLORATION

FashionPerform


Meta-Model Three: 120 Models of Creativity The table below presents 120 models of creativity. These came from three sources: 150 eminent creators in 63 professions and 41 nations, 8000 eminent people similarly distributed, and 4000 books and research articles on creativity from academics.

There have been some meta-models of creativity model types: (Harnad 2006) method, memory, magic, mutation types of creativity theories; (Styhre & Sundgren 2005) 4P creativity model types: process, person, product, and place (see Four P's of Creativity). However, the model below of 120 creativity models, is the only published model, derived from empiric data from creators, yet with nearest match academic models indicated for each, this comprehensive and detailed. Remember each of the models named below, in its full form, printed elsewhere, has 20 to 60 well-ordered components.

Figure 4120 Models of Creativity

from 150 Creators, 8000 Eminent People, & 4000 Books and Research ArticlesPeople from 63 diverse professions and 41 nations were sources of the below.

[A book of detailed models for 60 of the models below is available from scribd.com]Academic models that correspond with each empirical model below are noted in each box.

This Model of Models is the basis of a 4 semester course at KEIO SDM in Creativity Models. SOURCE De Tao Masters Academy Creativity & Novelty Sciences Studio Plan, 2012)

The Comprehensive First 60 ModelsEach model below purports to explain ALL of creativity

NOTE a book on all the below was published (Greene 2000)

The Partial Second 60 ModelsEach model below purports to explain SOME KINDS of creativityNOTE the below are being tracked closely for eventual generality

THE SOCIALITIES OF CREATING

THE MENTALITIES OF

CREATING

BRAIN MANAGEMENT

MIND EXTENSIONS

OUTSIDE BRAINS CATALOG EXPERIMENT SEEK EXCEPTION ENTOOL MENTAL

OPERATORS

RECOMMENDATIONS create creative life (by making interior & exterior room, and embracing paradox enough to enable mental travel), accumulate subcreations till they become creation machine, your creative life uses your creation machne to create Gardner , Simonton, Sternberg, Root-Bernstein,

SOLUTION CULTURE creators articulate to themselves their chosen field as a failure culture; then they reverse all dimensions that characterize that failure culture to invent a solution culture, which they then apply to realize aims the field has failed thus far to attain. Jonathan Feinstein, Greene, Marx, Arendt,

EVERYMAN SCIENTISTS, HYPOTHETICALITY (BAYES) creators become aware of limitations and tendencies in how they view the world (what they notice and miss), they consciously seek frameworks for getting beyond those limits in self and others, they develop exquisitely precise and unusual hypotheses, then invent ways to test them; artists do science. Bayes, Judea Pearl, Bunge, Sloman, Eamon

DECENTRALIZE INTUITIONS each group and era has its own characteristic intuitions, which creators notice and reverse or change; when central control by leaders is assumed, creators imagine many actors self organizing without central exchanges and specially designated leaders; Resnick, Papert, Minsky, MacDermott

TRAITS creators vary via being ingenue, problem seeking, violating bounds, bricolage, then they combine ideas via metaphor, abstraction, indexing, and multiple worlds mappings, then they select combines via finding saturated fields, countering intuitions, articulating, applying aesthetic rules. Guilford, Sternberg,

POLICY BY EXPERIMENT creators take all that exists as hypotheses not established truths, they play around with, experiment with what now is, including present trends and thoughts; they structure work not to make systems work but to learn from whether they work or not, how the universe functions. Deanna Kuhn (us as intuitive scientists)

ENDISH MEANS creators embed ends they aim for in the means they use to seek those ends, so that trying for goals, incrementally actuallizes them; they liberate themselves from past ways, then in no man's land seek out other ones liberated from the same realities, with some of whom they dream dreams of a replacement for old ways. Mao Tse Tung, Joseph Campbell

CHANGE MIND MAP TOPOLOGY creators assimilate ideas into models and arrangements on paper or in their minds, but they take another step and transfer whole models of ideas from one geometry type to another, viewing the same set of ideas from diverse frameworks Herman Hesse, T.S. Eliot, Foucault, Parsons,

QUESTION FINDING creators find vital tipping point questions via finding gaps (reverse trends, depersonalize, undo successes, expand models), via finding points of high leverage (fully represent, socially index, seek intersections, change

CREATION EVENTS creators turn distributed sloppy unorganized latent processes of thinking into intense, events, either of two ways, by tweaking in parallel several emerging processes and getting them to inter-relate, or by getting the right parties

EXTREME CONCENTRATIONS creators do normal thoughts, feelings, and actions but continue them to ridiculous extremes which cause them to attract great attention and change frameworks and expectations of others,

BRIDGE COMPETING NETS creators just do social work in this model: they put networks of people and thought into contact that before were not at all in contact, and by how much contact, where, of what sort, creators create a particular

models), via changing representations of situations (change: measures, scales, causes/effects, models), via changing logic (relate, imply, unify, cross domains) Einstein and Infeld 1938, Runco

together in the right context and roles. Greene

being declared “creative”. Arnold Ludwig

flow of ideas. Ron Burt, Gladwell

DARWINIAN SYSTEMS creators vary, combine, select, and cause idea reproduction across persons, creative works, fields, and domains (so persons, works, fields, and domains all 4 can either foster or hinder creativity, by supporting or hindering the 4 functions: vary combine, select, reproduce) Finke (pre-inventive forms)

FRACTAL MODEL EXPANSION creators map all that is known and tried about some idea or topic in highly organized and regular diagrams, so nothing already tried is omitted or distorted; then they regularize these maps to make what is vital and what is peripheral visually self evident; finally they expand the dimensionality of the diagram on all size scale so they imagine new items associated with EACH past item. Abbott

IDEA FARMING creators plant in minds, their own and collaborators' possible new ideas, then nurture them in various ways, letting them compete as they grow, till some of them tower over the others and more interesting or powerful or unusual; that means creators multi-task among diverse ideas planted. Edison, Kurzweil

AVATAR WORLDS creators in imagination or using new media see themselves or their favored ideas in alternative worlds relating in ways not of this world to other ideas and persons; exploring new contexts around and relationships with ideas in these other imagined world contexts, reveals aspects of ideas, combinations of ideas,. Dashavatara, Chip Morningstar,

COMBINED THOUGHT TYPES creators create by combining 16 distinct types of thought: getting inputs via empathy, doubt, observation, and parttern recognization, abstracting by associating, arranging, playing with, and representing ideas; grounding ideas by abstracting from cases, concretize idea in cases, extracting from routine, and creating new routines from ideas; outputting creative works by generation of—images, analgies, syntheses, and causal models. Root-Bernstein

SOCIAL AUTOMATA s, This is creating by tuning interactions of populations of entities, varying their connectedness, diversity, depth of contact, neighborhood size, till creations emerge. Greene, Scott Page, Gilbert, Melanie Mitchell, Wolfram

USE/MAKE ACROSS SCALES creators attend to and act on smaller and larger size scales of space and time than others, Root-Bernstein, Margulis (and Sagan spouse, she small, he large scale)

META-CREATION = RECURSIVE INNOVATION creators study how other creators create and modify their own ways of creating competitively based on those observations; this includes making a science out of things other creators treat as arts. Flavell, Demetriou,

GARBAGE CAN creators establish and notice 4 cycles: new tools enable new thoughts inventing newer tools; successful works change self/other expectations making more boldness making more success and two other cycles. Amabile, Baer & Kaufman

CREATE BY BALANCING creators map all the dynamics of an area on polar dimensions, often many at a time, and notice what poles are now emphasized and aimed for and which are slighted or missed; Greene

SYNCHRONIC DIACHRONICS creators study the past, map its sequences of innovation, express those as movement along abstract dimensions, then create new things by adding other dimensions, going to extremes not done thus far on past dimensions, and interpolating between items of past dimensions; Altshuler did this to patents to invent the TRIZ system. Altshuller

INTERFACE EVOLUTION most interfaces and bodies of knowledge, most teaching and explanation are organized by past categories and fits new things into those layers of accumulated past context; creators create new interfaces re-contexting all we know Liskov, Norman, Talcott Parsons,

BLEND SYSTEM JAPANESE AESTHETICS

REPETITION POWER, SOFTWARE

CULTURE MIX many NON-LINEAR SYSTEM DELIBERATE ISOLATE FUNCTION

perhaps all creations are done, without anything creative happening as far as the creator is concerned---the creator simply operates in another culture doing what is perfectly easy and normal in his or her own culture but those in the other culture are amazed declaring the results “creative”- Holyoak & Thagard, Koestler, Richard Florida, Gordon

creators set up systems of ideas, persons, works, groups, domains that involve many things interacting, they creators set back and tweak parameters of those populations and their interactions, or they set them up to reflect on and tune themselves; the world outside of creators is such populations of things interacting non-linearly and the world inside each creator's head is also such a population of things interacting Par Bak, Gladwell, Cowan, Casti, Kaufmann

IMPREFECTION create to make world world not to make world mind or mind world; AWARE (ah-ness of things, empathy towards things, sensitivity to impermanence of things, MUJO (impermanence of things), WABI, SABI appreciation of emptiness) Sen, Rikyu, Okakura

FROM MEANS modules encapsulation; creating modules is a constant in all forms of creation---isolatiing the behavior or effect we want from how it is produced; this allows, encourages exaptation and briocolage. Mitchell & Plotkin (existential types, modules)

DISCIPLINE MIX creators often perhaps always are ones who come from one domain to handle issues in another domain---here too they can be creative without trying hard or doing anything that is not easy and natural to them, in their original domain; Koestler, (Heilman, Nadeau, & Beversdorf, 2000)

DARWINIAN the most creative single process in the universe is the one that created life, and us, humans, that is natural selection, common images of natural selection are nearly 100% wrong; correct models, collecting neutral traits libraries till some are found adaptive when environment shift substantially, Simonton, Melanie Mitchell, Dawkins

BEAUTY OF BELONGING & NOT BELONGING create by showing the in-ness of outs, and the out-ness of ins; SEIJAKU (lost in one's thought but more lost in reality, in a scene), IKI (a clear, stylish manner and blunt, unwavering directness), FUKINSEI (unbalanced, non-symmetry, DATSUZO (freedom from habit, tradition; KOUKOU (the majesty transmitted from past greats by withered remnants) Donald Keene, Saito Yuriko,

REUSE & SPECIALIZE taking something and its effects and behaviors and relationships and making larger or smaller, faster or slower versions of it—v Dahl & Nygaard (simula)

TUNING creating is a tuning operation of finding precise delicate mid-value along poles of opposite value---too much of X and too little of X and you get not creativity, but some mid-value causes creativity---this is the inverse U function of creativity. Sternberg

SYSTEM EFFECTS creators seek unplanned emergent delayed distributed system effects, inventing by building ways to deliberately provoke and benefit from them. Schelling

HIDE WHAT YOU SHOW indicate not express, hint not display; USHIN (discrimination, exquisite distinction), SHIBUI (beauty that appears only when gazes linger); YABO (the ugliness of the court of Louis !4th in France—the ugliness of directness, opposite of IKI), JO-HA-KYU (slow, break, sudden), EN Haga Koshiro, Tadao Ando,

EXAPT: REUSE IN NEW CONTEXTSevery new thing every creator invents can, recombine with all past things, some of which combinations will themselves be creative: especially when deployed in distant remote unlikely new contexts and environments Levi-Strauss, Root-Bernstein

PARADOX DOOR creators seek out anomalies, paradoxes, contradictions, things that are not working---they struggle to make sense of them and have to let go of their own selves and attitudes and educations into new views in order to handle them—paradoxes et al are doorways to

SURPRISE creators surprise others after surprising themselves and their own minds, via liberating from what all consider essential, combining with others thusly liberated, to invent replacements that need protecting from established old ideas-forces Arendt, Rollo

PRESENCE OF NOTHING create by minimal word-less suggestion MIYABI (things polished so anything vulgar is removed); YUGEN (dim, deep, mysterious; fully recall from slight hint of things), HONSHITSU (the felt not thought essence of things), David Oden,

CODE INVENTING CODE creators that create things that create, that combine, that fuse, that split, that invent, multiply their variation, combination, selection. They make themselves more creative. Creators deploy initiative and creating/designing from their own selves into

getting beyond what is in oneself. Michael Parsons, Kegan, Marianne Lewis

May, Wildavsky, Fiske Onishi Yoshinori, embeddings in what they create, so what they create creates. Shannon, von Neuman, Turing, Kaufmann

SCALE BLEND creators use or invent new tools to expose phenomena on new size scales, larger or smaller---they track results on other scales —trying to invent an all scale result or theory that explains or generates the two scale and single scale results. Root-Bernstein

ADJACENT BEYOND the universe itself invented natural selection the most creative process we know—so do/can humans create the same way the universe creates? each new creative work combines with all prior ones making exponentially more combines--a self-scaffolding process.Kaufmann, Boden

SUBWORLDING living your fantasies in order to invent bridges UKIYO (the fantasy only world, of pure unhindered human desires), ANIME (the sudden decisive make of a feeling in a reader by One Stroke), OTAKU (the culture of narrow development from self out not social constraints in); COSPLAY (putting on lives via putting on clothes) Nobuyuki Takahashi, Shimokawa Oten, Seitaro Kitayama

OPERATE ON PRIOR RESULTS Similarly, recursion power: creators who apply operations not to similar inputs but to the results of that or other operations, extreme-ize catapult beyond norms and expectations = create. Turing, Von Neuman

IDEA MARKETING creators market ideas and possible solutions to their own minds, and use creative works to market ideas to their own field of people, and market that to the history. Palombo, Noice & Noice,

POPULATION AUTOMATON creators set up populations of things interacting non-linearly whose interactions they tune, till better than planned results/patterns emerge, on four levels: thoughts, solutions/works, creators, domains interacting thusly. each level has its own generator of paradoxes—negation, hubris, feedbacks, focus from parallel engagements Duncan Watts, Svyantek & Brown, Gavriel Salomon

SUBSPACES create sacred secular spaces where the essential nothingness of things is unobstructed. SAKURA (the dependable annual transience of things), SADO (a special exceptional world within our world where rank and crude exaggeration have no play), MUGE (the ultimate beauty of full meaning no longer blocked by ego, and worldly shallownesses) Bodhidarma(Bloodstream sermon), Gautama Buddha (flower sermon), Huike, Huineng, Myoan Eisai

OPERATE ON STRUCTURES OF DATAcreators who apply operators whether physical, emotive, or cognitive not to single items but to ordered patterns of such items, multiply effects in variety, scale, and scope and may end up creating as a result. N. Wirth,

GROUP PURITY SENSE EXTREMA DEMOCRATIZE MEDIA

COMMUNITY OF IDEAS creators put together ideas into community dynamics, among those ideas---associations, conferences, arguments, taxonomies, processes, sequences, events, fusings, branchings and many more; they control these idea community dynamics via meta-cognition operators. Minsky, Hewitt, Montuori & Purser,

SUBCREATIONS　in this model creators invent tools, facilities, images, representations of competitors, that by gradual accumulation expand the scope of creator productivity, imagination, cleverness, and density of idea realization in various media, till an amazing creation emerges on its own. J. R. R. Tolkien, L. Konzack(games),

CONSTRAINT ACCEPTANCE Creators study and organize constraints, seeing how others overcame similar ones and finding new constraints to add to force unseen before answers. Jon Elster,

CUSTOMERS AS OWN SUPPLIERS—APPS creating is making customers the makers not mere consumers; it is making things that turn everyone alive into a designer, producer, performer, creator; creating is inventing pre-inventive sub-creations that use to become invention. Jobs

SYSTEM MODEL creators are not all that create in this model—creation comes from a creator person interacting

PRODUCTIVITY creativity is a side-effect of immense productivity in this model; one of the first battles any creator has

MAP PAST DIMENSIONS creators create by mapping all past creations and inventing unique frameworks for

CUSTOMERS AS OWN SUPPLIER—INFO wikicreators create shared spaces where ordinary people in crowds can

with creative works (past, present and imagined future), with creative people in a field, and with the knowledge of his or her chosen domain; more than that creators interact in and across fields, creative works interact, creative fields of people interact, and creative domains of knowledge interact; so people, works, groups of people, and idea domains can foster or hinder creativity Csikzentmihalyi, Feldman; Styhre & Sundgren

and wins, is the battle for free time often won by personal inventions to free up time for creating; statistics show that the historically most famous creators are often even usually the most productive too. Simonton, Wishbow 1988 (poets 5 years prep), Hayes 1970 (painters—6 years prep)

ordering and interpreting them; creating is simply an operation on all past creations, understood and represented very abstractly, revealing dimensions in their evolution never noticed before. Ritchey, Zwicky

design and invent what formerly was designed and invented by central elites; creators invent tools that allow entire populations to create, tools that make readily fusable and usable the diversities of mass populations; Jimmy Wales, Malone,

SOCIAL COMPUTATIONcreativity in this model is a computational process among a group of people; one way to see this is as a complex flow of parallel intersecting steams of small events/meetings what people meet people, ideas meet ideas, funders meet people, with some events spawning, shutting down, and coordinating other events, King and Anderson, Payne, Montuori & Purser,

PERFORMANCE creativity is ideas performing in this model, at first performing inside a mind for your own self, then for imagined competitors, imagined heroes of your own field in past and future, failed ideas perform in your mind for you to see patterns among them that hint at solutions; possible works perform in your mind; Sawyer, Stanislavski, Stella Adler, Lee Strasberg;

VACUUM POWER this is Lao Tsu's style of painting in ancient China---painting spaces not objects, breaking perfections-symmetries-balances deliberately; creation is pure negation in this model---it is non-participation in the habits, views, and enthusiasms of a field or tradition; Ando, Lao Tsu, Merleau-Ponty, Marx

CUSTOMERS AS OWN SUPPLIERS---THINGS creators invent things that serve as tools and devices that encourage and enable ordinary people to create; this involves changing all products and services in this world so that their final form and next version comes from how people use them MIT Fab Labs, Malone, Toffler

SOCIAL MOVEMENT creators find whistle points, where slight inputs change entire systems, then they design seeds that release the forces in such whistle points in a society or profession and manage via public and private dialog tactics what emerges from the movement that results; Thomas Kuhn (paradigms), Christensen,

INFLUENCE creating is a social process of amazing a group of people via usual influence mechanisms, context setting, and selling avenues; varied types of person combine to create/influence a field—pioneers, translators, scholars, tinkerers, network hubs, glib salespersons, Kuhn, Eamon,

ISOLATE SENSES by seeing along one sense only you lose habitual constraints from other senses and see aspects of reality not noticed before; it is Einstein seeing the speed of light as a final speed limit in the universe (as Maxwell in 1882 suggested) and working out what that one seeing changes (it changes all of physics it turns out). Ramachandran, Suzuki, Zeami.

CUSTOMERS AS PUBLISHERS creators invent media by which all ordinary people can perform for others, can compose for others, can invent for others, can entertain others; creators spread creating from central elites to entire populations; John Milton Aeropagitica 1644,

SPACE SHARE creativity emerges when a certain intensity of sharing of intellectual space happens, in analogy to nuclear fusion happening when particle interactions are “confined” enough for a certain density of interaction energy to be reached, causing central elements, stripped of peripheral ones, to fuse. Segel, Schrage, Perkins, Zwicky, Gruber &

INVESTING creating is investing in idea streams in this model; you create by managing a portfolio of interests, some of which you develop into ideas and actions; you invest interest, research, imagination, tool invention, collaboration, and more; Runco and Rubenson (psychoeconomic model), Sternberg and Lubart, Hayak, Penrose,

INNER/OUTER REVERSALS this going out and finding that changes what you notice inside and going in and finding that opens out new outsides to understanding—is THE method of all creating, in this model; creating is going in to make new outs and going out to make new inside models and ideas and ways Joseph Campbell, Jung

SOCIAL DOERS creators make devices works and tools that enable groups to see and do what formerly only individuals saw and did; creating is taking the heroics out of creating by accumulating mass population steps and insights into stunning inventions; MIT Fab Labs, Malone, Toffler

Wallace

PARTICIPATORY ART & DESIGN you create by liberating people, ideas, devices, or the world from central controls and mass media messages controlled by central elites; creation is making audiences into performers, and reversing message flows. Shina Minoo

INFO DESIGN creators create by operating on information—representing information, modifying info types (causal, qualitative, etc.) and info topologies (logic, hierarchies, networks, etc.) Shriver, Tufte,

OBSERVE SELF OTHERS creators merely observe more and differently in this model; they pay more attention and high quality attention to things not attended to before; creating is watching without getting sucked into as others are—it is highly detached comprehensive reflective watching. Carravagio, Brad Blanton,

PARALLEL MEDIA UNIVERSE creating is a set of tools that liberate all from boring jobs and work and into full time creating--6 billion TV producers, virtual work worlds, creators entool every last person alive till everyone is creating all the time for each other; Toffler (de-mass-ifications)

SOCIAL SELF BRAIN ADJUSTMENT WORLD MIXES

MASS SOLVING creating is always the gradual deployment of a particular kind of mental protocol---a solving process---across a large population from whose variety innovation possibilities arise as the common process spreads; you can imagine Newton, Leibnitz and others exchanging letters and bits and steps of that eventual solving process spreading among them Ohno, Kano

COURAGE creators have a drive for truth that thrusts them into intense encounters with reality aspects poorly explained by current systems and knowledge, and creators practice courage of reaching beyond normal beliefs to raw phenomena, for truth. Rollo May, Betty Cannon, Tillich

UN-INDEX creators see raw reality by shutting down the outer cortex of the brain which usually indexes what we see so we see tiny fractions of what our senses get and we see only meaningful entities not raws inputs: certain autistic people lack the index around brains and directly access raw sensory inputs—these become savants, Mark Batey; Ken Heilman; Alan Snyder

RAISING NOT PROGRAMMING creators do not specify correct procedures but instead create entities and tools that themselves learn the world and how to operate it and by doing it anew without human attributes, discover or invent, design or make creative outcomes humans would miss; you create by not creating but by making things that interact to themselves create in this model. Rodney Brooks, Minsky

PROCESS DEPLOYMENT creativity is the spread of other or any protocols (not just solving ones) in this model. The variety of what you spread the protocol over generates the variations from which the group selects influenced by steps in the protocol they all share. Kano, Akai

ANXIETY CHANNEL creators unleash anxieties of existence normally hidden from, fled from and use that energy to see their chosen fields deeply and newly, Rollo May, Kegan, Betty Cannon, Joseph Campbell, Tillich

SHUT MODULES monks who shut down in meditation the brain module for being-in one's own body, fly. Creation is what you get when you work on something with key parts of your self and mind shut down. That is why people report semi-conscious states of mind as fostering creation the most. Davidson & Dalai Llama;

MANAGE POPULATION EMERGENCES designing populations of intelligent interacting agents from whose interactions emerge, unplanned, designs is how you create in this model; creating and designing and inventing are operations to tune the interactions of intelligent populations---whether software, robots, or people---in this model; Casti, Cowan, Holland, Kaufmann

OPTIMIZE IDEAL FLOW creativity comes from designing work, processes, and systems so that energy flows only in intended directions, so that the ratio of that energy over side-effects is optimized, and so that it is optimized over a linear function of values not single point values so results can be tuned without loss of generality; Einstein designed all of physics optimized around

EXTEND SELF DEVELOPMENT creating is something every human does---creating their own self---but only a rare few reject the self they inherited and replace parts with better things of their own invention or choosing (educated people); creators are people who extend this process from creating a self themselves to making the self they create a creative self,

WATCH MIND Some meditation regimes involve sitting and merely watching your own mind at work; months or years of this result in despair of ever reaching insight or happiness while led around by your own mind; despair at your own mind leads to practice bypassing its signals and categories—this leads to immense creativity Davidson & Dalai Llama, Rex Jung; Ken Heilman; Alan

PROSTHETICS the Borg in Star Trek II are what this model is about---adding machinery into brains that allow new sensations never in biology before and the opposite adding bio stuff inside brains similarly---all creating is just extending mind operators with tools outside of brains in this model. E. E. Smith(lensman, 1933), Project Pitman at Los Alamos

the speed of light as an ultimate Genichi Taguchi, Collcutt

MacKinnon 1961 Snyder

META-COGNITION creativity comes from deft changing of approach, aim, method, what is noticed throughout creator process of creating, due to this dual—being in but not of their process of creating. Flavell, Fisher, Shimamura; J. Feinstein;

INTEREST ECSTASY creators create interest in things no one else is interested in---they invest their intense beam of woven interests in tipping point places where slight inputs can have huge outcome effects; Csikzentmihalyi, Ypma; Jonathan Feinstein,

INPUT EXTREMA Creativity comes from exploring to find the extremes of what each sense and cognitive facility can handle---the lowest possible light visible, the most complex possible pattern to imagine. Ramachandran

SMART DUST creating is distributing over the entire universe communicating thinking imagining entities or extensions of human such functions till human mentality and all that exist think, feel, see, imagine, make together. Pister

SOCIAL CONNECTIONISM creators get ideas in frame contexts (idea solids = every idea a defined fixed place), by getting them into intense interacting, those frame are stripped off, so ideas flow between frames (liquid phase), till ideas form a frame-less gas, till the ideas lose peripheral contexts/ associations to become an idea plasma, coalescing into entirely new frames = creation; J. S. Brown, Duguid, Lave, Wegner

CAREER INVENT creating is merely inventing your own career rather than moving along a career path invented by someone else; they do this for the career that individual ideas have, that individual traits inside creative works have, that creative works have, that creators have, that people have; Klar et al, Ghiselin,

DE-CONCEPTUALIZE creators do what we all do except they refuse the categories we all use---they categorize anew from zero in all matters of interest; Zeami, Rikkyu, Lao Tsu; Ken Heilman; Alan Snyder

BRAIN AMPLIFIERS machines inside or around brains that amplify what they can do or react to = all in culture and all in civilization; creating is amplifying this or that operator in brains, with social, intellectual, cultural, physical tools and systems; all of civilization is such a tool in this model Simon Hooper (cyberware)

DEMYSTIFICATION creators demystify—that is all; that means they recover powers given to aspects of the world while growing up; assumptions made, limits on alternative ways imagined, filters so we miss parts of reality, hidden selfish motives and interests behind public services and boons, Illych, Foucault

PERFORMANCE CREATIVITY creators set up nine performances: ideas in minds, traits in works, works in fields---those 3 repeated in 3 levels: in the creator's own mind, others enacting the creator's ideas as performance, and third, audiences experiencing these three; when all 9 performances happen at once—creativity occurs and the secular is made doorway to the divine; Alice Isen, Mattis, Poincare; Sawyer (improv)

JIGGLE (ADD NOISE) creators jiggle mental frameworks and reality dynamics, inducing noise and randomness to unseat concepts and phenomena from their habitual contexts and settings; this resembles super-renormalization group operators in physics. Ramachandran, Remy Lestienne,

INFORMATED PLACES & TIMES creators are a history-long process of taking the all from everywhere and making it appliable in each local time and place; Zuboff, Altman & Sivo(Loopt)

KNOWLEDGE EVOLUTION

MIND SCALE EXTREMA RADICAL SUBCREATIONS

DIALECTICS creation is a generational process in all fields---the younger people to overthrow the dominant old people, go back to what was conquered and subsequently belittled in the past, resurrecting those ideas in new contexts to dislodge established ideas and powers in their chosen

INSIGHT alternating detachment and engagement on more and more abstract representations of what does not work results in accumulating a failure index the inversion of which specifies what eventual solution has to be like till creation suddenly occurs as sheer insight.

EXTEND SUBSTRATES you create by standardizing new substrates so that hundreds can study and combine them and learn from each others' experiences (bio fab copying wafer fab); you create by inventing substrates that many can use instead of just a few,. Langton (alife)

STRUCTURAL COGNITION TOOLS (cognitive list limit expansion) creators learn to apply ordinary mental operators to many times more ideas at a time than others---they invent tools for enabling them to do this and do their thinking, designing, solving work at many times the per time

field. Marx, Hegel, Abbott,

The Despair Doorway is opened by losing hope that who you now are, how you now think, will ever solve this X---then a pattern in your failures shows solution. Wallace Guilford Hadamard; Pasteur

unit of productivity of others. Entwhistle, Pask, Kintsch, van Dijk, Meyer, Flowers & Flowers

COMPILATION CYCLE you create by compiling knowledge from one model to another from one level of aggregation to another from one level of explicitness to another, from one level of consciousness/unconsciousness to another---such changes of format and form reveal gaps and patterns hithertofore unnoticed = creations Nonaka and Takeuchi

COGNITIVE OPERATOR EXTREMES creators apply normal mental operators to ideas BUT at extreme levels and degrees; the scale, force, agility, unconventionality, bias, expression, packing of mental operators can all be carried to extremes; Weisberg

TACKLE IMPOSSIBLES you create by tackling impossibles---ending death---knowing that steps in such bold directions will, by being outside of all extant realistic contexts surprise, amaze and perhaps, be powerful creations in some other context than the context that generated them Kawasaki, Jobs, Kurzweil

RADICAL INCREASES IN SOCIAL INDEXING creators notice that all current ways and societies operate with people knowing and using tiny fractions of the needs, interests, and capabilities of people around them---and they pioneer tools that expand how many of the needs, interests, and capabilities of those around them, people can notice, remember, and use; Thomas Vander Wal (web tags),

RELOCATING IDEA ECOSYSTEMS transplanting whole sets of inter-related ideas from one idea ecosystem to entirely different ones and making analogous and new links with the ideas in that context results in creation. Fauconnier & Turner (conceptual blending), Thomas Ward

MAKING SENSE by observing critically and skeptically human systems of belief, expecting many of them and much of them to be built on sand, creators by spotting the ones of weak foundation find towers of illusion and delusion to puncture with single well placed actions and works---creating is puncturing perpetual human towers of self supporting delusion Drazin 1999

EXTEND DOER SCALE new technologies and substrates change the cost and expense in money, attention, effort, resources, and time of doing certain functions---creators are those who organize people to operate a new speeds and size scales based on these capabilities Jeff Howe (crowdsourcing), Beni and Wang (swarm)

CULTURE PENETRATION AS INSIGHT PROCESS creators penetrate the culture of eras, fields, theories, problems—that is the same alternating of detachment with engagement on successively more abstract representations of that culture that all insight processes are and that is the same gradual accumulation of failures that when abstractly indexed specify what a solution must be like, more and more till a solution is suddenly found Bohannan(anthro)

IDEA WAVES sets of ideas pass in 8 year periods across all fields, finding great creative applications in some and no creative applications in others; creators are people who first notice such waves and who deploy their idea contents to appropriate parts/problems of their domain; Huczinsky,

PERCEPT INVENT creators invent new ways to perceive and new things to perceive---they create and invent by seeing what no one else sees---they do this by varying frameworks, levels of detail, associations, and other things that allow them to see realities no one else sees; cannot. Tomasello, Wittgenstein, Merleau-Ponty

INVENT NEW HUMANS creators see and solve the future now—so they use stem cells and DNA knowledge to design new forms of human body for when we all have to live on the moons of jupiter after our sun expands to red giant size; creators operate under bold constraints others will not face for generations; Kurzweil

SOCIAL STRUCTURAL AUTOMATA creators set up automata like protocol processes, among existing groups and structures and events in their field or society---till creations appear. Epstein & Axtell, Prietula & Carla

FRACTAL RECURRENCE victor ideas incorporate loser

EXPERIENCE REALIZATION creators make a hero story for

EXTEND TARGET SCALE creators take all we have and imagine and

REVOLUTIONIZE OLDER MEDIA creators examine the oldest most

ideas as components till later generations of youth revive them to overthrow old dominant ideas; creators learn these idea dynamics in their field and with great timing lead one or more of the forces that produce such repeated idea patterns. Abbott,

themselves and their culture: they launch fields on the hero journey of going beyond past ways into no man's land, fighting monsters till they realize the monsters come from inside them, then they come back to their field bearing magic powers from self transformation; Einstein, Picasso,

imagine them a million times bigger, a million times smaller, a million times faster, a million times slower---they wonder what we “get” if things are done at other scales and speeds; the US military is paying for bird sized autonomous flying devices then insect sized ones, then bacteria sized ones----intelligent dusts, for example; Hayes et al 1987 writers

assumed and used media and from entirely current and future perspectives redo reinvent them---so all the basics of thinking, feeling, and acting are revolutionized---creators replace prose, replace meetings, replace discussions, replace feelings Eamon, McLuhan, Negroponte, Maeda

SIMPLE PROGRAMS creators invent the smallest simplest fewest interacting components to generate all the complexity of some phenomenon in their field---they create by controlling, predicting, modeling the most with the least. Wolfram, Klahr(machine inventing)

SUBSTRATE UPDATE the world is an ever faster series of new substrates for doing basic functions better faster cheaper easier and for doing functions never possible in history before---creators are those who are first to redo a key function of their field on new substrates and the first to discover entirely new field functions made possible by such recently installed new substrates.Isse Miyaki, Stephane Leducs, Arber Nathans & Smith (nobel)

THE CALCULUS MOVE finite elements, renormalization groups, cellular systems, grid computing, cloud computing---all these are breaking something into millions of simple pieces and accumulating calculations simple for each piece into complex bigger scale shapes impossible to calculate directly—all creating is calculus of a sort; Leibnitz, Newton, Hrennikoff & Courant; (finite element); Ernst Stueckelberg and Andre Petermann in 1953

STRATIFY RESPONDING creators slow down and separate each within-the-mind layer of reacting---what do we notice, what feeling does each noticing evoke, what association does each feeling evoke, what framework comes with each noticing and how does it determine how the noticing makes us feel, what new framings are conceivable, possible, optimal, invent-able and what new noticings, feelings, framings, meanings do they generate---creators re-feel the world--Freud, Betty Cannon, Husserl, Ellis & Beck, Ken Heilman; Alan Snyder


http://en.wikipedia.org/wiki/Ernst_Stueckelberg

http://en.wikipedia.org/wiki/Ernst_Stueckelberg

Meta-Model Four: 54 Models of Innovation Another Novelty Science is innovation. Plural models of it can be obtained from both academics and from innovation practitioners, as described above.

Figure 554 Models of Innovation

How people make creations impact society and real human needs & situationsAcademic models that correspond to the empirical ones below are indicated in each box.

NOTE: a version of the below just as detailed as the one for 120 Creativity Models exists but it is not presented in this article for reasons of space. This model of models is the basis for a four semester course at KEIO SDM on models of innovation.


FUTURE PRESENT FIGHT

IDEA SOCIALITIES IDEA ECOSYSTEMS

CATASTROPHE SPOTTING

PATTERN SPOTTING

TREND RIDING

SOCIAL PHYSICS

LIVING INNOVATIONS

IDEA FARMS

abstraction inversions inhabit the future code changing action factors

educatization Silicon Valley clusters

DISRUPTION missing competitor caused disruption-Christensen

SUCCESS FAILScore bribes firm to ignore periphery = future--Bower

CAREER STEPmoving one's career forward not by competing for established fixed positions and roles but by inventing the entirely new --Greene

RIDE KNOWLEDGE DYNAMICS using knowledge dynamics, dialectics, pole-swings, loser enbeds---Abbott

GO OUT = GO IN globals invade locals, locals invade globals = all locals global—Jun & Wright

AUTOMATE INVENTION---nurture circuits among in-process projects, power of absurd concentration; many tries, many fails = many invents--Edison

SEARCHsearch in possibility spaces--Perkins

VALLEY INSIDE Silicon Valley introject (inside you)--Nevens & Lee

SELL PASSIONventure as enthusiasm package & presentation—selling motivation---Kawasaki

MOVEMENT successive successor-contexting social movements—Welch at GE

SELF BUILD ANGST educativeness burden of daily life+self-build= flee to what fled from (authority)--Giddens

VALLEY--flows of ideas, people, funds, techs seeking home to love them + tiny, fast, smart, adaptive coalitions of specialties--JSBrown

CUSTOMER INVENTORSdemocritize innovation= empower lead customers--Hipple

SUCCESS RIDING niches finding/ riding niches---Greene

TRUST BANKSethnic-immigrant trust-bank ventures--Granovetter

SOCIAL REVOLUTIONsocial revolution via liberty, free invents, public happiness, historic dream, conserve novelty--Arendt

DEMASSIFYde-mass-ify=global standard items make locals globally diverse make institutns. not fit---Toffler

VENTURE---anti-big, anti-East, from zero = new ways with new techs; anti-passive, anti-money---Greene, Jobs,

sociality social cognition

co-evolution co-adaptation social grammar Natural Selection

PRACTICE GEOGRAPHYsocial life of info—Brown & Duguid

UNORGANIZEinnovating = de-organizing---Sutton

FIT TYPE MENUinfo ecosystem device species proliferation—Davenport & Huber

INVESTMENT CULTUREculture of development: reliable near future--Grondana

RE-SOCIAL-IZE social relation type mixes: share, rank, reciprocate, price--Fiske

RECURSIONS--- natural selection systems within natural selection organisms--exaptations--Michod

NETWORK INVENTORnet not individual hero--Hargadon

SPREAD ONE HOME RUN INSIGHTmental practices of inventors---Schwartz

TAP GLOBAL DIVERSITYopen business, invention outsourcing--Chesbough

CULTURE FARMSculture farming—raising ecosystems of evolving cultures---Darwin, Boyd & Richerson

LEVERAGE DIVERSITIES use diversities: un-see un-do, balance in space in time diffce type/task type---Greene, Page

LEVELS---evolve repertoires of neutral traits, till envt. change makes some vital; evolve levels not fitnesses--Kimura

FOLLOW PROBLEMS ACROSS radical innovation=mix frame, anomaly, tool, crisis research; patron research

RE-SEE PAST FROM FUTUREinvent by managing mental models—Wind & Crook

PRODUCER CUSTOMERSmedia microcosmization = all consume all produce in all media --Jenkins

MISCOPYINGorthogonal copying in new context = invention---Westney, Roehner, Syme

ATTRACT CREATIVE ELITEtech, talent, tolerance = attract creative elites—city-fy as insight process--Florida

SOFT-HARD LEVELS----egg computer, DNA program, junk DNA control statements, wrapping modules; behavior to software, firmware, hardware---compile un-changeds to hardware—Gould, Dawkins

mixed with cllent research = combine artists, scientists, designers, engineers---Steffik

non-linear dynamics

emergence wave catching universal inventivity

non-linearity Interface Media Re-invent

NEW WAYS AIMS TECH AT ONCEtech life histories, firms fail by adding new means to old ways/aims---Wheelwright

POSITION IN SOCIETAL ENTHUSIASMS society lust--Lienhard

NOT ENOUGH IDEA HOMESmaximize creations without maximizing failures = R&D, finding fits, trashing non-fits---Matheson

TWEAK CON-NECTEDNESS TILL TAKE-OFFself organizing criticality—manage social automata till critical points---Bak, Gladwell

OTHER SCALE SIDE-EFFECTSunplanned order emerges = goods from bads (Mandeville) bads from goods (Schelling)--Krugman

REPLACE PROSEractal page formats replace prose---see count, names, order at a glance, read and write structures---Greene

NETWORK ECONOMIESincreasing returns to scale (net economies)--Arthur

SPAN HOLES spanning network holes---Burt; policy as experiment—structure for tries and data on tries---Thomke

INCREASE & VENTURIZE FRUSTRATIONSmanage knowledge by capturing frustrateds in org learnings + ventures ---Greene & Blackwell

ADJACENT BEYONDeach invention increases expo-nentially combines with past ones--Kaufmann

POPULATION OF FLUXING COALITIONSlowered coordinatn costs=tiny venture coalitns on web, big firm dissolve into market outsources, giant firm coalitions--Malone

REPLACE MEETINGSsocial automata replace meetings & discussions = missing micro-level = using humans in arrays, assigned cognitive/social functions as if CPUs in cloud computer network---Greene, Ignatius

SPOT PRE-RATIONAL HINTStechnology tipping points via gap detection--Burgelman

RECRUDESING IDEAShistoric necessity+ function lust+emergent process---Van de Ven. Woodman

DESIGN AS PROUNING from intruding Platonic forms design to pruning away detritus design, androgy style--Postrel

SIMPLE PROGRAMSsimple programs = iterative idea tries in neighboring environments till homes found---Wolfram, J.S.Brown

INNER/OUTER TIPPING POINTSnon-linear society net/web dynamics+ non-linear within mind message-stick dynamics = tipping points---Gladwell

REPLACES PROCESSESevents replace process replace bureau = evolution from fixed to flex---stratified responding, to response to response, to structural cognition--Greene


Meta-Model Five: A Comprehensive Version of One of the 120 Creativity Models—Natural Selection

The case for including in this article a model of Darwinian natural selection is easy to make---it is the most creative single process known in the universe---the process that invented life itself, and then went on to invent all the lifeforms that we know of, including ourselves.

Meta-Model Six: A Replacement for Prose Itself—Fractal Page FormatsThe natural selection model just described above and presented below, is two things: one, a summary of one fourth of Michod's overall model of natural selection dynamics; and two, a demonstration of a viable replacement for prose itself. One Structural Cognition tool, for handling plural models, is a replacement for prose called Fractal Page Formats. Why does prose need replacing? First, it is a very old interface, virtually unchanged for 8000 years. Second, it hides the structuring of its points so elaborate encoding and decoding are needed. Third, it lacks apparent visual scaling to handle the drill down drill up expand-breadth focus-narrowly operators we all do on the web. In research terms it hides the count, naming, and ordering of its point contents. The natural selection model below makes visually evident in an instant the count (how many points), the names of points, and their principle of ordering. It bridges web and older media nicely.

Replacing prose with Fractal Page Formats, meetings with Scientific Rules of Order, discussions with Stratified Respondings, brainstorms with Social Design Automata, classes and work processes with Mass Workshop Events—are five basic interfaces of daily life that can be changed to use and handle plural models not single right-y models.

DESIGN PURPOSE WORLDfrom Natural Selection

Figure 6

NATURAL SELECTION AS PHYSICAL LAW

NATURAL SELECTION AS UNIVERSAL ORGANIZATION PROMOTER

(for lifeforms and non-lifeform) Natural Selection 1) Generates Levels not Fitness, 2) Prunes non-fitting traits away, not selects optima 3) Has kinds of fitness

depending on type density effects Figure 6 (from Greene 2011)

DARWIN'S BIG ERROR] Only ONE of 3 Environments:Physical Survival Envt not Gene & Repro system Envt

TYPES OF NATURAL SELECTION BY DENSITY OF TYPES IN A POPULATION

How Nsn Generates

Worlds

How Nsn Generates

Optimal Design

How Nsn Generates Purpose

Generally the Fittest Do NOT

Survive

NSn as Natural Law (life & non-

life both)

Nsn is Central to Biology BUTFitness is NOT

Blocks to Heritability of Fitness Foster New Levels of Selection

Order EmergesNot

NecessarilyFitness

Density of Type Can Depend

onOther Conditns.

Econ of Fitness:Density of

Type in Popln.

Types of Nsn:1) by levels 2) by survival

type: 1st, any, fittest

4 Ways Density of Type affects

births & deaths

Fitness Types in Full Theory

Difference in Indl Fitness not Necessary not Sufficient for Evoln

When selectn is on several levels

simultaneously

FLAW 3: LUMPSTOGETHER

components best treated

separately

Flaws in Individl Fitness Idea: cover

survive/fecnd omits transmit/inherit

DETERMINERS OF RATE OF

INCREASE OF A TYPE

1) system factors like Nsn and 2) random factors like genetic drift

MORE FITNESS TYPES IF

GENERATIONS OVERLAP

1) intrinsic rate of increase, 2)

net repro rate 3) reproductive

value 4) individual fitness

PER CAPITA RATE OF INCREASE OF A TYPE (F-fitness)

is good fitness measure to

apply to natural selection NOT to random factor

cases

FOUR FITNESS TYPES:

1) Wrightian 2) genotype 3)

selective value 4) inclusive

DARWIN'S INDIVIDL FITNESS

is not individual but fitness of class of

individuals having same trait

FLAW 1: OMITS 3:1) sexual selectn

2) freqncy dependent effects 3) levels of selectn effects

=may decrease individual fitness

FLAW 2 STAT ARTIFACT =

in group or kin selectn = no

individual fitness is measured,

only group traits count

FITNESS ON ONE LEVEL IS POOR

GUIDE TO EFFECTS

even when the 1 level is

individual

NOT SUFFICIENT;constant selectn with

heterozygote superiority in fitness = fittest hetero- never

has highest per capita rate of

increase = fittest do NOT survive cuz

cannot breed true

NOT NECESSARY:partenogenesis=

survival & # of gametes same for all types but powerful

Nsn cuz of properties of mating system

FITNESS OF WHOLE SYSTEMon all its levels

determines evolution not

individual (level/organism)

alone

MATING SYSTEM PROPERTIES not just

traits of indvdl genotypes alone BUT INCLUDE density & freqcy dependent opportunities for

successful mating= heritability of fitness

CELLGROUP EXAMPLE:

indvdl fitness sensitive to

change in coopn benefits BUT fitness from coopn not

sensitive to coopn effect on individl fitness

BAD AT EXPLAINING EVOLUTION1) average fitness 2) average

individual fitness

INDIVIDUAL GENOTYPE

FITNESS DIFFERENCESnot necessarynot sufficient

for interesting evolution in NSn

FITNESS DEPENDS ON

population frequency and density often

SUCH RESOURCE TERMS

THEMSELVES DEPEND

on several other conditions = aneconomics of

survival

IMPLICIT INDARWIN'S

STRUGGLE TO SURVIVE is

density dependence of fitness

RESOURCES USUALLY

DECREASING FUNCTION OF DENSITY of a

type in a populn.

DENSITY DEPENDENCE

ITSELF DEPENDSon both 2 or more types

sharing need of same resource

CHANCE OF ENCOUNTERING

MATE =density of type in a population

= exampleof other density

dependent determiner of

fitness

BECOMING DENSE =

COMMONincreases cost of commonness =

parasites or disease wipe

outs

DEATH RATE DECREASES as

density increases IF protection

requires close social

interactions

DEATH RATE INCREASES as

density increases IF costs of commonness:

disease, parasites, predators =

SURVIVAL OF ANYBODY

BIRTH RATE INCREASES when density increases

=rare male mating advantage, sex ratio biased for females

causes increase repro when females rare =SURVIVAL OF

FITTEST?

BIRTH RATE DECREASES

when density decreases =

sex/mating costs of rarity, =

SURVIVAL OF FIRST

WHAT HAPPENS WHEN DofType

INCREASES1) increases rate of

birth decreases rate of death 2) increases

both rate of birth and of death


INCREASES3) decreases rate of birth increases rate

of death


INCREASES4) decreases both

rate of birth and rate of death

SURVIVAL OF FITTEST WHEN?

births & deaths independent of

Density of Type in a Population=Malthusian

exponential growth

OTHER DENSITY DEPENDENT

DETERMINANTS OF FITNESS

exist

NON-LIVING CASE

selfcatalyzgmolecules:

survival struggle=competitn forresources for

reprodn = fitness from sequence

makes folds makes survive/repro chances

MULTI-CELL ORG.EMERGENCE CASE

selection comes from between cell

between organism interactions = fit =survival makg

interactions

ORGANISMSBECOME THEIROWN WORLDsurvivors by

defn do not not fit envt or

modify it much= become their

own world

ORGANISMSBECOME OWN

ENVT.space/time

expand=becomeown

envt.=reduce selectn pressures

BY scale increase

or side-effects

TRAIT PURPOSE=ENTITY SURVIVES

IN THIS ENVT.Why survived?Good traits BUT

only when birthslinear fn of

density = manyother causes a

types apear rateincreases

EACH LEVEL DIFFER-ENT REPICATN MEANS = many forces affect replicatn. = benefit of any heritable traits

tested by many forces

WHAT DETERMINES BIRTH FN OF

DENSITY? linear, sq., sqrt? NSn

determines? can it?

VARIATIONS TO FITNESS NOT =

INCREASES IN ITNsn does not

aim for optimal designs

SYSTEM AT ABSOLUTE PHYSICS

OPTIMA FOUND IN ANIMALS

so great design somehow happens

DARWINS DYNAMICS

apply to non-living systems giving rise to many forms

DAWKINS ERRORmaximizing fitness idea creates selfish

gene replicating itself error

ENVT SYSTEMcan reduce

heritability of fitness: 1) random effects 2)

response to co-evolvg other species 3) change of freqcy

caused fitns changes 4) fitness that is

freqcy dependt (cost of commonns =

parasites

REPRO SYSTEMcan reduce

heritability of fitness: 1) cost of mate finding that reduces survival

(peacocks) 2) isolatn/immobility

causing inbreeding

FITNESS BLOCKS PROMOTE

EMERGENCE OFNEW LEVELSmolecules to

organulles, to cells, to multi-cell

organisms to societies

WHY A GENE TYPE SUCCEDS?

1) at survival 2) at re-plicating 3) at in-crease in freqcy in popln.= 2 fitnesses: 1) survive 2) entities become bags of neutral or fit traits

STEADY ENVT =DESIGN OPTIMA

lifecycle repetitions in

same environment

means designs appear to

optimize (for that

environment only)

NOT SELECT BUT PRUNE

design seems optimized but good designs not selected,

rathernon-surviving

traits were pruned away =indirect optima

SUPRESS LOW LEVEL FITNES

EVOLVESfor high levels to adapt to present envt. suppressn of lower level fitness must

evolve = apparent purpose

MORE LEVELS NOT FITNESS

EVOLVEfitness of lower levels reduced = borrow fitness

from higher: but virus except

WHAT IS NOT CHEMISTRY/PHY

SICS?differential

replication is ALL that is beyond

chem/phycs = is all that is

biology = all that biology is

BIOLOGY = TRIES IN ENVTS.

environment traits become

being traits, cuzeach organism's

experience becomes

compiled from soft/firm/hard

ware

NO INTENT?creatures are bags of traits that didn't kill parents = creatures look like they aim for pesent envt.

WHEN Natural Selection APPEARS

any system with replicating

entities and heritable

variations in fitness

HOW MEASURE FITNESS

expected reprodn success or capacity to

survive till reprodn (trait “fitness” to

challenge in this envt.)

HOW ORDER ARISES IN UNIVERSE

1) variation2) heritable capacities

3) struggle to survive to

reproduce =LIFE not needed

Natural Selectnas NATURAL

LAWheritable variatn in

fitness of set of reproducg things

(no men-tion of life in this

FIT DO NOT ALWAYS SURVIVE

example: small poplns where genetic drift

effects overwhelm

selectn so less fit individual

survive

SURVIVAL OF SURVIVORS

as poplns get small, more variatns in

fitness are not heritable =

success depends on chance

3 WAYS TO REDUCE

HERITABILITY OF FITNESS

1) envt 2) gene system 3)

reprodn system

NATURAL SELECTION IS:

the central law,organizing

principle of allbiology: the

ONLY difference between

chem/physics and biology

LIFEFORMS ARE NESTED

each level must replicate: organism, organulle,

chromosome, gene, DNA

strand

GENE SYSTEMcan reduce

heritability of fitness: 1) linkage, 2) epistasis 3)

pleiotropy 4) heterozygote

1 2

3

4

5 6

7

8

9 101112

13 141516

17 18

1920

21 22

2324

25 26

2728

29 303132

33

2

34

36

37 38

3940

41

42

4344

45 46

48

35

49 50

515253 54

5556

57 58

5960

61 62

6364

65 66

6768

69 70

7172

7374

7576

7778

7980

81 82

8384

85

Conclusion and Future Directions:

The web-ization of all industry, expands who---does things, and what they do. Perhaps our mania for single right-y models is a relict of our tools, pre-computer eras and habits of mind. Now that everyone has several computers at hand, handling plural diverse evolving models can be managed practically, making our mania for top, elite, static, highest ones go away.

Some future research directions coming easily from this article's plural diverse model perspective might be in order before closing.

1. Process Singularities Singularities in design process may be where two or more models of creativity conflict.

2. Apply Omitted Models Groups favor some models omitting others, so using omitteds may improve creativity greatly.

3. Evolve Among Models Particular designers, creators, innovators may learn and prefer some creativity models early in their career and evolve into others at later phases---why and which ones?

4. Find Best Models People and groups who create using some models may end up more creative than creators who use other creativity models.

5. More Models More Creativity? Organizations that support and use more models of creativity may get more creative outcomes than organizations that support and use fewer models.

6. Model Conflicts Conflicts in long term projects and creative collaborations may come from different parties and professions in them habituated to different models of creation.

7. Meta-Creativity Powers People who study which creativity models they use may, after that study, become more creative.

8. Model Diversity Powers People who are exposed to more diverse creativity models that they do not now use may, after that exposure, become more creative.

9. Model Trade-Offs Models of creativity may be in negative trade-off relations to each other so that supporting or doing one more may hinder doing others---environment supports for doing one may shut down many others, hence reduce total creativity levels in a person or organization. The mania for single right-y models of creativity, thereby, may harm creativity in practice more than they help it.

These are some research directions with strong implications for creative practice that come from the plural diverse models of creativity perspective of this article. Notice that quite a few models of Silicon Valley dynamics (see here Social Psychology of Creativity) mention the number, diversity, depth, detail of models of innovation, design, creating that come together, interact, blend and evolve new forms there (so this article may constitute how to generate replications of Silicon Valley to some extent [51]). Note that a new kind of university, funded by leaders in China, based on the plural models approach in this article is being built as this is being written (compare with Higher Education and Innovation).

Dr Deming the quality guru said to make one problem go away permanently, one had to throw 25 solutions at each of 25 root causes for each of 25 root problems equals 125 changes

installed to fix anything. Maybe creating, design, innovating, etc. are similar—single right-y models may be fun and easy to think about and test but having little impact compared to balanced repertoires of diverse models deployed wisely in particular cases.

References

1. Greene, Richard Tabor (2003). Are You Creative? 60 Models. www.youpublish.com. 2. Greene, Richard Tabor (2011). The NOVELTY SCIENCES SCHOOL. De Tao Master's

Academy.3. De Tao Master's Academy (2011) http://www.detaoma.com/newsds.asp?nid=1282 4. Simon, Herbert (1996). Sciences of the Artificial. 3rd edition. MIT Press.5. Bertalanffry, Ludwig (1968) General System Theory: Foundations, Development,

Applicationns. New York: George Braziller.6. Greene, Richard Tabor (1993). Global Quality. New York: ASQ & Irwin Professional7. Lillrank and Kano (1989). Continuous Improvement: Quality Control Circles in

Japanese Industry. Ann Arbor: Univ. of Michigan8. Ishikawa (1991). What is Total Quality Control—the Japanese Way. New York:

Prentice Hall9. Cole, Robert (1995) Death and Life of the American Quality Movement. Oxford10. Maeno, Nishimura, Ohkami (2010 Sept.). Graduate Education for Multi-Disciplinary

System Design and Management. Vol. 3 No. 2 Synthesiology11. Greene, Richard Tabor (2011). Your Door to Innovation. Keio Classnotes and

forthcoming, De Tao Masters Academy Press, 2013. . 12. Nakano, Masaru (2011). A Conceptual Framework for Sustainable Manufacturing by

Focusing on Risks in Supply chains. APMS 2009: 160-167.13. Zwicky, Fritz (1967). Discovery, Invention, Research through the morphological

approach. New York: MacMillan.14. Kintsch, Walter (1998) Comprehension: A Paradigm for Cognition; Cambridge

University. 15. van Dijk, Teun A. (1997). Discourse as Structure and Process; London: Sage.16. Meyer, Bonnie (1984) The Structure of Text in Handbook of Reading Research.

googlebooks.com. 17. Clover, Charles (2011, 25 June) Elephants are scared of bees, scientists say; The

Telegraph http://www.telegraph.co.uk/earth/wildlife/3309619/Elephants-are-scared-of-bees-scientists-say.html

18. Greene, Richard Tabor (1999); Are You Effective? 100 Methods; www.youpublish.com

19. Greene, Richard Tabor (2009); Brain Powers; www.youpublish.com20. Entwhistle, Noel (1982) Understanding Student Learning; London: Croom Helm21. Pask, Gordon (1966) A brief account of work on adaptive teaching and measuring

systems; Kybernetika Vol. 222. Torrance, E. P. (1974) Torrence Tests of Creative Thinking, Personnel Press. 23. Sternberg, Robert (1998) Handbook of Creativity. Cambridge University. 24. Pritzker and Runco, editors (1999); Encyclopedia of Creativity 2 Volumes; Academic

Press25. Greene, Richard Tabor (2009) Managing Self; www.youpublish.com26. Taura and Nagai, editors (2010) Design Creativity; Springer Verlag.27. Eamon, William (1996) Science and the Secrets of Nature; Princeton28. Giarrantano and Riley (2004); Expert Systems: Principles and Programming (4th

Edition); Course Technology29. Ericcson and Simon (1993); Protocol Analysis; Bradford MIT30. Laguna and Marklund (2004); Business Process Modeling, Simulation, and Design;

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http://www.telegraph.co.uk/earth/wildlife/3309619/Elephants-are-scared-of-bees-scientists-say.html

http://www.telegraph.co.uk/earth/wildlife/3309619/Elephants-are-scared-of-bees-scientists-say.html

http://www.detaoma.com/newsds.asp?nid=1282


Prentice Hall31. Hammer and Champy (2003); Re-engineering the Corporation; Collins Business

Essentials32. Simonton (1999); The Origins of Genius; Oxford University Press.33. Michod, Richard (2000); Darwinian Dynamics; Princeton University Press. 34. Kenney, Martin (2000); Understanding Silicon Valley; Stanford University Press.35. Deming, W. Edwards (2000); Out of the Crisis; MIT Press36. De Tao Master's Academy (2011); http://www.detaoma.com/newsds.asp?nid=1282 37. Flavell, J. H. (1976); Metacognitive aspects of problem solving. in L. B. Resnick

(Editor). The Nature of Intelligence; Hillsdale, NJ: Erlbaum38. Greene, Richard Tabor (2010); What If Creativity Were 60 Things Not One, Towards

a Grammar of Design: Design Creativity 2010, Springer39. Amabile, Teresa (2009); "Corporate New Ventures at Procter & Gamble." Harvard

Business School Case 897-088 40. Greene, Richard Tabor (1993); Global Quality; ASQ and Irwin Professional.41. Greene, Richard Tabor (2010); What If Creativity Were 60 Things Not One, Towards

a Grammar of Design: Design Creativity 2010, Springer42. Greene, Richard Tabor (2007); Excellence Science: 54 Routes to the Top of Any

Field; www.youpublish.com.43. Harnad, Stevan (2006); Creativity: Method or Magic? in Hungarian Studies Vol 18

No. 244. Styhre and Sundgren (2005); Managing Creativity in Organizations; Lavoisier45. Greene, Richard Tabor (2006); Are You Creative? 60 Models; www.youpublish.com46. Michod, Richard (2000); Darwinian Dynamics; Princeton University Press.47. Crutchfield and Schuster, editors (2003); Evolutionary Dynamics: Exploring the

Interplay of Selection, Accident, Neutrality, and Function; Oxford48. Nowak, Martin (2006); Evolutionary Dynamics: Exploring the Equations of Life;

Harvard49. Page, Scott (2008); The Difference: How the Power of Diversity Creates Better

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www.youpublish.com51. Brown and Duguid (2005) The Social Life of Information, Harvard Business Press.




http://www.detaoma.com/newsds.asp?nid=1282

AUTOMATA

DESIGN

DESIGNING AUTOMATA, AUTOMATA THAT

DESIGN

The papers of this section are named below:

1) Simple Programs--a model of creativity2) Social Automata--a replacement of brainstorming3) Social Automata Replacing Brainstorms in Design4) Social Automata Replacing Brainstorms, Mass Workshop Events Replacing Pro-cesses5) What Complexity Theory Can Contribute to Three Current Japanese Policy Chal-lenges.

The first article is a summary, believe it or not, of Stephen Wolfram’s work of genius ANew Kind of Science. This book is the bible of automata theory, a one-stop-shop for itall. Not only that but the English language in which it is written, is perfected andedited and improved and simplified--so each sentence is perfectly clear, but thethought movement from sentence to sentence, is challenging. It is a hard read, notbecause of language, but because of thought movement. The simple programs modelof creativity it presents talks about how the simplest think-able machines suffice tocreate the most complex behaviors and patterns in the universe. Complexity is notthe result of many diverse hard things but the result of a very very few extremely sim-ple things interacting simply. This is remarkable. There are many automata, out ofthe 300 basic ones, that are capable of general computation--the kind brains andmachine computers do. That means much of the universe is a smart as we are interms of its capability of producing surprise, pattern, new invention.

The next two papers above are about social automata--algorithms passed in certainpatterns among people arranged in certain patterns, with each person executing oneor several steps in the algorithm thusly passed, handing intermediate results to theadjacent person-steps. At regular short time intervals, overall designs accumulate,till in a couple of hours, dozens pile up--at which point dimensions of difference analy-sis gets done, to invent new-to-history dimensions of design.

The next paper presents the above social automata as a replacement for brainstorms,and other computation and bio-inspired replacements for common media and inter-faces--new ways to meet, discuss, process work, brainstorm, read, write, present.

The last paper examines complexity theory--a theory like simple programs, of howcomplexity and invention arise from simple things interacting--as a model for newforms of leadership and policy, based on tuning interactions among populatons ofgroups, teams, units, technologies, ideas, persons.

SIMPLE PROGRAMS MODEL OF CREATIVITY

What if time and space were epiphenomena, side-effects of the operation on a sub-quantum scale, of the world’s simplest thinkable machines--one dimensional cellu-lar automata? Math has proven that behaviors and structures as complicated as thegreatest that machine computers or brains-as-we-know them can come up withemerge without intent or design, “are invented without inventors” by these sim-plest thinkable automata machines. From the stripes on zebras (optimized tomake the entire shape of that animal disappear when running at average zebraspeed in typical grasslands) to Los Angeles traffic jams (inevitable from the slight-est perturbations in driver attention and speeds) to the emergence of time itselfand space itself just after the Big Bang, to the emergence of Natural Selectionitself, the process that “invented” life and all lifeforms, including us--the power ofthis “creativity without creators” overwhelms all that mere human creations haveyet achieved.

This context demands respect for how the universe with immensely simple autom-ata--initial states, cell layouts, rules applied simultaneously to all cells in cell lay-outs, repeating rule application to results of prior applications--generates andinvents things more complex than humans create, and this context demands disre-spect for the overweening complicated laborious hit-and-miss huge human machin-eries needed to “generate and invent” results less complicated than the universegenerates and invents with vastly simpler machinery.

The diagrams given below summarize the main points of a 1000+ page book, byStephen Wolfram, summarizing the above argument and its implications for design.By imposing HUMAN DESIGNERS we weaken design overall and delay its potentialcreativities and encumber vast innovations in ends and means with more limited,more local, more biased human traditions and minds. Till we regularly match theinvention of zebra stripes, an enormous modesty about what we up till now havecalled “design” is in order and badly needed. The enormous pretentiousness ofdesign, designing, designs, and designers--their lust for prizes for notice for salesfor decent pay makes them all petty and less creative than the simplest thinkablemachines.

This chapter introduces as paradigm taking years to flesh out and apply, of INDI-RECT DESIGNING by non-human designers of various sorts--putting people, tradi-tions, ideas into social design automaton arrangements and TUNING INTERACTIONStill better than dreamed of results EMERGE. This is the anti-design route to design--undoing all that designers are so proud of in their tradition, education, “designthinking”, and self promotion self praise.

This chapter is challenging--as deeply abstract as possible and its power is in pro-portion to its abstraction and difficulty of application. Many readers will wilt, unin-terested in anything not easy and fast. They need not waste time on this chapter(or on their career for that matter). This chapter is for those few who want andknow they need MENTALITY STRETCHING.

The Simple Programs Model of Creativity

Apply New Commonsense’s 96 Intuitions

Imagine a Basic Exploration Procedure

Choose and Design Substrate

Components of Simple Programs

Select Appearance of Simple Programs

Apply Simple Programs

Analyze Effects of Simple Programs

Creativity

Drop Traditions

Explore Simplest Universal Systems

discovery--8 intuitionssimple programs--8 intuitionsa new math--8 intuitionsa new randomness--8 intuitionscontinuity overthrown--8 intuitionsnatural selection overthrown--8 intuitionsphysics overthrown--8 intuitionsuniverse as a simple program--8 intuitionsmind overthrown--8 intuitionsa new physical law--8 intuitionsa limit on knowing--8 intuitionsfree: will, math, & applications--8 intuitions

model your project as at rightdefine all possible initial conditionsdefine all possible rule sets(run your model with all possible combinations of initial conditions and rulesets, watching for: which of 256, IC recurrence, boundary emergence, classes 1 to 4, signs of universality)

purpose (what intend to invent)allowed transformations (rulesets)allowed initial conditionschoose/design automaton substratebuild automaton(run automatonscan results of automaton runsspot interesting emergents and universalities)

initial conditions = programrules of transformation = computer hardwarelayout optionspoint-of-action options

start with cellular automata modelremove assumptions in turn to get: totalistic cellular automata mobile automata turing machines substitution systems sequential substitution systems tag systems cyclic tag systems register machines symbolic systems operator systems

look for layers feeding adjacent layerslook for variety that could be made emergent from one automaton substratelook for complicated layouts, rules of transform, points of action that add nothingremember universal systems can emulate each other but some are very cumbersome and slow ways to do things easy in others

most existing concepts and relations are emergents from simpler substrate automatonsmost continuity is randomness masking underlying discrete automatonslook for randomness assumed from environment but really inherentlook for repetition and nesting handled by existing models that ignore randomness and complex resultsidentify which of 256 elementary programs your results resemble

The Discipline of Mental Assumption Scouring

The Discipline of Adding Minimal New Ideas

Create byExploring

Simple Programs

Discoverthe New

SimplePrograms

Math of

a NewSource of

RandomnessOverthrows

Continuity & NaturalSelection

A NewSource of

ComplexityOverthrows

Physics& MindTheories

A New Physical LawLimit on Knowing

& TechnoFrontier

Dis-cov-ery

Simple

rams

A NewMath

A NewRandom-

ness

Contin-uity

Over-Thrown

NaturalSelection

Over-Thrown

PhysicsOver-Thrown

Universeas a

SimpleProgram

A NewPhysical

Law

A Limiton

Knowing

MindOver-Thrown

Free:Will,

Math, &Applicns.

Prog-

The Simple Program Types: The Simple Program Demonstrations:

Adapted from Wolfram’sANew Kind of Science, Stephen Wolfram LLC, 2002

Copyright 2002by Richard Tabor GreeneAll Rights ReservedUS Government Registered

Various Domain Implications:Simple Program Behavior Types:

O: humansmore complex

than worldN: humans &

world samecomplexity

O: complexbehaviorcomes fromcomplex rulesN: it comesfrom simplerules

O: reality iscontinuous socontinuous mathN: reality discretebut looks smooth at larger scale so discrete maths

O: nature artifacts morecomplex than human cuz godN: cuz we simplify design to whatour perception can handle sohuman artifacts simpler

O: complexity in livingthings from naturalselectionN: simple fromselection;complexityfromsimpleprog-

rams

O: behaviorvariety frommechanismvarietyN: same behavior frommany systemsregardless ofdetails

O: complexity of our perception greater than world; N: same as world

O: universal computationsystems rare in nature;N: universal systems verycommon in nature

1

2

3

4

5

6

7

8

O = old commonsenseN = new commonsense

O: human analysis& perception can

invent formulas showingfuture states of systems

N: us and systems samesophistication llevel so

must run system to predict it

O: ordinaryprograms needcomplicatedsoftware forcomplicatedbehaviors;N: simpleprograms canmake complicatedbehavior (initialconditions =prgm, rule =hardware)

O: firstcomputersused to automateold mathN: computers as basisof entirely new maths

O: usual science, ignorerandomness (ex: of primes)N: computer science, ignoreregularity (ex: of primes) explorerandomnesses

O: add complexity to rulesadds complexity to systembehavior

N: beyond thresholdmore complexityof rules doesnot changebehaviorcom-plexity

O: regularityof cellular automata causestheir complexbehaviorsN: when removeall such regular-ities, still getcomplex behaviors

O: myriad types of behaviors in world

N: repetition, nesting, randomness, localized

structures reappear

O: behaviors are specific to rulesthat generate themN: there are universal system behaviors independent of how they are implemented in rules

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15

16

O: usualscience, select

behavior, findexamples of it

N: new science, explore all behaviors of

simple systems

O: numbersare about sizeN: numbersare aboutdigit sequences O: in

chaotic systemssensitivity to initialconditions makes randomnessN: randomness fromsimple rules

O: partial differentialequations capture somethingdeep about world; N: they are a historical accident and shallownot deep reflections of reality

O: base science on mathequations (constraints);N: base science onsimple programs(processes)

O: adding spacedimensions addscomplexityN: adding themdoes not increasecomplexity oncethreshold is reached

O: complex behavior comes from complex rules; N: it comes from simple units interacting

O: model reality with equationsexpressing constraints processesmust meet; N: model reality onsimple programs that are processes of development not just constraints

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20

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24

O: greatest complexity is

randomness N: no, it is

complexity mixedwith order randomly

O: randombehavior comesfrom randominitial conditionsN: it comes fromsystem itself, it is inherent inrules of the system

O: random-ness makessystems unstableN: randomnessmakes systems stable

O: variety in behaviors ofsystems come from differences incausal elements in themN: standard types of behaviors sharedby systems having very differentunderlying elements

O: randomness comes fromrandom rules or initialconditions of a system;N: randomness isinherent propertyof system itself

O: a system is random becauseits initial condi-tions make it so;N: that begs thequestion sincewe ask whatcaused from-out-side environmt

randomness that entered system

O: random means no generated by simple procedure; N: it means we find no regularities in it

O: randomness is widespread cuzso many things interactN: randomness is widespread cuzsuch simple systems generate lots of it

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O: details ofrandom behavior

never repeated in nature;

N: details of randomnessrepeated exactly often

(same body pattern inmembers of same species)

O: behaviorwe see is smooth becausecauses are smooth;N: causes are discrete butrandomnessaverages effectsso we see smoothness

O: complexbeahviorsfrom systemsevolving to meetconstraints; N: it comesfrom simple rulesl; iterativesearches too slow even forbillions of years to find processthat meets constraints

O: extract a few specialnumbers, build model to attain them; N: search simpleprograms till similar overall systembehavior is found (complex randombehaviors ignored by usual maths)

O: cellular automaton modelsdo not resemble causesN: differential equationmodels do not resemble causes

O: not inventingnew models butfinding experi-ment data &implications ofexisting models;N: invent newmodels and model types

O: randomness of fluid flow comes from initial conditions; N: it is inherent in process of system’s

simple rules

O: equations specifying constraintsbut no way to find process that produces something meeting them;N: simple program that evolves explicitly through all states

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O: complexityof living things

selected by natural selectionN: 4 billion years too small

time for iterative search tofind processes to meet

constraints so simple programs furnish complexity

O: features ofliving thingsoptimal cuzselected by NS;N: features aregood enough &not fatal cuzcombination ofaccident & selection, and cuz

O: coarse

simple programsare easy to make

and finefeatures fromselection: N: coarse from selection,fine from simple rules

O: natural selection is powerful: N: natural selectionis weak, can only operate on simple,stable features, with clear alternativesets

O: natural selection can select which automatonprogram needed fora behavior; N: impossible togo from behavior back to rules that

produceit

O: naturalselection makesthings complex;N: it makes themsimple; simpleprograms makethem complex;NS is like humanengineering, bothmake things simple

O: features that change the most between species

show natural selection effects; N: no, they show simple

program effects

O: we see similar complex systemsin different species cuz selectedseveral times; N: no, cuz simpleprograms widely available

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43

44

45

46

47

48

O: systemsevolve to

complexity notsimplicity:

N: systems canevolve to

simplicity buthumans cannot

find the initialconditions needed

to do it practically

O: allsystems obey 2ndlaw ofthermo-dynamics;N: class 4automatanever settledown, do not obey it

O: complexitywe see inuniverse comesfrom complex means;N: it comes fromsimple programs

O: what we see comesfrom laws of universe;N: we see discrete causesaveraged by randomnessfrom plentiful simple programs

O: universe is complexso decompose it tosolve it; N: it isgenerated bysimple programso no

incrementalmethodworks

O: currentknown lawstell us natureof universe;N: currentlaws tell onlyemergentfeatures notgenerativeones

O: relativity, quantum, &

quantum gravity models can be

made to converge:

N: no, evolve theirfeatures up

from simpleprogram

model

O: simplicity of knownphysical laws imply

complex origins; N: simplicity of origins

different source (simpleprograms) than laws

for emergents

O: space

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53

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56

is continuousN: it looks

continuous butit is discrete

O: ouruniverse hasspace, particles,motions, gravity:N: our universe hasspace, the restare emergentfrom the simpleprogram thagenerates

spcae

O: spacehas contentsN: space &its contents aremade of same stuff

O: space-time ascontinuumN: space generated bysimple automaton, timeas real evolution of thatprogram

O: space-time as a whole our universemoves withinN: universeevolves indiscretespace &timesteps

O: symmetrybetweenspace & time is deeppropertyN: it ismerelyemergentproperty,n not deep

O: global clock updates cells in universe automaton;

N: universe ismobile automaton

updated 1 cellat time, but

we are in itso till we areupdated, no

time exprce

O: universe is strings vibratingN: mobile automaton creating

causal net (row = space,col = time) slices = moments,

tangles = particles, curve = gravity, automaton

randomness = quantumeffects, motion emergent

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O: mindequals

data reduction by:find irrelevancy or

regularity; N:mind = data

recognition by:find irregularity &

regularity

O: seecomplexityfind programthat generatesit = perception& analysis:N: impossibletask for systemsas complex as our percept/analysis are(= most)

O: if simple’rules makebehavior we can find;N: like findingprocess meets constraints, oftenimpossible

O: something is random if no shortdescription exists;N: is random if no shortdescription can be found;

O: mind complex compared to thingsin world;N: mind and world samecomplexityso we see“complexity”N: mind is

forms ofsimple programs,can spot repetition &nesting, not

further;

O: mind caninvent meansto summarizemost phenomena;

N: mind is cell automaton of nerve layers

making hash code

memory

O: mind is manydifferent modular

computers

O: thought is many differentfeatures & capabilities;N: all are emergents frommemory that generates all

working as layered cellular automaton

O: different

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computers cando different

computations;N: universality

is proved, canemulate any

other computer

O: computers &languages butscience is notconcerned;N: simpleprograms atthe heart ofmost sciences,so toouniversality

O: simpleautomatonprograms toosimple foruniversalityN: have builtuniversal automaton,27 of 256 are universal

O: Turing, register,cellular machines differentN: can emulate Turing withregister or cellular and viceversa

O: computational universalityrare thing; N: it is muchless rare, lower thresholdthan we thought,systems everywhereare universal,capable of mostcomplexfunctions in universe

N: there is 1highest levelof computatn.is achieved by

all systemsnot obviously

simple

O: few systemsachieveuniversalityPRINCIPLEof GCEquvlnce

N: when in nature there are no forces

forcing simplicitysystems selected

not functionally but simplicity

ease of

O: systems selected functionally

finding

O: can find initial conditions that make any universal systemperform computation of anysophistication level; N: can bepractically impossible to find

such initial conditions

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O: formulasamong key

measures ofthing give its nature;

N: computationalmodels of evolution

process of thingsgive complexities

& future states

O: mathof equationsexpressingconstraintsentities mustmeetN: but oftenimpossible tofind processthat meets, sosimpleprogramsare better

O: continuityis realN: it is idealizatnof how randomnesshides discrete units

O: space and quantumprobability amps continuousN: absolutely all in theuniverse is discrete

O: our minds morecomplex than ourworld so complexityis illusion;N: minds &world samecomplexityso we can’treducesomepatterns

O: goal isfind shortcut to systemevolution tofind results;N: no shortcuts possiblefor manysystems =computatnlly

irreducible

O: if find definite rules of system

can predict outcome;N: even if know

rules, cannotpredict, must

run

O: no competition betweensystems for making predictionsand systems they predict;N: is competition so often

cannot predict cuz same level of complexity

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88

O: no ultimate limit

on what sciencecan know:

N: if complexfor us CIrreduc.ble

so no theory/formula short cut,

O: if findlaws of systemit is not freebut determined,so human freedom from god;N: minds areCIrreduc.bleso cannot predictso free will

O: shockat Godel’sfinding thatmaths are incomplete;N: no shock sincemaths & world filledwith universal systems

O: math has all butirreducible systemsN: math does what it has good tools for, simple programsgood for irreducibility

O: math is old andhas explored mostaxiom systems;N: all math hascovered tinyfraction ofaxiomsystems

O: intelligenceis only inhumans;N: if it isability forcomplexcomputationsmany systemsin world have

it beyond humans

O: simple programswithout applicns.

N: bias/deadlock/conflict randomness,

evolve chemicalnano computers

using rule 110

O: find systems that does a purpose N: explore all possiblesystems, observe purposesthey might solve

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cellular automatatotalistic cellular automatamobile automataturing machinessubstitution systemssequential substitution systemstag systemscyclic tag systemsregister machinessymbolic systemsoperator systems

class 1: repetitionclass 2: nestingclass 3: randomnessclass 4: complexity-- moving persistent local structures interacting & mixed with randomness

all simple program types arithmetic based automata

recursive number sequencesdigit sequences for special numbers

math functions represented by substitution systems iterated maps continuous cellular automatapartial differential equations2 & 3 dimensional cellular automata2 dimensional turing machines2 dimensional substitution system fractalsnetwork systems with different operations at different nodesmultiway system (1 unit replaced by 1 or 2 new units at each step)

constraint systems

Examples of all 4 behavior classes for:

limited row size wrap around cellular automata

kneading model of initial condition effects

similar rule automata have extremely different behaviors

automata snowflake/crystal/fracture/ turbulence/bodyshape/ animalpatterns

random mutation in automata models

substitution system model of plant growth

reversible automata with irreversible behavior

block cellular automaton

constraint networks

math--vast range of unexplored abstract systemsphysics--wrong continuous tools for discrete realitiessocial science--distinguish discrete effects from emergent onescomputer science--show all process is computationalphilosophy--definitions of life, mind, intelligence, randomnessart--percept/phenomenon complexity competitionstechnology--automaton techniques for vast complexity from simples

1 Commonsense: humans are more complex than the world and universe we inhabit(humans are special)

Intuition: the universe and everything in it (we, the world) are cellular automatons, hence,the same level of complexity (humans are not special--science has always advancedby discovering new forms of this).

2 Commonsense: phenomena are continuous so models of them should be in continuousmaths

Intuition: phenomena are discrete on small scales and look continuous at larger human sizescales so automata should explain both scales where continuous maths cannotexplain generative small scale stuff.

3 Commonsense: complex behavior comes from complex rules and systems.Intuition: extremely simple rules suffice to produce behavior as complex as anything in our

universe.

4 Commonsense: the reason the complexity in nature is greater than the complexity inhuman artifacts and doings is it was designed by a divine being.

Intuition: the reason the complexity in nature is greater than the complexity in human arti-facts and doings is we constraint our engineering to produce things our limited per-ceptual and thinking systems can readily handle.

5 Commonsense: all the complexity we see in biological organisms comes from beingselected by natural selection.

Intuition: much and maybe most of the complexity we see in biological organisms was neverselected for and is a happenstance occurrence due to the widespreadness easiness offinding universal computation systems in natural processes.

6 Commonsense: the variety of complex behaviors we see in human and natural systemsentails a variety of mechanisms producing them.

Intuition: the same basic forms of behavior occur over and over again in system after sys-tem, independently of underlying details.

7 Commonsense: universal computation systems, systems that can emulate any othercomputation system, are complicated and therefore rare.

Intuition: an immense number of processes in nature and human undertakings are universalcomputation systems and such systems can be extremely simple, generated byextremely simple rules.

8 Commonsense: the complexity of our human systems of perception and analysis isgreater than that of the simple programs

Intuition: the complexity of our human systems of perception and analysis is the same asthat of simple programs that underly all the phenomena in nature so we see complex-ity because the level of object and subject in complexity terms is the same.

9 Commonsense: by applying human intelligence, perception, and analysis we can comeup with formulas allowing us to predict how complex natural systems evolve

Intuition: most natural systems are as computational sophisticated (complex) as our ownintelligence, perception and analysis is so there is not short cut to doing all the com-putation of running the system for seeing what it eventually produces--a fundamentallimit on theoretical science and what it can predict

10 Commonsense: in ordinary computers to get complicated behavior complicated pro-grams are needed, roughly as complicated as the behavior you want.

Intuition: in cellular automata where rules are like the hardware and initial conditions arelike the programs, extremely simple rules and initial conditions suffice to producebehavior as complicated as anything in the universe

11 Commonsense: computers were first used to automate, make easy, traditional viewsand maths

Intuition: computers might have other uses different and more profound than making it eas-ier to do past things

12 Commonsense: intuition from traditional science (systematic ways of generatingprimes, ignore randomness with which primes appear

Intuition: intuition from computer science (systematic ways of generating randomness,ignore regularity)

13 Commonsense: adding complexity to the underlying rules of a system adds complex-ity to the behavior of the system

Intuition: adding complexity to the underlying rules of a system, beyond a certain pointadds no complexity to the behavior of the system

14 Commonsense: the regularity of the rules or cellular space or initial conditions of cel-lular automata cause them to be capable of categories of behaviors actually seen(they are therefore rare exceptions not general)

Intuition: as we remove one by one all the main regularities in cellular automata we get sys-tems capable of exactly the same categories and complexities of behavior

15 Commonsense: they are myriad types of behaviors of systems in the worldIntuition: repetition, nesting, randomness, and localized structures are the four types of

behaviors found in nearly all systems in the world

16 Commonsense: behaviors are localized and specific to the rules generating the behav-iors

Intuition: there are universal types of behaviors of all systems in the world independent ofthe particulars of how those are implemented in terms of initial conditions and rulesof the system

17 Commonsense: select behavior one is interested in and find examples of it to studyIntuition: explore what behaviors are generated by all versions of simple systems

18 Commonsense: numbers are about size (magnitudes)Intuition: numbers are about digit sequences (patterns)

19 Commonsense: in chaotic systems sensitivity to initial conditions is what producesrandomness of behavior in the system

Intuition: the randomness comes from the simple rules of the system, not the initial condi-tion differences being exaggerated by shifting operations

20 Commonsense: partial differential equations capture something deep about hownature is

Intuition; they are a historical accident that captures nearly nothing deep about how natureis

21 Commonsense: base science on mathematical equations (but given the equation“rules” it is hard to figure out how the system will behavior)

Intuition: base science on simple programs (since such program are explicit rule sets it iseasy to figure out how the system will behave, just run the rules--though shortcutsabout eventual results may be impossible to find in finite time)

22 Commonsense: adding further spatial dimensions adds complexity to behavior of sys-tems

Intuition: adding further spatial dimensions adds no complexity to system behavior

23 Commonsense: complex behavior comes from complex rulesIntuition: complex behavior comes from units interacting

24 Commonsense: model complex behavior using equations expressing constraints (andconservations) that entity relationships must meet (how to find actual system pro-cesses that do this can be nearly impossible when not impossible)

Intuition: it is hard to get randomness behaviors and interacting localized structure behav-iors from constraint systems compared to getting them from cellular, mobile, Turing,substitution, network automata.

25 Commonsense: the greatest complexity is randomness (because there is not shorterdescription of it by capturing regularities in it)

Intuition: the greatest complexity is randomness mixed with order--interacting persistentlocalized structures

26 Class 4 above 2 and 3 in complexity but between 2 and 3 in activity level. Class 1 for-gets info from initial conditions; class 2 info from initial conditions is retained butlocalized, not communicated around the system; class 3 any change anywhere iscommunicated everywhere in the system eventually; class 4 long range communica-tion sometimes occurs but much communication is localized. Class 3 systemsbehavior details are affected by changes in initial conditions.

27 Commonsense: random behavior comes from random initial conditions to a systemIntuition: random behavior comes from the rules of the system itself, without initial condi-

tions needing to be random

28 Commonsense: randomness makes systems unstableIntuition: much of the stability in systems around us comes from their randomness

29 Commonsense: the complexity we see in natural systems comes from complex differ-ences in the causal elements of these systems (thus natural systems do not resem-ble system programs in behavior types possible and actual)

Intuition: the complexity we see in natural systems comes from something irrespective ofthe causal elements of these systems (the the behavior types possible and actual forsimple programs are the same as for natural systems having extremely diverse lowlevel causal elements)

30 Commonsense: randomness in a system’s behavior comes from randomness in a ruleof its operation or random initial conditions (this makes system behavior sensitiveto initial conditions too)

Intuition: there is another source of randomness besides those two, randomness resultingfrom simple rules of the system like in cellular automata

31 Commonsense: explaining randomness in a system’s behavior by randomness cominginto the system from outside via initial conditions

Intuition: but that begs the question for we still have to explain where that outside random-ness came from (inherent randomness generated by simple rules as in cellular autom-ata perhaps)

32 Commonsense: traditional definition of randomness is sequences that can never begenerated by any simple procedure whatsoever

Intuition: but it is impossible for any process in our universe to generate randomness likethat (since that would rule out simple cellular automata that produce randomnesseasily in terms of sequences all our perceptual and analysis techniques can find nopatterns (regularities) in)

33 Commonsense: randomness is widespread in nature: this is hard to explain becauserandomness is defined as coming from systems having no simple rules or proceduresof any sort

Intuition: randomness is widespread in nature: this is because it comes from systems likecellular automata that are so simple and widespread in nature that we can expect tofind lots of them everywhere, just as we find lots of randomness everywhere

34 Commonsense: details of random behavior in natural processes never are repeatedfrom one experiment to the next (because such randomness comes from outside the

system as environmental condition changes in initial conditions or boundary condi-tions)

Intuition: details of random behavior in natural processes can be found to repeat fromexperiment to experiment because a specific automaton-like process is generatingthat randomness (two members of same species that nearly repeat random-lookingpatterns or body architectures, body symmetry in animals,

35 Systems with smooth continuous behavior come have elements that are discrete (flu-ids and their molecules for example) because randomness averages out discretebehaviors generating appearance of continuity; continuous behavior can arise insystems with discrete components only when there are features that evolve slowlyrelative to the rate of small scale random changes;; Continuous systems can creatediscrete behavior (water phase change for example)

36 Commonsense: (behavior of significant complexity comes from satisfying constraints )systems evolve to satisfy constraints

Intuition: (behavior of significant complexity comes from explicit evolution rule processeslike in automata) iterative searches, with randomness and other tricks, to find pat-terns that meet constraints still are much too slow for even billions of years to suf-fice to find patterns that satisfy moderately complex constraints

37 Commonsense: test models by extracting a few special numbers from observed systembehavior and get the model to attain those numbers

38 Intuition: systems that are simple can be characterized by a few special numbers, butsystems of any complexity cannot so traditional math’s interest in attaining a fewspecial numbers is useless for them

39 Commonsense: a cellular automaton model does not resemble the same low level ele-ments that in reality make a gas’ behavior etc.

Intuition: a differential equation model does not resemble the same low level elements thatin reality make a gas’ behavior etc.

40 Commonsense: one expects that science would be filled with inventing and trying outnew models but in fact most science is accumulating experimental data or workingout consequences of existing models

Intuition: existing math type models give constraints but no hint as to how to find processesthat meet those constraints so working out existing models is time consuming anddrives out time for trying new models

41 Commonsense: the randomness of fluid flow turbulence comes from initial conditionsIntuition: the randomness of fluid flow turbulence comes from inherent dynamics of the sys-

tem generating randomness

42 Commonsense: the complexity of pattern and behavior of living things comes fromnatural selection

Intuition; the complexity of pattern and behavior of living things come from simple pro-grams many of which generate randomness or patterns of great complexity

43 Commonsense: 4 billions years of life on earth is such a vast amount of time that nat-ural selection has been able to meet many difficult constraint conditions of lifewith adaptations by searching the space of all possible living thing features

Intuition: the actual spaces to be searched are much too vast for small amounts of time like4 billion years to suffice therefore most of the complexity there in living things is notselected for but from simple programs used to generate many processes in the uni-verse and life

44 Commonsense: current features of living things are optimal for survival because natu-ral selection selected them into being

Intuition: most current features of living things are not optimal at all but good enough towork and not fatal, because a combination of selection effects and possibly biggeraccidental features of simple programs that generated them

45 Commonsense: the features of living things are there because selected by naturalselection

Intuition: the features of living things are there because they are easy for simple programsto generate hence plentiful

46 Commonsense: coarse and fine features of living things alike come from beingselected by natural selection

Intuition: coarse features are key to survival functioning hence selected by natural selec-tion; fine detail comes from easy to find programs for doing things hence happen-stance not selected

47 Commonsense: any complex behavior or pattern in living things is there becauseadaptive for survival, selected for by natural selection

Intuition: the vast majority of complex behavior or pattern in living things is there becauseeasy simple programs for doing things abound and are found by mutation and createcomplexity for free that happens to be there not because selected by natural selec-tion

48 389 evidence that the above is true, four points

49 Commonsense: natural selection is a very powerful process (but if complexity is soabundant and easy to find in simple programs that genetic interactions could bewhy aren’t more features of living things much more complex than they are)

Intuition: natural selection can only operate on rather simple systems because 1 complexforms have variety too huge to be all tested/searched 2 complex forms have multiplesimultaneous functions so same detail may help some individuals and hurt others sonatural selection cannot act consistently 3 when behavior of a program is much morecomplex than the program itself mutation in its rule will have immense numbers ofchanges so natural selection cannot select out a few of them 4 if variations can go inmany different directions then there may be no clear cut positive and negativeadvantage for many of them so natural selection cannot act in consistent direction 5iterative random searches get stuck usually and are too slow to explore the vastarray of possible life forms

50 Commonsense: natural selection can select which automaton program is right for abehavior needed for the survival of an organism

Intuition: but it is nearly impossible to go from a behavior produced by some simple ruledautomaton back to what the automaton’s rules and initial conditions were (decompo-sition into organs of living animals is evidence that systems must be simple anddecomposable for natural selection to handle them)

51 Commonsense: natural selection is what makes natural systems complexIntuition: natural selection is what makes natural systems simple (like human engineering

forces systems to be simple)

52 Commonsense: natural selection is of a different order than human engineeringIntuition: natural selection and engineering are very similar--they work to meet constraints

(goals), they tend toward smooth continuous answers, they tend toward decompos-able systems, they reduce complexity of component and whole

53 Commonsense: features that change the most between species show natural selectioneffects

Intuition: features that change most between species show the most complexity hencecomes from easy simple programs that produce complex behavior because easy tomutate into

54 Commonsense: we see very similar complex features appearing in living thingsbecause natural selection selected them several different times as adaptive fororganism survival

Intuition: we see very similar complex features appearing in living things because manysimple programs are capable of behavior of great complexity hence by their simplic-ity are easy to find and mutate into

55 Commonsense: we observe systems increasing in randomness not random systemsevolving to simplicity

Intuition: simple initial conditions (and rules) suffice for many systems to create great com-plexity, therefore we set up experiments with initial conditions simple enough for usto imagine but the initial conditions that suffice to make a reversible automatonevolve from randomness to order are extremely unordered and therefore difficult toguess or iteratively search for so we observe systems moving from order to random-ness = second law of thermodynamics

56 Commonsense: all systems obey the second law of thermodynamicsIntuition: class 4 automata never settle down ,sometimes random behavior, sometimes

independent entities that interact, sometimes both, they do not obey the second law

57 Commonsense: the universe is complex so the rules that form it are complexIntuition: very simple rules might forms the universe’s complexity, so try rules having little

structure and assumption built it (simplicity, simplicity, simplicity)

58 Commonsense: what we see comes from the laws of the universeIntuition: randomness turns discrete generator systems features into larger scale averaged

out continuous looking features so what we see masks the actual nature of the uni-verse’s laws

59 Commonsense: the universe is complex so decompose it and find rules for those piecesIntuition: if the rule for the universe is extremely simple then slight changes in it will pro-

duce unworkable universes entirely unlike ours so you cannot incrementally find it

60 Commonsense: (simplicity in known physical laws imply simple rules of the universe)the currently known physical laws of science tell us about the rule of the universe

Intuition: (simplicity of known physical laws do not imply simples rules of the universe) cur-rently known physical laws are not fundamental but describe emergent features ofthe large-scale behaviors of some underlying simple rule.

61 Commonsense: space is continuousIntuition: but it is built of discrete elements

62 Commonsense: our universe has space and contents of space (our universe has grav-ity, elementary particles, motions and there will be underlying constructs for them)

Intuition: much of the contents comes from properties that space itself has (much of thecharacter of our universe will be a result merely of space, not special constructs);space and its contents are made of the same stuff so our universe is mostly just spaceitself; all the features of our universe must at some level emerge from the propertiesof space.

63 Traits of space that generates our universe’s features: low level rather random, emer-gent level what we see as space as human; network of nodes without location, onlylinks

64 Commonsense: that spacetime as a whole is laid out and our universe explore itIntuition; that out universe explicitly evolves across discrete time and discrete space steps

65 Commonsense: symmetry between space and time is a deep property of the universeIntuition: symmetry between space and time is a superficial emergent property of the uni-

verse

66 Commonsense: a global clock somehow defines the updating of each cell in theautomaton that is the universe

Intuition: we are in the automaton that the universe is so a mobile automaton updating onecell at a time can do a lot of updates of aspects of the universe including us before wenotice time has elapsed; so to us many cells in the universe appear to have beenupdated in parallel connected by emergent things like causality

67 Commonsense: a mobile automaton representing space (links form distances)

Intuition: a causal network representing the view of spacetime of us inside the universe’sautomation (links form causal paths); each updating event (of the universe’s automa-ton) is a node in spacetime [the rows are events occurring roughly at the same time;the links are directional by causation not reversible as the space network links are];successive slices through causal network corresponds to successive moments in time;every connection in the causal network amounts to propagation of an effect at thespeed of light over the elementary smallest distance possible; slices at differentangles equals motion at different speeds--this automatically produces shortening ofspace and time as Lorenz found a v2 over c2; particles are analogs of localizedautomata structures = emergent = tangles of connections in the network that isspace; particle known constants like charge will be properties of the tangle that theyare, their core, while energy and momentum will be a kind size of a coat around thatcore; the randomness of many quantum mechanics phenomenon comes from theautomata background of randomness upon which local structures move; if the uni-verse is an automaton network then particles, tangles, can continue to have activelink sets even when apart in space terms (action at a distance stuff, and heisenberguncertainty principle stuff)

68 Commonsense: motion is an intrinsic basic component of the universeIntuition: motion is an emergent phenomenon hiding many specific detailed operations

69 reduce data by finding irrelevant for purpose data and by finding regularities in data; data compression, feature detection, pattern recognition system identificationfind useful summaries of data; perception and analysis as inverse of simple pro-grams making complex behavior--we see complex behavior and want to find thesimple program that generates it--thus there are fundamental limits to what per-ception and analysis can do because simple programs can create arbitrarily complexbehaviors and going backwards from seeing those behaviors to what simple programcreated them is often impossible; this is similar to the problem of going from con-straints expressed in equations to what process satisfies them

70 Commonsense: if simple rules create a behavior it is not too hard to find what thosesimple rules are

Intuition: simple rules can create arbitrarily complex behaviors and going from behaviorsseen to what simple rules created them is like going from constraints to what pro-cesses satisfy them, = an almost impossibly hard task

71 Commonsense: something is random if no short description of it existsIntuition: something is random if no short description of it can be found (it may exist but

going from behavior to what short program generated it is often impossible)

72 Commonsense: something is random if no short description of it existsIntuition: something is random is no simple program can find regularities in it

73 Commonsense: human perception and analysis is very complex and sophisticated com-pared to the phenomena in the world it is directed at

Intuition: all forms of perception and analysis correspond to rather simple programs (simpleprogram forms of data compression methods reduce repetitive and nesting but notanything more complex, similarly our own human perception and analysis does not gobeyond recognizing repetition and nesting) we cannot recognize some forms of nest-ing and we see local patterns that are not there in random arrays

74 Commonsense: layers of nerve cells doing functions in the brainIntuition: layers of nerve cells interacting like cellular automata simple programs interact

in the brain (generating a hash code so that similar inputs get similar hash codes oflocations where contents are stored)

75 Commonsense: human thinking has various rather different aspects and capabilitiesIntuition: all such aspects and capabilities are emergent properties of a substrate of mem-

ory that generates everything

76 Commonsense: different computers are capable of different computations

Intuition: we have proved universality--beyond a certain threshold all computers are capa-ble of emulating each other and hence doing all computations that can be done

77 Natural languages are universal in that you can translate between them (though theycannot describe usefully the most complex outputs of simple programs); Naturalscience has not been related to universality in the past; universal systems can pro-duce arbitrarily complex behavior.

78 Commonsense: simple automaton programs are too simple to be universalIntuition: we can construct (have constructed) an automaton program that emulates all the

other automaton programs; nothing can be gained by using rules more complicatedthan the universal automaton and we have ways of vastly simplify the rules of theuniversal automaton; rule110 is a universal cellular automaton simpler than the uni-versal automaton; 27 of the 256 elementary cellular automaton rules may be univer-sal; universal systems produce complex behavior

79 (we can emulate with cellular automata Turing machines, register machines and theothers) we can emulate cellular automata with Turing machines, register machinesand the others)

80 Commonsense: computational universality is a rare thingIntuition: the threshold for computational universality is vastly lower than we thought,

much more of the world is done by universal systems than we thought

81 All processes, in nature and artificial ones by man can be viewed as computations.Any process that follows definite rules regardless of the kinds of elements itinvolves is a computation. So two kinds of processes--rules defined by people andrules defined by nature; A new law of nature--the principle of computationalequivalence--there is essentially just one highest level of computational sophistica-tion and it is achieved by almost all processes that do not seem obviously simple.Therefore universality if very common in the universe; no system can do computa-tion that are more sophisticated than those carried out by systems like cellularautomata and the like; When there are no constraints that force overall simplebehavior most rules appearing in nature were selected in no functional way, exceptthat they were very simple. When a system is universal, by choosing appropriateinitial conditions, that system can be made to perform computations of any sophis-tication that any other system can perform, but it may be impossible to find thoseinitial conditions for some universal systems even though they are possible and“there” in some sense; sometimes even a simple initial condition will generateevolution that includes blocks that correspond to essential all possible initial condi-tions. (directory idea 724)

82 Commonsense: traditional math models systems by formula relations among a few keymeasurable entities

Intuition: such maths miss the computational nature and complexities of systems

83 Commonsense: traditional math models system by equations expressing constraints onhow they behave

Intuition: such maths force extremely hard computations to be done to try to find processesthat meet such constraints

84 Commonsense: continuity is what makes traditional maths workIntuition: continuity is just an idealization, missing how randomness from many simple

included systems hide how local units work from emergent behaviors via averaging

85 Commonsense: space is continuous and quantum mechanics probability amplitudesare continuous

Intuition: absolutely everything in our universe will turn out to be discrete

86 Commonsense: systems eventually will be found to be simple because our systems ofperception and analysis are more sophisticated computationally

Intuition: the principle of computational equivalence asserts that systems in nature are notless sophisticated computationally than our systems of perception and analysis, theyindeed are equivalent, indeed equivalent to any system of perception and analysisthat can be, so things are complex because such systems of perception and analysiscannot find patterns in their more complex outputs; observers will tend to be compu-tationally equivalent to the systems they observe, so they will find the behavior ofsuch systems complex

87 Commonsense: goal of maths, to short cut computational processes that produceresults with formulas that find the outcome without doing that process

Intuition: the Principle of Computational Irreducibility: even if you have all the rules andall the initial conditions of a system you may not be able to ever predict it totalfuture outcomes

88 Commonsense: if you can find definite rules for a system then it will be easy to pre-dict how the system will behave

Intuition: but many simple automata have simple rules producing behavior arbitrarily com-plex that we can never predict

89 Commonsense: our systems for predicting are more computational sophisticated thanthe systems we are trying to predict behavior of

Intuition: they are not: competition between systems making predictions and systemswhose behavior they want to predict--to predict a system’s behavior the system mak-ing predictions must out-compute the system it is trying to predict, but the Principleof Computational Equivalence says in general systems from making predictions willbe computationally equivalent to systems we are trying to predict.

90 Commonsense: no ultimate limit on what science can be expected to doIntuition: systems that are complex for us are most likely computational irreducible hence

we can formulate rules for systems but deducing the consequences of such rules is aremuch work as evolving the entire system from scratch itself = no short cuts via for-mula or theory

91 Commonsense: a good model is simpler than the phenomenon it modelsIntuition: an accurate model of a computationally irreducible system must involve computa-

tions as sophisticated as the system it is a model of, but we have seen that evenextremely simple rule sets produce behavior arbitrarily complex so we can learnmuch of the behavior of even computationally irreducible systems using extremelysimple rule sets in our models of them--that is good news

92 Commonsense: in traditional science we can find the underlying laws of a system andsystem controlled by laws are not free so humans have free will because of some-thing divine, outside this universe

Intuition: human brains have simple rules that, like cellular automata, are computationallyirreducible producing behavior arbitrarily complex so the only way to know howhumans will behave is run their brains as a program = free will; the gap betweenextremely simple basic unit rules and arbitrarily complex behavior that emerges isour apparent freedom and the freedom of many other systems we observe (take“computationally irreducible system behavior to mean free of obvious laws)

93 Commonsense: math--from simple axioms complex behaviors is like nature--from sim-ple elements complex forms and behaviors

Intuition: both math and nature by the Principle of Computational Equivalence are filledwith systems equally complex coming from simple rules

94 Commonsense: shock at Godel’s theorem showing logical incompleteness of mathsIntuition: not shocking at all since math is filled with computationally irreducible systems

of arbitrary complexity from simple rules

95 Commonsense: math is full of everything but complexity of irreducible systemsIntuition: math choose topics that its tools were good at handling so avoided the vast array

of irreducible systems in nature and even eventually avoided contact and inspirationfrom nature itself

96 Commonsense: math is old therefore has in time explored many if not most possibleaxiom systems

Intuition: math has explored a tiny fraction of possible axiom systems, there are many newtypes unexplored for not other reasons than math has historically happened on a par-ticular topic of continuity

97 Commonsense: intelligence belongs only to humansIntuition: if intelligence is ability to do sophisticated computations all sorts of natural and

artificial systems must have it because universal systems are plentiful

98 Commonsense: we can define life so as to distinguish it from non-living thingsIntuition: life and simple programs ultimately cannot be distinguished

99 Commonsense: we can recognize alien life via purposefulness of its artifacts, or theirsimplicity, or their complexity

Intuition: because universal systems are plentiful we will not be able to tell whether a com-plex or simple systems resulted from what we would call living beings or just resultedfrom any of a great variety of other universal systems at work

100 Commonsense: simple programs have simple applicationsIntuition: systems in which bias or deadlock can be avoided by applying randomness and

systems that have to out-think an opponent; many alternate logics and axiom sys-tems untried historically can be the basis for computers, a chemical system couldevolve into being a computer if based on rules like 110 of cellular automata (a line ofchemicals as a row of an automaton, and catalysts as rules), simple automaton rulesused to build or repair tissues and organs,

101 Commonsense: find a system that does a purposeIntuition: explore all possible systems and note purposes that could use some of their com-

plex patterns of results

A Note for Grad Students and Researchers

If we define a community of physicists in the late 1800s, initial conditions they faced orselected, and rulesets they participated in, we can model the emergence of special relativityas the outcome of a disciplinary automata process--collective minds repeatedly applying arepertoire of transformations on an expanding body of inputs--latest experiment results andfrustrations with theory from the most recent automaton ruleset application being the inputfor the next application of those rules of transformation. Alternatively, define some initialcondition thoughts in Einstein’s mind and rules by which he transformed/examined them iter-atively applied to the results of each such rule application. The trains of thought Einsteinactually experienced in inventing relativity theory is an emergent result of this thoughtautomaton of initial thought conditions acted on by a set of transformation rules (interests,skepticisms, alternative visions, advice from others attended to). This chapter’s model isthusly, in two ways, a model of how all past creations were achieved.

If we want to paint something really beyond all the assumption of all modern art thus far, wecan set up an automaton with initial conditions alternatives and ruleset alternatives, then runsimulated versions of that on computers or social versions (substrates) of that among groupsof people till results better than what we wanted, intended, planned, and expected emerge.This chapter’s model is thusly a model of how all future creations may be achieved.

As you read through Wolfram’s book you trace huge insights and sections of it back to variousother books on complexity theory that have been published. The lack of attribution to thisother work troubles me. However, Wolfram’s book more coherently thinks through andimagines how the various complexity theory findings and ideas relate to each other. He alsohas applied the ideas to accomplish more in many ways than most other people. In particu-lar, his mastery of physics and maths, of the traditional sorts, makes his change of approachto those fields a real act of imaginative accomplishment worthy of our respect.

The hard part of the transformations required for this chapter’s model of doing creativity isdisencumbering yourself of a lifetime of habits of using ideas and tools too complex to do thejob. We have all been trained in traditional disciplines in 2500 years of continuous mathsstyles of thought and conceptualization. The computation we have been exposed to istainted by that same history. It is all too elite, all too static, all too complex, and all toobfuscating not revealing. It is science for elites by elites and of elites. It is increasinglydivorced from life and human service needs. It has lost its way, its own tools deluding it.We all are absolutely filled with its influences, concepts, approaches, tools, salaries, andincentives. Shucking all that is neither easy nor pleasant given the hours and sweat investedin learning all that in our pasts. That is the challenge of this chapter’s model--dropping tra-dition first as our initial step toward creating.

Creating things indirectly by setting up automata that evolve into things, possibly better thanyou imagined or planned dissatisfies people over lifetimes trained to take full personal creditfor invention. Setting up automata that invent dissatisfies. It gets resisted and denigrated,ignored and countered. It will not be popular any time soon, however powerful it eventuallyproves to be. This is the second hurdle for creating in this chapter’s model--using modelsmuch to complex and ornate for the job required--something over lifetimes we were trainedto do. We will find it hard indeed to stop.

This chapter’s model of creativity comes, unlike others, along with already built and usedcomputer tools in the form of Mathematica notebooks available to those buying Wolfram’sbook, A New Kind of Science. Few models of creativity if any have appeared with proceduraltools for doing creation ready for widespread distribution, learning, and application.

Model Details

Two steps of creating: drop traditions (the discipline of mental assumption scouring) and explore simplest universal systems (the discipline of adding minimal new ideas).

The two sins that vitiate creations: unwitting traditional assumptions, and unwitting use of idea more complex than needed for effect.

You create by dropping traditions and exploring the simplest systems that are capable of uni-versal competition. To do that you have two things to overcome--a lifetime of assumptionsfrom tradition that blind us to simple program ways of thought and a lifetime of personalinvestment in idea tools more complex and often less powerful than simple programs.

Applying the new commonsense ushered in by simple programs:find old commonsense in how you view your present system, creative projectsubstitute new commonsense and review your present system, creative project

discovery--8 intuitionssimple programs--8 intuitionsa new math--8 intuitionsa new randomness--8 intuitionscontinuity overthrown--8 intuitionsnatural selection overthrown--8 intuitionsphysics (2nd law of thermodynamics) overthrown--8 intuitionsuniverse as simple program--8 intuitionsmind overthrown--8 intuitionsa new physical law--8 intuitionsa new limit to knowing--8 intuitionsfree: will, math, and applications--8 intuitions

The journey to creation always starts with self examination that becomes other revisioning bygetting inside a new framework for viewing self and world. It is a matter of two differentcommonsenses in conflict--an old one of continuous math models and a new one of discretecomputational models. To create you first have to examine yourself in 96 places for whereold commonsense still has sway over you. Liberation from that sway is your first step towardcreativity.

Basic exploration procedure:

model your project or phenomena as simple program (define analogs of “components”section below, do “select appearance” section below, and follow steps in “applying sim-ple programs” below)define all possible initial conditionsdefine all possible rule setsrun your model with all possible initial conditions, rule sets, combinations watching out-puts for:

which of elementary 256 behaviors resembles what you getrecurrence of all possible initial conditions as part of evolution process of some programsemergence of permanent boundaries segregating different kinds of system behaviorsrepetition, nesting, randomness, or mix of randomness with moving persistent interactinglocalized structures as final end-state the system evolves towardssigns of universality of your system via its achieving complexity of behavior in roughequivalence to rule 30, 110, or the 25 other universal most elementary cellular automatatypes

Creation building with simple programs:what are you trying to build/inventwhat transformations will you allow/invent (“machine of linked transformations”--therules of the automaton)what types of initial conditions will you input into those transformationswhat alternative sets of transformation will you try outwhat alternative sets of initial conditions will you try outchoose substrate for representing your automaton: computer cellular array of coloredcells, individual quantum states of electrons, individual molecules aligned in some way,etc, people organized into groups authorized to apply certain defined procedures, etc.)build your automaton so square colors map onto initial conditions, results, and allowedtransformationsrun your automatonscan results of your automaton for phenomena of interest

The journey to creation next requires you to envision an indirect new way of creating by set-ting up automata that generate myriad results you paw around among till something wonder-ful emerges. This is a decidedly more indirect way of creating than designing a wonder andexecuting directly your design. Many creators will dislike this indirection, resist it, counterit, oppose it, and deny it. That, however, will not reduce its true powers to create.

To create you have to imagine you and your process of creating as setting up simple programsand letting them generate myriad behaviors that you scan for “your creation”. You set up asimple program generation procedure and a simple program scan for “creations” procedure.Imagining this new indirect form of creating starts with a model of your project as a simpleprogram. That involves figuring out what should be initial conditions (the program), the rulesof transformation (the computer hardware), the layout of evolving results/inputs to next iter-ative application of the rules (a cellular grid, directionless network, etc.), and the point ofaction (what where when changes particular items in your layout. You then do somethingcomprehensive and unusual. You imagine all the possible initial conditions (programs) ofyour model. You then imagine all the possible rule sets (computer hardware types) of it.These two imaginings are by no means easy. Much that shallowly goes for creativity comesfrom steps like these. It is easy to imagine interesting initial conditions and rulesets but thatis a long way from realizing all the parameters that define all the possible such conditions andrulesets. Next you imagine running your simple program with all combinations of those ini-tial conditions and rulesets--what will you look for in their results? What might you discover?This too is an imagination of powerful not easy to do sorts.

Following the imagining stage is a stage of planning an actual simple program do embody yourrulesets and run your initial conditions “programs”. This involves deciding what substrateyou will actualize your simple program in--computer code on traditional personal computers,chemical messengers between human or invitro nerve cells, nano-technology arranged quan-tum “wells”, or the like. Some choices of substrate put you in a position where only a littlework is required to set up and run your simple program model. Other choices of substratemay require you to invent new technologies for placing things, reading things, connecting

things and take months or years to build. It is up to you--how important is choice of a newand different substrate to your ultimate purposes.

Components of simple programs:initial conditions = programrules of transformation/substitution = computer hardwarelayout = cellular grid, network of directionless connections, etc.point of action = all initial conditions at once, one predetermined point, one calculated point

Actually setting up your simple program, once you have decided what substrate to do thatupon, is rather straightforward. You define initial conditions, rules of transformation, layoutof initial conditions and intermediate results for iterative rule application to them, and thepoint of action you prefer.

Select appearance of simple program (simple program type):start with cellular automata model of your systemmove to other models by successively removing restrictions found in cellular automata:

cellular automatatotalistic cellular automata (remove single cells defining next row results)mobile automata (remove parallel influence at once of previous row so one active cellthat moves)generalized mobile automata (remove one active cell limitation, allow new active cell cre-ation for more than one)turing machine (remove two or limited possible “head” active cell states)substitution system (remove grid of cells entirely, so no fixed array of elements, numberof elements can grow)neighbor-independent substitution systems (subdivide boxes then substitute, removeneighbor effects)sequential substitution system (remove replacing each element with scan and find thensubstitute)tag system (remove scan instead remove fixed at left of row, based on its contents add toright end of row)cyclic tag system (remove not knowing which block will be added by “whenever Xremoved, add Y” rule)register machines (remove idealization, instead two registers and two operations, incre-ment and decrement-jump and programsymbolic systems (remove defined function operations, nested expressions of variablesthat can be any operation)network system (remove directionality)operator system (remove pre-defined transformation function)

choose way to layout results for visual obviousness of making whatever distinctions youwish to make among them

Next you choose the highly structured simple cellular automation type of simple program or,by removing restrictive elements of it, one of a number of more general simple programs (allcomputationally equivalent “universal” computation capable devices).

Applying simple programs:look for layers passing results to adjacent layerslook for a variety of phenomena that could be modeled as emergents from properties of underlying

substrate they all share and are generated fromlook for complicated layouts, rules of transformation, and points of action that add nothing by

their complication but can be transformed into simpler cellular automata, or other simple pro-gram, form

remember universal systems can emulate each other but obvious simple steps in one can become messy, long, tiresome, confusing steps in another so choosing which universal system to use is extremely powerful, practical, and a source of much creativity

Next you look for layers in what you are modeling that can be represented by evolving rows ofa simple program’s output/input. Or, you look for a variety of phenomena in what you wantto model that are now understood as distinct and separate but that might turn out to beemergent things from an underlying automaton substrate. Or, you look for complicated sys-tems of evolution and transformation, that may be logically equivalent to simple programs,but cluttered by all sorts of unneeded complexity and distracting features. Or, you look forwhich simple program types most efficiently and elegantly represents and does what youwant to model. You examination of these orients how you apply a simple program to modelsome particular invention or part of the world.

Analyze and use effects of simple programs:most existing constructs and relations represent emergent effects not generative ones, so remodel

as emergents of automaton programsmost continuity is discrete generative automaton effects averaged out via randomness so remodel

as automaton effects averaged out by automaton generated or imported randomnesslook for randomness assumed to be from environment but that actually could be inherently gener-

ated by the system you are building or analyzinglook for repetitive or nesting patterns handled by existing models with randomness and moving

localized persistent interacting structures mixed with randomness that are ignored or slighted by them

identify which of the 256 most elementary cellular automata programs the behaviors of your sys-tem most resemble

Next you actually run your simple program model and scan the emerging results for variousexpected things and surprises. You can expect that many existing constructs and relationswill in simple program automata results show up as emergent results of the automaton. Youcan expect that existing continuity will turn out in your simple program results to be theresult of randomness, inherent in the simple program averaging out initial condition differ-ences and rule set differences. You can expect randomness in existing theory thought to befrom changing environment conditions to turn out in your simple program results to be inher-ent in the program itself. YOu can expect for existing models before your simple programone, to capture repetitive and nested behavior but miss random and complex behavior (per-sisting mobile local structures that interact and mix with randomness). You can expect someof your simple programs results to closely resemble particular ones of the most elemental 256one-dimensional cellular automata types found by Wolfram.

Making Your Self, Your Child, Your Workgroup Creative

Some Personal Stories

Table 1: Recommendations from the Simple Programs Model of Creativity

Levels How tos

Makingyour selfcreative

It is really a lot to ask, that people drop 2500 years of work, results, habits, training,heros, and accumulated knowledge and instead look at the world afresh using a verynew recent “program” framework. It takes a great deal of thought work to extricateoneself from just one deeply buried assumption. Think about one deep bias or habityou freed yourself from in reality--how easy was it, how long did it take? To do allthat work not for one bias or habit but of 96 of them is a huge undertaking. It is thehurdle that you must cross in order to be creative in this chapter’s model.

Making yourself creative starts then with this challenge--undoing your own thinkingpatterns in 96 ways. If you get well along in doing that, you move immensely closerto being creative. AA, Alcoholics Anonymous, for example, gets people to view theirdrinking in a different framework. It requires application of that different view andframework. In a dozen rituals at each meeting it powerfully and non-optionallyforces that different framework around each drinker’s drinking. It is switching alle-giance to that framework that saves people, research has shown. By viewing theirpast histories and problems from the new framework they rename everything, reordereverything, reprioritize everything. They achieve hope at a cost of constant vigilanceand a new self identification. Becoming creative, according to this chapter’s model,is much like that. It involves switching allegiance to a new framework--from 2500years of continuity-obsessed maths to 50 years of discrete simple program models foreverything--mind, brain, thought, society, beauty, and the universe.

Makingyour childcreative

The wonderful thing about this chapter’s model of creativity is this--your child mayalready have the computer exposure and computer commonsense that enables him orher to master this simple programs modeling of everything must faster and more thor-oughly than you--if you can protect your child’s doing such simple program modelingfrom school systems, textbooks, and teachers thoroughly opposed to that viewpointand utterly untrained in it (and unsympathetic). That is the rub--you child is newenough not to be encumbered yet by years of propaganda for old continuous maths,but, by the same token, you child is socially embedded among people, books, andschools that are completely unaware though hostile to the simple programs view ofthe universe and everything in it.

You make your child creative by re-interpreting each daily piece of schooling andhomework from a simple programs perspective and by using the Mathematicaresources created by Wolfram for exploring the simple programs world at home, afterschool. If you are very very lucky, your school or some teacher in it will have foundWolfram’s book very approachable and its Mathematica notebooks very easy to use.Most of us will not be that lucky so we have to coach children ourselves, daily afterschool, in simple programs versions of the old frameworks schools push onto them.

Makingyour work-group cre-ative

Businesses, especially US ones, will eagerly hire Wolfram or his stand-ins and competewith each other to apply his simple program ideas to developing entirely new technol-ogies. So the world’s most competitive businesses--the ones competing with such USbusinesses--will be forced by competitive pressure, to master the simple programsmodel of creativity. It will be rammed down their throat by competitors using itunless they master it early enough themselves.

However, businesses typically miss revolutions, mistaking them for improvement cam-paigns of limited scope. To the extent the simple programs framework is a true revo-lution not an incremental improvement, it is likely businesses will be highlycircumspect about it and indeed work hard to assimilate it to their preferred ways ofseeing and doing things. This means individual managers and workgroups can makethemselves more creative than others around them by seeing such waterings down ofthe simple programs perspective, and personally avoiding them (possibly whileencouraging others to water down a lot, to think meanly of people).

Thirty five years ago I was exposed to a mass workshop event, wherein 40 different dailyworkgroups each followed procedures extracted from experts for achieving some result, shar-ing intermediate results daily. I had never before been in a meeting in which each of 8 peo-ple was assigned a particular mental transformation to apply to some data, and ordered inparticular linkage patterns to each other, then started to work with initial inputs to some ofthese 8 people. Many rebelled, hating not having the freedom to do thinking any way theywanted. It took quite some time before people settled down, stopped fighting the proce-dures, and realized that they were being exposed to an entirely new way for people to “be agroup” cognitively. The world’s best colleges, several of which I attended, never practicedsuch cognitively demanding procedures followed by large groups of people. When later Iwent to Deming Prize winner (for quality) companies in Japan and saw in quality functiondeployment, policy deployment, automation deployment, and research deployment cam-paigns, sophisticated processes deployed to thousands of workgroups across entire compa-nies, I realized such mass workshop procedures were capable of real businessaccomplishment--they were no toys.

In my own work I abstracted from the first mass workshop events, put on by that religiousgroup I joined right after undergrad school, and from my later exposure to procedure deploy-ment campaigns in Japan, the idea of the workforce as a social computer, with individualhumans or workgroups or events as C.P.U.s. (central processing units, like Intel Pentium chipsin personal computer in the early 2000s). In classes I taught I included two or three days persemester a social automaton process exercise where students defined initial condition (pro-grams) and ruleset (computer hardware) for they as people to play the roles of, producingautomaton results by row across the array of seats in any classroom. From these exercises,students got a number of intuitions--the difference between designed and emergent results,the difference between generative layer and emergent result layers, the difference betweennarrow and broad neighborhoods, the difference between sequential and parallel processing,and many others. This social automaton exercise trained them in the intuitions of the simpleprogram model of creativity, long before I had built such a model or read Wolfram’s book.

A little bit, at Xerox, and a lot, in some of my University of Chicago MBA classes, I createdsocial virtuality. That, I defined as, one group of people (my workgroups or my class mem-bers) organized a dozen different ways, for achieving a dozen different respective outputs,with daily or every-four-hours decisions about how to organize themselves next (by choosingone of our pre-arranged, trained, and practiced twelve patterns). In this way one groupcould be organized as if it were twelve entirely different groups, switching (multi-tasking wecalled it) among them as needed many times during any week. Strangely this way of workoverwhelmed us all so initial weeks were struggles with chaos, but within two months, ashabits and viewpoints changed, a tremendous uplift of productivity and morale improvementarose. We found ourselves at the end of a year of being thusly organized (for students takingtwo of my classes in succession at Univ. of Chicago) fully as productive as four or five groupsof the same size. By switching, using standard event procedures, several times a day, amongtwelve different organization forms for ourselves, we ended up doing four or more times thetotal work we normally would have fit into a week. This small miracle I called “social virtu-ality” one workgroup being organized as twelve “socially virtual” ones. It was this moreintense social form of work that I wanted to automate, not normal barely competitive exist-ing individualistic work forms. Social virtuality, thusly defined, can be mapped onto the sim-ple program model of creativity in a number of interesting ways. Firstly, it amounts to anautomaton where we switch among twelve different rulesets iteration by iteration (four hoursper iteration in most cases, sometimes one day per iteration). Secondly, it amounts to anautomaton where what is next to what changes for each iteration. Thirdly, it amounts to anautomaton where initial conditions shift in one of twelve different ways for every iteration.In addition to all that, it is a powerful way to switch people to the simple programs model ofcreativity viewpoint.

I am writing a novel these days, while finishing this book. Knowing that sex sells and havingan unusual sexual imagination, I have been populating passages of that manuscript with sex-ual social automaton arrangements. Men like to imagine themselves with a different womanassigned to different parts of their own male body and men like to imagine themselves multi-tasking among different parts of one woman’s body and among the same part of different

women’s bodies. In addition men like to imagine themselves and women together multi-tasking among different sexual activities and tools. Put these two together, with a bit ofstaged procedure and you get various sexual social automaton events, more structured that1970s free public sex clubs in Manhattan or Dusseldorf, yet more erotic because of that struc-turing. It is, I suppose, an erotic automaton--eros being often the first profitable applicationof many new technologies. Actually putting on and experiencing such sexual social automa-tons is an open frontier for future conquest.

If you look at global business society you see automata like cascades--huge long tree shapesof bureaucratic business organizations, extended horizontal networks of trading and invest-ment, upstart mobile persistent local venture structures, and the like. What enterprisesocial automaton generates the particular mix and ratios of such structures that we experi-ence as our actual current global economy? What does that social automaton say our eco-nomic future pattern will be? What class of automaton are we? This is an interestingdirection for research that greedy business school professors will be hot to trot for.

Some Suggestions for Application

Two steps of creating: drop traditions (the discipline of mental assumption scouring) and explore simplest universal systems (the discipline of adding minimal new ideas).

The two sins that vitiate creations: unwitting traditional assumptions, and unwitting use of idea more complex than needed for effect.

Applying the new commonsense ushered in by simple programs:find old commonsense in how you view your present system, creative projectsubstitute new commonsense and review your present system, creative project

discovery--8 intuitionssimple programs--8 intuitionsa new math--8 intuitionsa new randomness--8 intuitionscontinuity overthrown--8 intuitionsnatural selection overthrown--8 intuitionsphysics (2nd law of thermodynamics) overthrown--8 intuitionsuniverse as simple program--8 intuitionsmind overthrown--8 intuitionsa new physical law--8 intuitionsa new limit to knowing--8 intuitionsfree: will, math, and applications--8 intuitions

This chapter presents 96 changes in commonsense represented as “commonsense” state-ments and intuitions that counter those commonsense truths. The first step in creating iscleaning your mind of old mindsets and points of view. The 96 guidelines furnished heremake that process specific and something you can measure the quality of. 1. What is something you are now working at inventing or creating?2. Find in your process of making it examples of each of the 96 old commonsense positionslisted in this chapter.3. What would change in your process of creating if each of those 96 old positions werechanged to the new position?4. Find in the traits of what you create traces of each of the 96 old commonsense positionslisted in this chapter.5. What would change in what you create if each of the 96 old commonsense positions werechanged to a new position?

Imagine the Basic exploration procedure:model your project or phenomena as simple program (define analogs of “components”section below, do “select appearance” section below, and follow steps in “applying sim-ple programs” below)define all possible initial conditionsdefine all possible rule sets

run your model with all possible initial conditions, rule sets, combinations watching out-puts for:

which of elementary 256 behaviors resembles what you getrecurrence of all possible initial conditions as part of evolution process of some programsemergence of permanent boundaries segregating different kinds of system behaviorsrepetition, nesting, randomness, or mix of randomness with moving persistent interactinglocalized structures as final end-state the system evolves towardssigns of universality of your system via its achieving complexity of behavior in roughequivalence to rule 30, 110, or the 25 other universal most elementary cellular automatatypes

Substrate selection:what are you trying to build/inventwhat transformations will you allow/invent (“machine of linked transformations”--therules of the automaton)what types of initial conditions will you input into those transformationswhat alternative sets of transformation will you try outwhat alternative sets of initial conditions will you try outchoose substrate for representing your automaton: computer cellular array of coloredcells, individual quantum states of electrons, individual molecules aligned in some way,etc, people organized into groups authorized to apply certain defined procedures, etc.)build your automaton so square colors map onto initial conditions, results, and allowedtransformationsrun your automatonscan results of your automaton for phenomena of interest

The Simple Programs Model of Creativity suggests that creating is done indirectly by settingup simple programs that generate alternatives and scan them for points of interest. Thehuman role is exploring what gets produced and found by the simple programs. Simple pro-grams that your mind is were used by past creators while you and I today have the option ofusing computer or social simple programs or simple programs erected on other substrates--chemicals, nano-technology, and others. 1. What are you trying to create?2. What moves or transformation are you prepared to make to turn inputs into your final cre-ative output?3. What is the input to your creation process--inputs of all sorts?4. Define types of initial conditions you are willing to try to achieve something really creativein this project.5. Define types of transformation rulesets you are willing to try to achieve it.6. What automaton substrate should you use--social automaton, computer automaton, chem-ical automaton, mental automaton?7. Set up that automaton, run it, and observe its outputs, looking for what gets created.

Components of simple programs:initial conditions = programrules of transformation/substitution = computer hardwarelayout = cellular grid, network of directionless connections, etc.point of action = all initial conditions at once, one predetermined point, one calculated point

Simple programs are simple, that is why they are everywhere, inside us and outside us.Nature uses them because they are everywhere, that is, easy to find. We, however, have notused them consciously for thousands of years, because we lacked a concept of computation,because continuity-obsessed maths denigrated and avoided half of all system behaviors (ran-domness and complexity) which they were not well-tooled to handle. It is useful to definelots of familiar parts of life and work as simple program forms to undo some of the unfamiliar-ity of the concept.

1. Select twenty arbitrary parts of your life or work that interest you, or, select twenty worksof creation that impress and inspire you, whether done by you yourself or others. For each ofthem do the following:2. What initial conditions or program was involved in it?3. What rules of transformation or computer hardware was involved in it?4. What layout type did it use?

5. What point of action where rules were iteratively applied to some point or points in thelayout was used?

Select appearance of simple program (simple program type):start with cellular automata model of your systemmove to other models by successively removing restrictions found in cellular automata:

cellular automatatotalistic cellular automata (remove single cells defining next row results)mobile automata (remove parallel influence at once of previous row so one active cellthat moves)generalized mobile automata (remove one active cell limitation, allow new active cell cre-ation for more than one)turing machine (remove two or limited possible “head” active cell states)substitution system (remove grid of cells entirely, so no fixed array of elements, numberof elements can grow)neighbor-independent substitution systems (subdivide boxes then substitute, removeneighbor effects)sequential substitution system (remove replacing each element with scan and find thensubstitute)tag system (remove scan instead remove fixed at left of row, based on its contents add toright end of row)cyclic tag system (remove not knowing which block will be added by “whenever Xremoved, add Y” rule)register machines (remove idealization, instead two registers and two operations, incre-ment and decrement-jump and programsymbolic systems (remove defined function operations, nested expressions of variablesthat can be any operation)network system (remove directionality)operator system (remove pre-defined transformation function)

choose way to layout results for visual obviousness of making whatever distinctions youwish to make among them

Selecting which simple program to use is crucial. Even though all of them are universal com-putation systems capable of emulating any other computational system, some will do a par-ticular computation fast and in easy to visualize and influence form and other will do thesame computation very slowly and in hard to visualize form. It is like picking up grains ofsand with buckets or with toothpicks--both are pick up devices but one is a lot more effectivethan the other at picking up sand. The way you select a simple program type is by startingwith cellular automata because their form is the most well defined and restrictive. Byremoving particular assumptions you get the other, less defined and restrictive forms. 1. Select three different creative projects you are involved with.2. Select what aspects of each to model as simple programs.3. Roughly define the initial conditions, rulesets, layouts, and points of action for each interms of your creative projects.4. Start with cellular automata in the list above and represent your projects as that, then intotalistic cellular automata form, then in mobile automata form, then in generalized mobileautomata form, and so on till you have represented you project in all the forms above. 5. Which form is easiest and most natural to set up and run? Why?

Applying simple programs:look for layers passing results to adjacent layerslook for a variety of phenomena that could be modeled as emergents from properties of underlying

substrate they all share and are generated fromlook for complicated layouts, rules of transformation, and points of action that add nothing by

their complication but can be transformed into simpler cellular automata, or other simple pro-gram, form

remember universal systems can emulate each other but obvious simple steps in one can become messy, long, tiresome, confusing steps in another so choosing which universal system to use is extremely powerful, practical, and a source of much creativity

Simple program models are natural fits for the three situations in the list above. Look forlayers passing results to adjacent layers, look for apparent variety that could emerge fromunderlying automata dynamics, look for complicated procedures and forms that could be suf-fering from much unnecessary complication. These are guidelines for what in your creativeproject or situation to model as a simple program.1. Select three creative projects that interest or engage you now.

2. For each, what layers passing results to adjacent layers are present?3. For each, what apparent variety could really be emergent from underlying automatadynamics?4. For each, what unnecessary complication or detail is present that, when stripped away,might reveal an automaton-like bedrock underneath?5. Define automata types for each of your answers.

Analyze and use effects of simple programs:most existing constructs and relations represent emergent effects not generative ones, so remodel

as emergents of automaton programsmost continuity is discrete generative automaton effects averaged out via randomness so remodel

as automaton effects averaged out by automaton generated or imported randomnesslook for randomness assumed to be from environment but that actually could be inherently gener-

ated by the system you are building or analyzinglook for repetitive or nesting patterns handled by existing models with randomness and moving

localized persistent interacting structures mixed with randomness that are ignored or slighted by them

identify which of the 256 most elementary cellular automata programs the behaviors of your sys-tem most resemble

When you run your automaton in social, chemical, nanotechnology, computer, or other sub-strate form, you explore the results, looking for various things listed above. You can look forsuch things in your automaton results but you can also look for such things in real world exist-ing results as hints that automaton dynamics really generate them but go unrecognized tillnow. 1. Select creations that fascinate you and yet the origin of which is not clear.2. What constructs and relations in them may be emergent effects not generative ones andwhat automaton may generate them?3. What smooth behaviors are there and what automaton-generated randomness hidingunderlying discrete automaton dynamics may generate them?4. What randomness imported from changes in environment are there and what randomnessinherent in a generating automaton may actually be present but unrecognized?5. What patterns of behavior that are random or complex (persistent local moving interactinglocal structures mixed with randomness) are not handled by present forms of a system, whichinstead merely handles repetitive or nested behaviors?6. Which of the output patterns of the 256 elementary simple programs is present?7. Define the automaton that your answers to the above indicate, in detail.

Types of Creative Thought

Remove Assumed Continuity in Systems

We have been trained, educated, and brainwashed for 2500 years to model systems via con-tinuous smooth functions taken from ideal geometric forms. The smoothness we observe inbehaviors, relations, and things may be mostly apparent, caused by randomness inherent ingenerative automata that averages discrete causal effects making them look smooth. Byremoving continuity as an assumption and treating continuity as a illusion covering discreteautomata dynamics masked by automata-generated randomness we expose more actualdynamics controlling the world to our view and influence.

Examine Extremely Simple Programs as Possible Causes of Complex Behav-iors, Avoid Complex Causal Assumptions

We have been trained, educated, and brainwashed that math is complex for elites only thatnature is complex for elites only that only geniuses can understand truly our world. This is asick sick point of view and civilizational result, caused by an irresponsible detour that mathtook hundreds of years ago. If instead of participating in this radical complexity and elitismat the heart of Western culture and science we seek extremely simple causes of complexbehaviors we can confidently use the 256 elementary cellular automata types to spot howextremely simple systems can create behavior as complex as anything in our universe. Wecan save time and effort and broaden understanding by seeking causes in simple programs notelite complex continuity-obsessed traditional models.

Create Indirectly by Setting Up Simple Program Automatons that Explore Alternatives and Scan Them for Points of Creation

A new way of creating that turns over two major parts of the process to simple programsexists, though many traditionalists will resist it. In this indirect way of creating you set upsimple programs to generate alternatives and set up other simple programs to scan thoseresults from points of creation or interest. Your human role is then just setting these up andscanning yourself their final results.

Creation Event Designs

Social Automata Events

Over the years as I developed the models of creativity in this book, I have occasionallyinvented mass workshop events, based on them, wherein 20 to several hundred people comeup with 1 to dozens of inventions or discoveries in three or four days of work together, follow-ing procedures suggested by a creativity model now in this book. Below I explain a few over-all principles of such events and give basic procedures for the particular event type that usesthis chapter’s model of creativity. I cannot give exact detailed instructions for all roles andparts of the event set up, holding, and follow up, as space does not permit and that, in anycase, is the subject of a later book.

A major manufacturer in a high tech industry wanted to get its entire workforce more disci-plinedly inventive. People were being “creative” but only by shallowly coming up with alter-natives, seldom carrying them far enough or developing ideas deeply enough to reach realpossibility and consideration. The CEO wanted to change the tone from shallow “creative-ness” to serious investigative inventiveness throughout his workforce. To do that we decidedwe had to inject more serious modes of inventing into thought repertoires filled with general“lateral” “buy low sell high” “that’s neat” ways of thought. TRIZ heuristics were appropri-ate, but how to get 3,000 people to master them quickly and cost-effectively and apply themin a short period of time after mastering them?

I designed a social automaton event as follows. 480 people were organized as a social autom-aton of layers. Sitting in 12 rows of 40 people each with the last row passing results back tothe first row, rulesets were defined and applied iteratively by each row to initial conditionsor results from the adjacent row. Initial conditions were particular data about need or mar-ket or product or technical capability situations all of which might suggest new inventionspossible. Rulesets were TRIZ heuristics for turning existing means of doing something intonew inventions and another types of ruleset on the typical reasons and ways that new inven-tions fail in markets. We alternated rulesets, one row applying TRIZ heuristics, the nextapplying failure modes (changing ideas to prevent failing in that way), the next row againapplying TRIZ heuristics. We put a market bid dynamic between each such pair of rows sothat resulting ideas were announced orally and people on the next row bid on which idea theywanted. There are a number of other details that do not need to concern us here. In aneleven hour day, typically 100 rows of results were generated (6 minutes being the averagetime per row for processing its inputs with its assigned rules). In two days, 50 winning inven-tion ideas, fully qualified for testing were developed out of over 2500 invention ideas gener-ated and discarded (by the failure mode analysis in most cases). Now, over a decade later,44 of those 50 are in production and 42 of them are profitable (as of the last time I chatted uppeople from this event).

The Knowledge Evolution Models of Creativity

Idea Waves

SimplePrograms

CompileCycle

RelocateIdea

Ecosystems

DialecticFractalRecurrence

Creation

recontexting

spread

self contradiction

origin

result

Social Automata Replacing Brainstorms in Design:

As Example of “Designs No One Designs” Emerging as Monastic Innovations Changing Mundane Interfaces in a Computational Sociality Way

Abstract—What if entire civilizations “design” via massive dialogs that emerge as they evolve? What if, in some rare eras, the most mundane interfaces and media dissolve and return in new form? What if brainstorms in our era are being replaced by more “computational sociality” style procedures: social automata? This paper of overly huge scope is so because these questions are huge in scope. Three size scales of one large research project set are reported here: 1) data from media reports on revolutionary emerging changes producing a model of emerging Monastic Innovations, five new intellectual interfaces as “designs no person directly designs”, plus a model of what generates them, a dialog among five known types of computational system 2) from that same data, detailed specification of components of one of those five new interfaces, a replacement for brainstorms that fixes the two most significant flaws in usual brainstorms, Social Automata; 3) data from 2826 groups doing work normally done by brainstorms instead by new social automata procedures showing an initial mapping from types of social automata to types of task particular automata types excel at supporting; 4) from that same data on emerging Monastic Innovations, one of the five new intellectual interfaces—fractal page forms—a replacement for prose-text—found to be a powerful tool for designing the protocol component of Social Automata. OVERALL RESULTS: a dialog among five known types of computational system generating five specific monastic style innovations, each a new intellectual interface, one of them specified in detail, Social Automata, and 27 types of it mapped into 15 task types they were best at supporting, and another of the five intellectual interfaces, fractal page forms, used to design protocols of Social Automata.

Keywords: social automata, computational sociality, general empiric computation, monastic innovations, fractal page forms, rhythms of socialness, measures of socialness

STARTING SUMMARY—

Main Point Overall— If we take hundreds of comments on trends in one need area from dozens of sources as specs in a design process, not mere media noise, what design emerges? Are society and media somehow “designing” emerging new interfaces, devices, etc. without any one individual human doing the design? Can we treat media noise as a design process? Secondary Main Point---can a process that replaces “posting comments” in brainstorming with “composing one design after another

every 2 minutes” in social automata, inspired by and specified as above in the main point, fix the biggest flaws in brainstorming?

� LEVEL 1: Emergent monastic innovations from data—14000+ media expressions discourse analyzed� LEVEL 2:

generated by a dialog among five computational system types� LEVEL 3:

producing five new intellectual interfaces, � LEVEL 4:

one of which was social automata, replacing brainstorms

� LEVEL 5: another of which was fractal page forms, replacing prose-text� LEVEL 6: 27 types of

social automata mapped onto 15 types of task they excel at supporting—from data 2826 groups using social automata instead of brainstorms, forms filled after each use. � LEVEL 7: fractal page

forms enabling the formation of good protocols for social automata from data—2826 groups using social automata instead of brainstorms, forms filled after each use

This paper covers the above seven levels twice: first in the one large paragraph summary given below; second in seven sections of this paper covering each level in an introductory and somewhat cursory way. Some final sections present the data and results that generated the seven levels of this research.

Monastic innovations. Some innovations are big changes in such mundane fundamental unchanged-for-centuries parts of life that they seem to emerge from myriad societal interactions rather than be “thought up” or “designed/invented” by any one person. Prior research (Ulrich Oevemann, 1991) found, for example, banking emerging from simulated interactions of hunter-gatherers without any human “designing” or “inventing” banks. Prior research turned up whole society manias and enthusiasms that generated global communities vying to best each other in un-sticking whatever

the mania wanted improved—oil energy, speed, info connectedness in our own era, so attributing designs or inventions to single individuals looks like a media mania for hero stories rather than an accurate model of design and invention (Lienhart, 2000). Prior research (Greene, 2013) turned up 100 distinct models of innovation, one of which, Monastic Innovation, concerned such substantive changes in extremely often used mundane interfaces, with lasting history-scale impact, such as invention twice of the job, once by Benedictines in Spain (Nigg, 1959) centuries ago and again, in different form, by Rinzai Zen monasteries in Japan (Colcutt, 1996), in their Muromachi era. In our era, the computational devices and systems humans generate are causing individual and social processes to become more computational. Question. Are the simultaneous immense changes of our era—the web, globalization, Asia's emergence, global warming, global finance disasters—leading to, designing-by-emergence, as it were, Monastic Innovations that are replacing traditional media in especially computational ways?—if so which particular ones? Data. 14,000+ expressions of emergent changes in ends and means (expressions of frustration, opportunity, investment, ridiculousness, violations of commonsense) from 20 print-web-broadcast media reports each sampled at 20 times in one year, were analyzed with discourse analysis to produce candidate Monastic Innovations in our time—the top five being five intellectual interfaces. Result one, a dialog generating new computational systems and devices. A dialog among five extant types of computational system was found: machines, biologies (earthly and invented new), societies, brains, minds. Spotting new computational systems in one of them leads to inventing/designing new ones in others of them, which in turn leads to spotting/making still others. Result two, five new intellectual interfaces. Scientific rules of order replacing meetings, stratified responding replacing discussions, mass workshop events replacing work processes and classes, fractal page forms replacing prose-text, and social automata replacing brainstorms were found. The latter, social automata, involved groups generating every 2 minutes or so one new design after another, using a weave of various expert design protocols, that weave, mutate, cross-over, and genertically evolve during use. Question. What can social automata do that brainstorms failed to do? What fixes the two biggest known flaws in brainstorms—too early focus of topics

popular with a group faction and ideas passing each other in the night rather than interacting? Data. Forms filled in by 2826 groups using social automata instead of usual brainstorms over a period of years were analyzed for various measure of good outcome and which types of social automata they used. Automata types corresponding to extremely good outcomes or to very frequent good outcomes were noted and reported here. Result three, a table of 27 automata types and the types of task each achieves extremely good levels of support for. The social automata types are tentative and partial, only three parameters define them. The task types combine five different systems each with three types for a total of fifteen. Result four, a list of the most frequent fill in comments about virtues of social automata when compared to brainstorms. The fill in data strongly supported social automata as correctives of the two biggest known flaws with brainstorms—too early focus, and ideas passing each other in the night without interacting. Question. How do we come up with good comprehensive, yet detailed enough to be practical, protocols for social automata to use? Data. The same forms filled in by 2826 groups using social automata of various types over a period of years in several colleges and consulting assignments used above, were used here to capture what tools helped designing good social automata protocols. Result five, another of the 5 intellectual tools, fractal page forms, as a replacement for prose-text. The comprehensive topic coverage, drill down multiple fractal levels, lowest level great detail, and well ordered nature of fractal page forms allowed groups to invent powerful protocols that specified lots of diverse ways and approaches to tasks that social automata do.

LEVEL ONE—EMERGENT

MONASTIC INNOVATIONS

Many of us have the same intuition—that our era is going through a very fundamental change. We look back through history where others did so and identify with them, both with their problems and with their solutions. Many of us have another intuition we share with others—that many parts of how people both individually and in groups, think and act, are becoming computational in any of various senses. We feel a rebound from ways of the digital systems we buy becoming ways we stylize our thinking and acting. Many of us have a third shared intuition—that new ways of thinking and acting,

individually and in groups, are proliferating rather than just changing. We feel menus of choice where in the past we had one favored or required way for things. What pressures are building or emerging in the most fundamental aspects of us—so as to constitute Monastic Innovation in our era? What such innovations are those forces causing to emerge? Are really powerful fundamental new “designs” emerging in ways human inappropriately and not entirely honestly take personal credit for?

Data in the form of 14,000 expressions from 60 media channels, was subjected to unusual discourse analysis methods. Categories from the expressions were carefully ordered and named. When grouped thusly, various frustration, change, opportunity expressions in media constituted the specification of emerging inventions and innovations. Were this collection of items treated as a crowd spec of an invention instead of as chaotic disjointed random personal responses—would true new designs be thusly constituted? (Can unconscious undeliberate crowd designs become and be fully inventive powerful designs?)

An example is given here below, in the form of sample expressions, with interface implications specified, united at the end into one new interface they all end up pointing towards. Examples: 1) Webpages are designed rather than written—result: kids are used to designing pages more than merely writing prose. 2) SMS text messaging has replaced writing pages composed by single individuals with mental models shared by all minds in a group after dozens of small messages sent among them—result: interaction replaces symbol structures. 3) Smartphones researching anything any time in the web—result: editing whether to list a source or list the idea gotten from the source. 4) The vastness of information available to everyone makes education and intelligence into choice of the best parts of that immensity—result: intelligence becomes where you search and focus not what you “know”--smartness is your mental directions not your mental stores. 5) Nearly all the inputs to the web are bushy, unordered, countered by every more sophisticated search engine algorithms—eventually tools for making inputs more regular, and for regularized maps of web contents will emerge. Summary of above points: a) Design pages not write prose b) Interactions replace structure c) Choosing to cite the idea or provide the link d) Intelligence as mental direction not mental stores e) Bushy

inputs replaced by regularized input and web = IMPLICATION—I) regularized diagram pages replace prose. II = fractal page forms with regular: branch factor, point order, point naming. The above is an example of how the Fractal Page Form replacing prose-text new intellectual interface was derived from expressions categorized and taken as interface specs rather than as scattered insights. All the five new intellectual interfaces were derived similarly.

LEVEL TWO—DISCOVERY OF A

DIALOG AMONG 5

COMPUTATIONAL SYSTEM

TYPES THAT GENERATES NEW

INTERFACES

A pattern appeared among the expression categories. DNA mechanisms underneath evolution gave rise to genetic algorithm versions of them developed in machine computers. Genetic algorithms on machines morphed into expanded, extended, twisted, invented new versions of genetic dynamics, that revealed hithertofore unseen aspects of biologic evolution. Some parameters invented in machine simulations of artificial natural selection systems, turned out to exist in natural real natural selection, uncovered when specifically searched for.

This is an example of a dialog between two computational system types—biologic computation system generating a corresponding machine computation system, in turn, revealing new aspects of the original biologic computation system.

Dialogs between five fundamental computational system types were found: biologic, machine, society, brain, and mind (tools outside brains that make minds smart). This dialog generates hundreds of distinct new technologies and makes parts of society and personal mentality more and more computational. The first inventions of each dialog enter the dialog, proliferating it, spawning, fractally, layers of further dialogically invented designs.

Machine computers cause us to spot similar computational go-ings-on in biologic systems, which, when modeled, turn out more sophisticated than we ever dreamed of, inspiring us to invent still newer machine computers, which in turn, help us spot similar computational goings-on in societies, which in turn turns into iWatches which......

LEVEL THREE—DISCOVERY OF 5

EMERGING NEW INTERFACES

The result of the General Empiric Computation Dialog discovered above was five new intellectual interfaces, as emerging, possibly Monastic, innovations in our time. No one article, analyzed, directly named or stated the components and full functions of each of the below, but each of the below was defined by a convergence of a series of articles over a year in

various sources and media. Social automata below as replacements for brainstorms were specified by entirely separate sources, media, and remarks such as the following (groups develop particular favored procedures for handling issue types over time, people learn to recognize cognitive roles each other are best at and call for each other to play those roles they excel at, groups together long times hold design discussions where they together design what issues they want to face and how they want to handle them, and many similar others).

Remember, this research took separate such scattered remarks as specifying one particular new invention or design.

• meetings replaced with scientific rules of order--(meetings where everyone leads group chosen treatments assigned to group chosen topics to produce group specified outputs within group designated time periods—the group designs such an agenda first, then assigns roles, who leads applying what treatment to what topic, in what order, the executes that deisgn)

• discussions replaced with stratified respondings--(instead of people telling each other the different points or meanings they detect in a shared experience or encounter, people share first what they each noticed, then what they each felt at each thing noticed, then how they

each segmented those noticings, then what each segment caused each of them to recall, then how they each interpreted the “meaning” of each segment, then the meaning of the whole)

•classes/work-processes replaced with mass workshop events--(large scale social automata, that is, the computational arrangement and operatings of a group people in many in-parallel workshops whose partial products from intense days of work combine into powerful overall results.)

•brainstorms replaced with social automata--(small scale social automata are computational treatments of people by each other—arranging people in arrays, operations assigned to roles in

the arrays for people to apply to partial products passed to them by adjacent roles in the array, iteratively, at short regular time intervals—the whole six or ten step automaton collecting completed designs, decisions, and the like, each the result of six to ten different operations applied by roles in the automaton)

•prose-text replaced with fractal page forms--(hierarchic arrays of points well named, with a sentence or two per point,

Figure 1: Dialog among 5 Computational System Types

highly regularized with the same branch factor within and across levels, the same ordering of point within and across levels, standard formats for point names within levels and across levels).

LEVEL FOUR—SOCIAL AUTOMATA

REPLACING BRAINSTORMSSocial automata, derived as stated above (see

Wolfram, 2002 for a central inspiration for social automata), from expression data, were turned into a practical method then practiced by numerous groups over a period of years. They were found to fix the two biggest flaws in usual brainstorms, that is, they make groups not focus too early on first popular-with-a-faction ideas by continuously generating multi-step designs (typically 40 or more per hour) and they make ideas interact by getting people to apply mental operators to ideas given from others nearby.

Social automata are defined as: 1) An array of roles (done by individual people or groups or small consultation events on the web). 2) A path of partial results among roles (can change based on judgments by particular roles or units of time or evaluations of final results). 3) A protocol of mental/social operations to be successively or in parallel applied to results from prior protocol steps to produce some final outcome (design, judgment, evaluation, decision, estimate, prediction, critique, composition, performance, etc.)-- the protocol can change during execution of an automaton based on the same criteria allowing path changes. 4) An iteration interval of time in which all or some types of protocol steps must be completed, with partial results handed to next roles in the path (the iteration interval can change during execution of an automaton based on the same criteria allowing path changes). 5) Some whole automaton operators (cross-over of some roles within an automaton to later or earlier slots, cross-over of some roles from one automaton having one protocol to another automaton having the same or a different protocol, reverse doing of a protocol so the last role does the first role and vice versa (the person wrapping up final results suddenly has to generate initial seed ideas and operations etc.). [These roughly correspond to Wolfram's one dimensional cellular automaton elements: rules, array of elements, states elements can be in, initial states distributed across initial array elements laid out, iteration interval]

LEVEL FIVE—FRACTAL PAGE

FORMS, ANOTHER NEW

INTERFACE EMERGING

Another of the five new intellectual interfaces derived from the expression data was Fractal Page Forms replacing prose text. Some of the expressions that led to this interface design emerging from categorization work were presented above. Just as social automata were derived from the same data as another of the five new intellectual interfaces, the expressions made very clear that the days of prose text were numbered. The dialog among 5 computational system types that spawns myriad new computational sociality technologies in our era, mentioned above, works like this—computer people expert at human interface design comment on old and new media, mentioning particular ways that prose fails as an interface—it is so difficult that people who find points in it get into top colleges like Harvard, it is a linear stream encoding of visually simultaneous and in parallel ideas, it is an interface that hides the contents it purports to be presenting, and others). In this way computer people dialog with writers till, from their dialog emerges a replacement for prose satisfying simultaneously computer interface critiques of prose and writer wanted values in it to be preserved.

Hundreds of expressions referred to various

numerous widespread forces all pushing us towards an interface-for-writing that shows its point contents visually rather than hiding its point contents in coded strings of symbols.

Visually self evident count—how many points, naming—what are the names of each point, order—what principle orders the points in the text—is emerging in messy bushy webpage form, facebook photo comment form, and other media.

However, the expression data pushed this one or two steps farther along—the future is a vastly less bushy more regular version of visual point-self-evidence: highly regular geometries of points. Below are Fractal Page Forms, one in Chinese and the other in English, the former of the names of 60 models of creativity and the latter of the 64 capabilities of high performance teams. Later, in this paper, a connection between Fractal Page Forms and Social

Automata is established.

LEVELSIX—MAPPING OF 27

SOCIAL AUTOMATA TYPES

ONTO 15 TASK TYPES

The five new intellectual interfaces derived from the expression data, were each turned into methods and applied by numerous groups. Size limits on this article prevent dealing with details of any but one of the five, and the one chosen for this article is Social Automata, which

involves, in a subsidiary way, another of the five, Fractal Page Forms.

Social Automata are rather abstract representations of ways for people and their minds to be together, and specify millions if not quadrillions of particular ways to organize people and their work. This immense space is an initial block to productive use of social automata. Therefore, initial groups using social automata as a method, were given a form to fill in to measure all aspects of both the process of doing them and the quality of results they produced.

Data from 2826 groups using social automata instead of brainstorms, produced general patterns of types of automata that excelled in supporting certain types of task: 1) Competitive versus cooperative automata—competitive are best for norm-violation tasks, cooperative automata best for tipping point finding tasks; 2) Homogeneous versus diverse automata—homogeneous automata best for editing and detail development tasks, diverse automata best for departure from past practice and liberation from past norms tasks; 3) Serial versus parallel automata---serial automata best for layered, additive step by step innovatings, parallel automata best for winner-take-all of fusion-of-all multi-viewpoint innovations. More comprehensive and detailed mapping of 27 types of social automata to 15 task types is given later in this paper.

LEVEL SEVEN—AUTOMATA

PROTOCOLS DERIVED FROM

FRACTAL PAGE FORMS

Early in the derivation of the five new intellectual interfaces strong relations among them due to overlap of the forces that produced them all, was noticed. Two of them had especially strong inter-relation—developing good protocols for social automata demanded comprehensive, yet very detailed, balanced, non-biased, every-viewpoint-encompassed models of an immense variety of aspects of the world.

Some social automata needed various very different types of social relationship, some needed touch-screen function types of smartphones, others needed distinct stages of social learning or social revolution, still others needed abstract functions that all societies and

Figure 2: Fractal Page Form of 60 Models of Creativity in Chinese

Figure 3: A Fractal Page Form of 64 Traits of High Performance Teams

social entities within societies (social clubs, ball teams, colleges, persons, etc.) shared. Such models needed to be hierarchic so newcomers to them could, by drilling down from few top general-level categories context and understand detailed lower level items. Such models needed balance as in depth drilling treatment of any category needed to be possible so automaton protocols would not slight or favor one alternative for reason of flaws in tools not flaws in thought. At the same time as this need aspect of social automata protocols was emerging, Fractal Page Forms were being developed and applied by other groups, using them as a method of work. Almost instantly social automata groups borrowed Fractal Page Forms from the other group, to use as a basis for protocols they designed for social automata. The highly regularized formats of Fractal Page Forms, the drill down and drill up levels (each sharing the same ordering and branch factor as other levels), the comprehensiveness of them, the diversity captured by their categories—all these made building social automaton protocols faster, easier, and produced more powerful protocols. For example, using the Fractal Page Form in this article on 64 Traits of High Performance Teams—some social automata assigned “bad boy” traits to one automaton role, “good girl” ones to another role, and so on.

Now consider Greene's 60 models of creativity (Greene, 2006), Fiske's (Fiske, 1993) four types of human relating—communal sharing, mutual reciprocity, hierarchic ranking, market pricing—and Arendt's (Arendt, 2006) social revolution steps—liberation, founding freedom, historic dream, and conserving novelty—and Rollo May's (May, 1998) stages of power development—negative, assertive, partnering, transformative power—and hundreds of other theories that diversify and specify what there is to notice in our lives and work, that direct us to the high leverage spots in the onrush of general perception: these turn out to be excellent as the cores of social automata protocols. Protocols with diverse factors in them, with very different frameworks in them, with more choice of frameworks and items, with balanced development of all choices and alternatives—were more powerful and less biased than others.

THE TWO DATASETS (MEDIA

EXPRESSIONS FOR FINDING

INNOVATIONS AND FORMS

AUTOMATA FILLED IN FOR

FINDING TASKWORTHYNESS)

In our web era we suffer from too many sources of information with not enough expert aggregation and editing of it, formulating from it, clear implications and results. The same means that create this problem offer a kind of crowd-source solution—using the same multitudes who create stuff on the web to aggregate, categorize, edit, and formulate it into useful results. This polarity resembles the messy inputs into the web, handled by ever more arduous search algorithms.

So with all of us wondering—what does all this burgeoning change portend?--we might answer that collectively in one of two ways (or both): 1) crowdsource way: get a crowd to aggregate, edit, and form many diverse changes into focused useful results---many handlers of many sources 2) sourcecrowd way: get one or a few people to aggregate a huge variety of results, edit, and form them into useful patterns---single “expert” handler of fewer sources.

For many prediction tasks, the crowdsource beats the sourcecrowd. For other tasks vice versa. Since the task of turning vast changes and publishings on change implications into coherent patterns is not a prediction task, this research chose a sourcecrowd approach, one expert person or group gathering and aggregating, categorizing, abstracting patterns, then aggregating such patterns into a coherent useful model. Summary: 1) 20 web sites, 20 print media, 20 broadcast programs (for which transcripts were available); each sampled on 20 different days of the past year 2012. 2) 1200 items/articles/programs with expressions in them marked to show emerging new types of: problem, opportunity, investment (long term only), ridiculous ends, ridiculous means, violations; 14,000 total expression markings categorized by similarity of ends, and categorized again by similarity of means; 2,800 categories grouped by 50 abstract interfaces they involved 3) 50 resulting interface categories ranked by revolutionary-ness of changes emerging for them (3 groups did this ranking, with inter-rater agreement greater than 88%; non-agreed items were carefully renamed and afterwards-ranking had 97% inter-rater

agreement, with un-agreed items debated then slotted). 4) RESULT: 25 final interfaces of which 5 were most revolutionarily changing, ways of: meeting, discussing, processing/classes, brainstorming, prose-text (reading/writing). Other papers will handle the remaining 20 slightly less revolutionarily-changing interface types.

The results, presented earlier, were: 1) large scale—the General Empirical Computation model of a dialog among 5 types of computational systems that generates many of our new media and info devices and systems including five meso scale items below; 2) meso scale—five particular Monastic Innovations—replacements for meetings, for discussions, for classes and processes, for brainstorms, and for prose-text; 3) micro scale—social automata (or social cellular automata), arrangements of people interacting with other people as if all were processors in an array passing messages and partial products among each other and each applying pre-specified operations to partial products handed to it.

Implications of the above three scales are: 1) they help us spot the computational nature of all sorts of aspects of work and life not seen so before 2) they provide a deeply abstract new level for modeling familiar systems and phenomena 3) they inspire innovation and invention that enhances or builds on and expands certain computational aspects of phenomena.

The Sample: Keio University—64 social automata, Kangaku University---202 social automata, Beijing University---8 social automata, University of Chicago---48 social automata; consults at global Fortune 500 companies--- 116 social automata---equals 438 automata of 6 people each.

Total number of participants (forms filled): 2628

Participant age: mean 22 range 18 to 71Participant years of college education: mean 4

range 0 to 14Participant gender: 51% female 49% maleParticipant major/skill-area:

arts humanities 12% business 41% sciences 8% engineering 19%, professions 20%

The Instrument Items: Sample items from the form filled in by 2826 social automata users are given here:

Circle as many items below as describe accurately all or major parts of the task your social automata just completed:Labor---repetitive doing

of rote tasks requiring little thought

Work---fashioning something into what you imagined/plan

Action---setting loose revolutionary changes among people with unforeseeable consequences

Decide---investigating alternatives/contexts then choosing

Design---combining & inventing wonderful renditions

Develop---working the details that turn ideas into realities

Think---making arrangements of ideas or new ideas

Present---conveying organized points to particular audience

Listen\hear---discern meanings under what/how others say

Personal---face to face deliberate encountering of people

Asynch media---leaving messages/documents for others

Live media---electronically together though physically apart

Economic---reconciling supply and demand differences in any part of society or social entity

Political---adjudicating human plurality of view, value, aim in any part of society or social entity

Cultural/value---escaping mere purpose to create life value in any part of society or social entity

Social change---undoing and redoing social powers and arrangements around you and others in any part of society or social entity.

If you had done the same task with the same group of people but instead of a social automata arrangement being used , your normal ways of working such tasks had been used, what would be different and how---circle one of each choice below, and beside that answer indicate why that difference appeared:

Speed: slower or fasterQuality: lower quality outcome or higher

quality outcomeFun: less fun to do or more fun to doInterest: less interesting to do or more

interesting to doIdea relations: less idea interaction or more

idea interactionIdea depth: shallower ideas made or deeper

ideas madeSocial relations: less social interaction or

more interaction and others.

Data Analysis: There are several problems in analyzing the above data for what social automata are “good” for what types of task: 1) college students are not representative samples of work; 2) usual Hawthorne effects of greater attention, motive, and social supports for newly introduced routines whether good or bad; 3) well mastered “normalized” routines at work

cannot be compared in power or effectiveness with new largely unpracticed routines not embedded in competitive real organization environments; 4) criteria of what is “effective” and “best” evolve so what is viewed “best” at the end of a social automata experience or two is not the same as what was viewed “best” a year or more before using normal routines in normal work; 5) social automata reported in the above data as “best” for some type of task, and reported moderate, and reported worst for it, may be so due to different mechanisms in those doing the automata or in subtle factors of how social automata were introduced, done, or viewed. For the above reasons, usual correlational analysis was shunned—though may top journals accept such work, it is, for the above four reasons, invalid. Instead the data were used to seek patterns. Results of two patterns were fused: 1) extremely good outcomes extreme levels of good outcome (for each task type) came from which social automata types--Outcomes 2 standard deviations above the mean, and the social automata types that generated those outcomes, were counted for this measure; 2) good outcomes extremely frequently extremely many moderate to extreme good outcomes (for each task type) came from which social automata types--Automata that 60% or more users of reported moderate or good outcomes from where counted for this measure.

For each type of social automata in the RESULTS TABLE--Outliers, the type of task that it met either or both of the above outcome criteria of “bestness” on was put in its table slot. That task type reached a good outcome by either or both of the above measures when done by the social automaton type its location indicates (rows and columns). Where two or more task types met that criteria, the highest scoring one either or both of the above two criteria was inserted. This means the RESULTS TABLE--Outliers captures social automata that: GREATLY improve performance of the task types marked (at least 20% of the times used) or that moderately improve NEARLY ALL performances of the task types marked (at least 60% of the times used) No claim to validity, reliability, or causality is made in this paper for the RESULTS TABLE--Outliers entries. They are suggestive patterns from a large but unrepresentative sample.

DERIVATION OF SOCIAL

AUTOMATA METHODS FROM

EXPRESSION DATA

One of the five most revolutionarily-changing interface types was the brainstorm process. 300 individual expressions that generated this interface super-category out of several change categories, were then used to define—what specific changes were emerging in how people brainstormed as the web, the 2008 financial crisis, global warming, etc. percolated through global societies. The following elements were thusly derived: 1) person to person TO processor to processor person to person ways of work were becoming somewhat

algorithmic, influenced by the software algorithms of work systems; 2) micro-level organization the undisciplined, happenstance, personalist, emotive ways of work at the bottom of organizations were being invaded by more task to task ways from how higher up groups worked and inter-related; 3) doer units unspecified tasks could be sent to email addresses or sites on the web and there, who knows who handles them, then results sent to next addresses and sites, making processes more algorithmic and who does them more diverse and undetermined; 4) way repertoire competence each person doing what they usually do in the way they usually do it was becoming more and more recognized as an obstacle to work and effectiveness and there were forces for getting people to master standard repertoires of mental, social, emotive, relational, imaginative work functions; 5) meta-ness work and thought and creation were becoming more and more meta-, that is, reflective—people

watching how they X and adjusting ways of Xing in process as they work; 6) bigger way repertoires better work and thought done by people with more ways, more diverse ways, better ordered and accessible ways was often more creative, more effective, more educative, more attractive than work and thought done by people with fewer, less diverse, unordered, inaccessible ways---repertoire of ways scale matters.

These main elements were refined by another 20 detailed ones to define something named, by the author of this paper: “Social Automata” and presented both above and below in this paper.

Social Automata Types: There are myriad possible arrangements of a few people in social automata arrays, but the most heavily used ones,

thus far, are given in the table below. These are arrangements of SEVEN people, with some protocol of procedures spread over them in some simple or complex way, so each person or pair/trio “role” applies some assigned operation to partial products handed it by neighboring roles. LINES---invites sequential transformation processes; TRIANGLES---invites sequential doing of 3 or more distinct subprocesses; CIRCLES---invites repetitive refinement, recursive scaling up and down, of the same operators; FIGURE 7s---invites short different approaches or procedures regularly intersecting and handing off partial results to each other.

Initial Common Multiple Automata Array Types

For example in simple automata there may be one or two testing or editing steps, and quickly people realize there are four or six diverse types of editing needed for great results, so they add an entire social automaton dedicated to diverse editings to design or decision or production automata. Below are suggestive partial initial types: A) 1 into 2---elaborate or variously treat partial results B) 2 into 1---competing or diverse alternatives fused or selected from C) 2 into 3---a fused product of diverse sources or ways itself applied in diverse directions D) 3 into 2---diverse sources fused then treated in two diverse ways or places E) 0 into 8---specially fecund designs or products variously directed and applied F) 8 into 0---extremely various capabilities or needs fused into dense intricate powerful single capabilities.

TWO RESULTS—SOCIAL

AUTOMATA VERSUS BRAINSTORMS

AND MAPPING AUTOMATA TYPES

ONTO TASK TYPES

Results One—Social Automata versus Brainstorms: The second research question was what fixes the two biggest flaws of brainstorms. The forms filled in by the 2826 social automaton participants, provided good data of their impressions about social automata versus brainstorming. This data are not especially solid because these participants were well practiced in brainstorming and new to using social automata. Below, the most frequent fill in comments about what was “best” about social automata as these students and clients, all new to social automata use, experienced it, are summarized: 1) deeper development of ideas/alternatives rather complete results of multi-step transformation process rather than more slightly shallowly developed brainstorm results—listed first by 740 of 2628, listed at all by 2601 of 2628; 2) more ideas developed per unit time many more completed multi-step designs and creations generated per unit thim where brainstorms frequently hunker down to two or three quickly liked alternatives (40 to 150 alternatives designed rather than 3 or 4 by brainstorms in the same time period)--listed first by 601 of 2628, listed at all by 2291 of 2628; 3) idea forced to interact not pass each other in the night forced our ideas to use and apply ideas of others instead of merely looking at and dismissing ideas of others as in usual brainstorms—listed first by 577 of 2628, listed at all by 1892 of 2628; 4) sophisticated procedures used able to use some rather sophisticated steps/procedures compared to freeform casual mental operators found in usual brainstorms—listed first by 1373 of 2628, listed at all by 1800 of 2628; 5) generating from blank slate forced new views/thoughts the initial seed-idea first roles in each automata rapidly ran out of obvious quick variants to suggest forcing invention from blank slate minds, which meant new views and changes in framework achieved compared to frameworks left the same by usual brainstorms—listed first by 1113 of 2628, listed at all by 1194 of 2628; 6) reversing roles across person arrays allowed results experienced to redirect initial seeding the occasional reversing of roles assigned to person/pairs meant that the last guy who wrote up final results became suddenly the

Figure 4: 14 of Billions of Social Automata Array Types

first guy generating initial seed alternatives and variants—that allowed useful redirecting to what prior results had missed—listed first by 85 of 2628, listed at all by 1100 of 2628.

The two common brainstorm flaws were—too early focus on ideas popular with loud factions in a group, and ideas passing each other in the night not really interacting. 1 and 2 above address that first flaw and 3 addresses the second flaw. A majority of initial users of social automata found they excelled brainstorms in the above 6 ways and a large marjority found the fixed the two flaws of brainstorms listed above.

Results Two—Types of Social Automata versus Types of Task: Within the broad specification of Social Automata derived as stated above, were many myriad variations imaginable. Not only did Social Automata suggest new more computer, software, algorithmic, and machine-like ways for people to be arranged and pass partial results among each other to get tasks done, but the refined version of the elements of them derived above formed a cogent and powerful grammar to express all known extant forms of work while suggesting a huge frontier of new possible ones yet to be explored.

The space of possible social automata types to be explored is immense so no one has scientifically mapped even a tiny fraction of it thus far. This paper reports merely an initial foray or two to see in the simplest first approximation way what types of social automata might be distinguished and how they map onto types of task to tell us which automata types handle which task types the best, whatever “best” might mean in this context.

RESULTS TABLE—OutliersThe 27 Social Automaton Types Below

Produced Best Results and/or Most Frequent Good Results for the 15 Task Types

Indicated BelowWITHIN or BETWEEN AutomataThere can be competitive or cooperative steps within automata or among a group of automata; serial or parallel steps similarly; fixed or switched steps similarly.

CompetitiveRoles can compete with other roles doing same protocol step, automata can also compete.

CooperativeRoles can feed into each other cooper-atively as can whole auto-mata feeding into other automata.

Edit-iveRole can edit and improve results from other roles or make their own results. So can automata edit other auto-mata.

Serialafter

Fixed protocoloperations assigned to

Labor, Work, Action

one task a different one

roles do not change during automaton operation

Switched protocoloperations assigned to role change at times or other conditions during operation

Decide Design Develop

Evolved protocoloperations assigned to roles are edited, improved, invented during operation or prior protocol steps.

Think Present Listen/hear

Parallelsame task done by several roles at same time

Fixed protocol Personal Asynch Media

Live Media

Switched protocol Labor Work Work

Evolved protocol Action Action Action

Shapedsame task done by different roles at different times

Fixed protocol Economic

Economic

Economic

Switched protocol Political Political Political

Evolved protocol Culture Social change

Social change

TASK TYPES

Labor, work, action

Decide, design, develop

Think, present, listen

Personal, asynch media, live media

Economic, political, culture-value social change

BEST-NESS MEASURES

1. Satis-faction of doer

2. Satisfaction of customer of task output

3. Efficiency as more output value per input cost

4. Effective-ness as outputs delivered where have biggest impact

5. Creativity as got more and better from inputs than ever achieved in the past.

The above table contents were obtained from written forms filled in by students and consulting clients immediately after doing work in social automata formats. Grades by others on the quality of the work done (a grade for each of the “best-ness” five types in the table given to each automaton output) were combined with participant categorizing of task types involved in their work and grades they gave themselves on the five best-ness types. Fill in remarks from all participants provided some mechanism information on why certain social automata types fit best some task types, but these fill ins were voluntary, spotty, somewhat inconsistent, and from people not well practiced at doing work of any major work significance or volume in social automata format.

DISCUSSION

Future research involves longitudinal data in power and impact of each of the five new intellectual tools, plus expanding to research on the 14+ other new emerging algorithmic tools

spawned by the dialog among five known computational system types. As we observe social phenomena taking on a computational algorithmic cast, viewing what dialog between what extant computational entities spawned it, will enable, perhaps some predictive power.

REFERENCES

[1] Oevermann, Ulrich (1991): “Genetischer Strukturalismus und das sozialwissenschaftliche Problem der Erklärung der Entstehung des Neuen.” in: Stefan Müller-Doohm (Hrsg.), Jenseits der Utopie. Theoriekritik der Gegenwart., edition Suhrkamp. Frankfurt am Main: Suhrkamp, Pp. 267–336,

[2] Lienhard, 2006, How Invention Begins: Echoes of Old Voices in the Rise of New Machines. Oxford University Press,

[3] Greene, RT (2013) Your Door to Innovation: 100 Models Bestest-Mostest Press (forthcoming).

[4] Nigg, Walter, (1959) Warriors of God: The Great Religious Orders and Their Founders

[5] Colcutt, Martin (1996) Five Mountains: The Rinzai Zen Monastic Institution in Medieval Japan (Harvard East Asian Monographs) [6] Wolfram, Stephen (2002) A New Kind of Science Wolfram Research Books

[7] Greene, RT (2006) Are You Creative? 60 Models at http://www.scribd.com/RichardTaborGreene

[8] Fiske, Allan Page (1993) Structures of Social Life Univ. of Chicago Press

[9] Arendt, Hannah (2006) On Revolution Penguin Books

[10] May, Rollo (1998) Power and Innocence Norton

Social Automata PowerOne theory covered in another part of this book is General Empirical Computation. That theoryexamines how social computers and biologic computers, invented by natural selection over billions ofyears interact with machine computer, invented by humans recently, to spawn recognition of furthercomputer types in societies and biology and invention of further new types of machine computers byhumans. The three domains of computer are interacting continually to spawn still further forms ofcomputation. Among all these are computational sociality (another theory treated in a later part ofthis book). This is people, familiar with various machine computer regimes, seeing new ways toarrange humans, using people as if they were central processing units to be put into array formatsand given algorithms of work to perform and exchange among each other. Indeed, all extant formsof human social organization are such computational socialities, though rather dull and homogeneousones. When you really imagine machine computation regimes--object oriented programming, neuralnets, simulated annealing, and so on--you can readily imagine forms of human organization neverseen in human history before--might not some of them outperform our traditional ways of arrangingour selves? Social automata power is the power that comes from one particular machine computerregime applied to revise how humans think and work together--cellular automata. By replacing thecells of cellular automata, with persons sitting in array patterns, we can apply much of automata the-ory to get work done by humans, sitting in fixed arrays, and exchanging messages and partial prod-ucts with each other. Just as cellular automata have rules, applied simultaneously, to all cells,based in the different initial conditions around such cells, so too rules can be defined for humans sit-ting in arrays, so that humans facing certain configurations of initial conditions around them invokecertain particular rules, operating on those nearly initial conditions.

Many social automata have been created and used to do work, resulting in experience of what formsof social automata outperform usual work arrangements and what do not. This amounts to discoveryof a new missing micro-layer of human organization, replacing vague meeting and discussion proce-dures, customary among humans worldwide, with much more specific and particular cognitive opera-tions, more specifically flowing among persons, sitting in arrays. Instead of brainstorming, voting,commenting in rotation by everyone, and similar meeting procedures, you get two people proposing adesign, two others suggesting means to realize it in actuality, two others tweaking it for feasibility intime and money and technical capability terms, two others critiquing it rigorously, two others repair-ing it to withstand critique points, and then, when dozens of such mini-designs have accumulated,one every two or three minutes, the entire automaton dissolves and examines the 30 or 50 designsoutput, evaluating them for the best ones. In this way, 50 designs per hour can be gotten from agroup, each design built on a five step discipline process basis, when normal. groups get at most fiveor six designs from such a group in such an amount of time. This is the extra productivity that havingthis micro-organization layer enables.

The question is, just what are all the social automaton arrangements, that work well? Since thereare billions of them, no one knows. However, some people have experimented with this idea fordecades and built up fairly large repertoires of social automata that all work much better than usualhuman meetings and discussions. It is these high performing social automata forms that are reportedin the 25 chapters below in this part of the book.

There are a number of dimensions used in designing social automata. We can put cleavages ofgroups in charge of functions they usually are denied--so we make women the designers, and menimplementors of those designs, for instance. We can put old people in charge of formulating propos-als, and make younger ones edit and improve the proposals, for another example. This is the cleav-age dimension of social automata design. Similarly there is a parallelism dimension of socialautomata design. We can design automata that amount to several streams each creating the sameproduct in parallel, with or without cooperation allowed among streams. Or, we can design autom-ata with elaborate sequential design and editing and checking procedures, with nothing being doneby parallel teams. The first gets us several candidates to choose from at the end, the latter gets usone more fully fleshed out design, with particular features to tweak at the end. Which is best ishighly dependent on who the customer of the output product is and what satisfies them best. Thereare a dozen other design dimensions of social automata covered in the chapters below.

Every social automaton has the same abstract dimensions of cells (basic units, persons), abstractneighborhoods uniting some of those cells, rules for basic units, rules for neighborhoods, iterativeapplication of rules, initial conditions, behaviors assigned to basic units and/or neighborhoods.These abstract components of any social automaton can each be stretched in aggressive directions,to produce strange or highly unusual work arrangements, possibly able to achieve things no otherhuman organization form achieves. For example, evaluation of what is being produced by a socialautomaton can be put at the end by one neighborhood or at the end done by all neighborhoodstogether. It can also be spread all along, thought the procedure steps of the automaton, so each lit-

tle partial product by any one neighborhood gets evaluated in some way by those who produced it orothers, before being passed on to the next step in the procedure. By tinkering with how much eval-uation, of what type, distributed how, we can come up with dozens of different social automatondesigns, some of which may outperform, by considerable margins, usual forms of work. The chaptersbelow cover dozens of such stretches of the basic components of social automata.

People able to get ten times the output, in the same time and quality, as others get, from any 8 peo-ple working together, rise to the top of our world. If they also get ten times the learning and enthu-siasm of doing the work, then all the better. Social automata are both more productive than usualmeetings and discussions and more fun. You learn more in them, meet people better in them, dohigher quality work, get more done, and time flies, no one feels bored and under-utilized. Socialautomata truly are power, real improvements in power.

Computational Sociality: Design Dimensions of

Social AutomataThe components of any social automaton are as follows:

1) product to be produced by the automaton2) initial conditions input to certain parts of the social automaton array3) basic units (persons), how many of what skills, training, and types4) topology putting basic units into some array format (people sitting in rows, strings, arrays, cir-

cles, figure eights, etc.)5) abstract neighborhoods combining certain basic units functionally whether or not they are phys-

ically together (adjacent pairs, adjacent females, adjacent elderly, etc.)6) behaviors (rules) either the same for all basic units, or for all neighborhoods, or distinct, some

rules for some unit types and neighborhood types--note, rules either have the form of “any unit/neighborhood having the following inputs, does X” or “units/neighborhoods of Z type, does Y”

7) allocation of behaviors (rules) to specific basic units, or neighborhoods, or to all units, or allneighborhoods, when the iteration time interval changes, rules are applied to new inputs to pro-duce new partial products from each unit or neighborhood or all of them

8) systems to install to some units or neighborhoods--reflexivity system for evaluating processquality and partial product quality, tuning system for adjusting degree of connectedness, diver-sity, and deployment of initiative throughout the automaton

9) partial product format specs10) iteration interval, that is, the amount of time that all units have to taking their inputs, pro-

cessing them, and generating required output formats.

The above components are in rough order, so you design a social automaton by specifying 1, then 2,then 3, and so on till you specify 10. The critical step is 9, specifying the exact form of each partialproduct. This is what each unit or neighborhood works toward, during any one time interval. Thesesocial automatons generally use a single time interval, with units/neighborhoods doing their assignedbehaviors during the time interval. I usually use 2 to 5 minutes per interval, so every 2 or every 5minutes, all units/neighborhoods hand their partial products to next units/neighborhoods in thetopology/array and take inputs from other units/neighborhoods in the topology/array.

If you seriously look at the above components list, you find that existing meetings and work organiza-tions are social automata. They are large in scale, however, having abstract neighborhoods calleddivisions, departments, sections, and workteams. Workteams are their basic units, for the most part.They do not have neigbhborhoods within workteams--this is the missing “micro-organization layer”that I refer to. If you break up 8 to 10 person workgroups into five neighborhoods, say, of two peo-ple each, you can make them into a social automaton. Contemporary organizations will have spe-cific outputs that workteams, collectively, are responsible for. Such workteams take inputs fromother workteams, and pass along partial products to still other workteams. The overall organizationbreaks down tasks, hierarchically, into smaller tasks, and still smaller tasks, till each workteambreaks its task down into tasks for each person. There are several unthinking conformities in thissystem that are both unnecessary and dysfunctional. For example:

a) one person does one job all day five days a week--compare--one person does a different jobevery two hours for a total of 20 jobs per week, each differentb) one person works for one part of the organization all year--compare--one person works forthree different parts of the firm within any one weekc) one person works for only one organization all year--compare--each person works for two dif-ferent firms at the same time each weekd) how a person does his or her work rarely changes much within each year--compare--each per-son develops a repertoire of 5 or more ways to do each job he has and tests them experimentallyone month per year, choosing the best two or three to alternate using at his or her job each week.

The above simple changes in work organization are impossible revolutions in businesses world-widetoday. They are much too difficult and bold for modern managers to handle. They scare managers.However, most of them are far more interesting and productive than existing work arrangements foremployees. Truth is, the above changes in usual work organizations are difficult for not-so-well-edu-cated managers, the norm world-wide, to encompass. Managers, especially upper level ones, needthings kept simple because they lack educational levels sufficient to grasp anything complicated.Also, there is not a little laziness behind this. So instead of a world where everyone does four or fivedifferent kinds of work each year--staying fresh and broad--we have a world where people burn outor tune out, because they do the same thing day after day year after year, because the managerswho design their firms are too lazy and mentally limited to imagine, design, and launch more compli-cated work arrangements. From a social automaton view, current forms of work organization occupya trillionth of the possible work arrangement forms possible. Social automata are ways to explore,even on a micro-within-workgroup level alternative forms of work organization possibly dozens oftimes more interesting and productive than the simple command hierarchies that chimpanzees andcorporate managers like and set up.

The truth is--most businesses organize themselves for their main mission--getting masses of low levelmonkeys to worship and inflate the egos of big shot monkeys on top of their command hierarchies.Most businesses are conducted for psychological purposes, with finances managed to just keep thepsychological games going. Most businesses do not make a lot of money because no one in themcares about that--everyone is satisfied with one-up-man-ship games with their fellow monkeys. Thisis only a slight exaggeration--most CEOs are running their firms purely to enrich themselves person-ally. Keystrokes on executive information systems, over the last 50 years, in all top firms, haveshown that top executives never access such data for business purposes, but rather, two or threetimes a day, to see how their personal investment portfolios are doing on various markets. It is allmonkey business in a banana-land.

When you think of contemporary organizations as limited versions of social automata design space,you imagine all sorts of alternative ways to organize work that have never been imagined or triedbefore. We have an immense unexplored frontier before us that social automata allow us toexplore. Let’s go!

Exercising Social Automata

It is best to start designing social automata for 8 to 10 person workgroups, and later expand scale to50 to 100 person sections, then to 400 to 1000 person departments. If you perfect the 8 to 10 personlevel, then power at all larger scales is assured. The contrary is not true--if you start with socialautomata designs for entire firms of hundreds of people, no value may be found because work disci-plines at the small levels are not matched with the overall automaton arrangement. It is best tostart at the micro level and work up.

Matching Topologies to TasksBelow are two lists, one of tasks to be done, expressed as products to be created, and another ofarray topologies of a 10 person social automaton to do such tasks. Your job is to select the bestarray topology for doing a particular task and to justify your choice, stating precisely why thatarray topology is better than all the others for doing that task. What is it about the task thatmatches it best to that array, and what is it about that array that matches it best to that sort oftask?

List of array topologies:1) two 5 person neighborhoods, each neighborhood organized as five sequential persons doing onestep each in a 5 step sequential process; the two neighborhood compete, comparing their finaloutputs, then by overall vote of all ten people, the best overall output is chosen, and the bestparts of the losing output incorporate to improve parts of it. 2) five two-person each neighborhoods arranged sequentially with each neighborhood doing onestep in a 5 step process; after a few dozen outputs have been thusly produced, the array dissolvesand all ten people evaluate the products accumulated, choosing the best one and incorporatinginto it best parts of losing other candidates.3) three three-person-each circles, with a sequential six step process within each circle (each per-son doing two steps), and the circles exchange halfway partial products with each other, halfwaythrough the six step production process4) three three-person-each circles, with one circle generating proposals, the second and third cir-cles competing to edit and improve those proposals, and the first circle choosing the bestimproved proposal from the products of those two

List of tasks:a) design a form of chair made entirely our of pet bottlesb) design a fund raising campaign to raise $5000 in a month from college studentsc) set up a email-order delivery coffee shop, anywhere on campus, that is profitable from startd) evaluate existing state policies for immigration and propose specific improvements that localpolitical leaders can help implement.

Your job:1) for each of the four tasks, which array topology is best?2) what traits of the task make that array best?3) what traits of the task make that array best?4) what weakness in each of the other array types makes if not best compared to your chosenarray type?

Express Contemporary Group Organization Form in Social Automata TermsFind an existing group or organization and express it in social automata terms by specifying eachof the ten social automata components as found in that actual existing organization.

1) product to be produced by theautomaton___________________________________________

2) initial conditions input to certainparts of the social automatonarray___________________

3) basic units (persons), how many ofwhat skills, training, andtypes_____________________

4) topology putting basic units intosome array format (people sitting inrows, strings, arrays, circles, figureeights,etc.)________________________________________________________

5) abstract neighborhoods combiningcertain basic units functionallywhether or not they are physicallytogether (adjacent pairs, adjacentfemales, adjacent elderly,etc.)_____________

6) behaviors (rules) either the same forall basic units, or for all neighbor-hoods, or distinct, some rules forsome unit types and neighborhoodtypes--note, rules either have theform of “any unit/neighborhood hav-ing the following inputs, does X” or“units/neighborhoods of Z type, doesY”________________________________________________________________________

7) allocation of behaviors (rules) to spe-cific basic units, or neighborhoods, orto all units, or all neighborhoods,when the iteration time intervalchanges, rules are applied to newinputs to produce new partial prod-ucts from each unit or neighborhoodor all of them_____________

8) systems to install to some units orneighborhoods--reflexivity system forevaluating process quality and partialproduct quality, tuning system foradjusting degree of connectedness,diversity, and deployment of initia-tive throughout theautomaton_______________________

9) partial product formatspecs_______________________________________________________

10) iteration interval, that is, theamount of time that all units have totaking their inputs, processing them,and generating required output for-mats._______________________________

Now change just two of the above ten components, that is, redesign the existing organization bychanging two of the above ten--

a) which two are best to change, b) how should they best be changed to improve organization performance, c) how does each such change help improve performance? d) what does each change lose in terms of performance attributes of the existing organizationform?

Standard Human Meetings as Social Automata (Of

Limited Sorts)All existing forms of human organization can be seen from the social automata perspective as beingsocial automata, though of extremely limited sorts. In other words, out of the entire space of possi-ble ways to organize tasks and people and the relations of tasks to people made possible by the socialautomata highly abstract framework, actual current ways of organizing people occupy a tiny portionof that space of possibilities. Our current ways of work examine and use a small, very small, fractionof possible ways. The internet, unfortunately, has mostly, merely become a new “place” to execut-ing traditional existing ways of organizing. Only a few slight changes--some asynchronization of pro-cess steps, some automatic sending and receiving of messages, some enhancement of individuals andtheir ability to publish in ways everyone in the world can access and see--in how we work haveresulted from much internet spread and expansion. The social automata framework, presented inthis chapter, suggests tens of millions of new ways to organize work in face to face settings andacross the internet. This space of organization forms if largely, even entirely, unexplored. We donot know what we will find there, but first excursions into it, made by the author of this book overthe last 20 years, have outperformed existing forms of organization in a number of non-negligibleways.

How do people now meet? They have discussions--about composing today’s agenda, about who willcome late and leave early, about news and environmental changes affecting the meeting’s work,about how to decide what to do, about how to do what has been decided on. Each discussion is com-posed of a sequence of responsive remarks--people hear what others say and respond. There arestandard types of comments, well researched decades ago, with particular combinations of remarksfound to correlate with productive meetings (as judged by meeting results and by those participat-ing), and other combinations correlated with unproductive or dysfunctional meetings. See the dia-gram below, which summarizes two different ways of categorizing meeting remarks. Each way worksthe same way--you find combinations of remarks that correlate with productive meetings (measuredby objective results and by assessments of participants) and that correlate with unproductive meet-ings. These--discussions within meetings and frequency distributions of remark types within discus-sions--are two different size layers within meetings. Any one meeting with consist of a number ofdistinct discussions, and each such discussion will consist of a particular distribution of remark types.Both the discussions and remark type distributions are unordered--that means that what is discussedfirst, second, third, and what remarks are first, second, third, are not well determined in normalmeetings. Departures from planned agendas are frequent and often much more important than whatthe agendas included. Discussions at the start of meetings about what the agenda is and should beand discussions that break out unexpectedly during meetings about revising the agenda or departingfrom it, are frequent and not at all abnormal. Research has shown that failure to have thorough longdiscussions of what the agenda should be initially leads to large-scale undermining of agendas duringmeetings as break-out discussions about agenda revisions appear, often with increasing frequencyand heat as the meeting draw toward a close. Like it or not, the participants of nearly all meetings,one way or another, themselves decide on the agenda of the meetings they attend, over-riding leaderimpositions. With discussions taking place in any order and within discussions with remark typesbeing made in any order, there is no overall meeting process, no overall discussion process, no over-all remark process to notice and continuously improve. Total quality views of traditional meetingsnotice that the most basic requisites for improvement are thusly missing. Meeting processes are tooun-ordered, ill-formed, various, undependable, to be studied and improved--whereas nearly all otherprocesses in contemporary businesses have been studied, modeled, and continuously improved, usingordinary quality methods.

Meetings are small groups. There are entire academic disciplines that examine the dynamics of smallgroups. Consider the complicatedness of relationships within any marriage, then consider a dozentimes that complication--that is the level of complexity of any meeting. The social realities of meet-ings undo what we all assume and think overtly as commonsense contents of meetings. The meaningof meetings, for example, is emergent, both from forces meeting prior to meetings to arrange thingsand control and influence things in the meeting and from people meeting after the meeting to nudgeinterpretations and impressions so as to change what the meeting means to people (and what resultsfrom it). The “contents” of any meeting is who sits with whom and who responds to whom, as wellas who avoids whom in sitting or responding. People help meetings to fail deliberately as parts oftheir personal career strategies or other subgroup strategies. In order to get a large budget for ourproject to-be-proposed next year, let’s cooperate to make the alternative approach this currentmeeting is supporting fail, for example. People meet sometimes to get real work done but mostoften they meet to symbolize power relationships or celebrate deadlines met or missed. The mean-ing of remarks in any meeting can be real alternatives considered and selected from or it can be ashow of alternatives, none really considered, but needed as a background show for what was pre-determined or pre-selected outside the meeting itself. Meetings can be entirely taken up with dis-plays of hormone driven male or female behavior--male hormones taking up all meeting time in pettycompetitions of males trying to outdo each other in looking important and females trying to outdoeach other in demonstrating greater intimacy and trust of key persons (displaying pride in relation-ships). The way these hormonal dynamics dominate or blend with rational meeting purposes andcontents is a key trait that any meeting has. Meetings can assign tasks to people based on what theyare competent to do or they can assign tasks to challenge people to grew beyond current ability lev-els. The leadership of most meetings is a mix of over up-front self-important-looking buffoons (often

Propose--propose some-thing

Build--further develop someone else’s idea

Support--support some-one else’s idea

Disagree--express differ-ence or disagreement with what someone says

Defend--improperly defend your own or some-one else’s earlier remark

Attack--improperly attack or disparage some-one’s remark

Test Understanding--make sure you understand correctly

Summarize--summarize several earlier remarks or whole meeting flow

Seek Info--ask for facts, remarks, reactions etc.

Give Info--[in many meetings most remarks are of this type]

Shut Out--interrupt or prevent someone speak-ing

Bring In--invite some-one to speak

Generating Basic Units

Naming Points

Combining Points

Distinguishing Points

Establish-ing Truth

Furnishing Evidence for Points

Countering Evidence for Points

Weighing All Evidence for Points

How the World Works

Asking for Points

Finding or Suggesting Causal Links Among Points

Finding Cate-gorical Simi-larity Among Points

Using Peo-ple Well

Inviting Talent, Roles, or Par-ticipation

Rehearsing Limits, Requirements, or Values

Structuring Time, Action, or Discussion

Standard Evidence Based

emergentmeetingmeaning

meetings emerging from pre-meetings interacting

meeting meaning emergingfrom post-meetings interact-ing

networks whom people sit with andaway from

whom people respond to andignore

demystifi-cation

strategic encouragement offailure of self

strategic encouragement offailure of others

reality orappear-ance

meeting to symbolize and cel-ebrate

meeting to do work and fur-ther processes

consulta-tion or realparticipa-tion

remarks as alternatives to beconsidered, fused

remarks as context for choicesmade, fixed

hormonal-ity

emotion meeting of hormonalstatus displays/gossips, neu-roticism, cultural biases/blindnesses

rational meeting of tasksdone, issues surfaced/fixed

develop-mentality

work within the competenceof persons

work beyond competence,stretching abilities

leadership overt up-front appointed lead-ership

covert back-of-the-roomemergent leadership

Social Realities of MeetingMeeting BehaviorsMeeting Behaviors

getrequirements

get meeting require-ments of external meet-ing customers fromparticipants

get meeting require-ments of participants inmeeting from partici-pants

partic-ipantsdesignownmeet-ingagenda

get suggested agendatopics from participants

group similar items,name groups

identify all groups oftopics that can be post-poned or eliminated ordone by some othermeeting/group

prioritize remainingtopics listed

create final agendatopic list

between meeting pro-cess check

treat-mentsandtreat-mentlead-ers pertopicassigned

standard meeting rolesassigned

treatments per topicdetermined

leaders of each treat-ment determined

time/tools per treat-ment determined

topics/treatments/lead-ers excluded fromtoday’s meetingassigned

execution of the meeting designed

meet-ingclose

requirements met check

action items and post-poneds assignmentsreviewed

Democratic Rules of Order

males) and covert, unseen, backstage influencers who control contexts, agendas, evaluations, andpick which buffoon to have up front preening and pretending to be leader.

Meetings As Social Automata

This is best seen first then discussed--see the table below.

Notice in the table above that not a single social automaton dimension is done or specified, exceptone, which is dictated by leaders--system reflection functions usually are the monopoly of the meet-ing leader. The social automaton model specifies 12 dimensions of interaction design, only one ofwhich is used by traditional meetings. In other words, usual human meetings entirely omit 11 of 12dimensions of design from the social automaton model. All human meetings throughout the eonshave explored only one of twelve social automaton dimensions. There are literally billions of waysof arranging for humans to interact that have been missed entirely, never explored, by usual meet-ings. I call this the missing micro-layer of human organization. At the base of banks, bureaucracies,adhocracies, and all other forms of human organization, is a missing layer of possible organizationthat has never been explored or used before. This is a new, open frontier, both in face to face gath-erings and across new media like the internet.

Making Meetings More Productive by Specifying Some of their SocialAutomaton Dimensions

A few organizations, Xerox for one example, have standardized remark making types, evaluation themix of remark types in meetings and even suggesting a simple standard meeting process. The resultis meetings remarkably more effective uses of time than similar meetings in other organizations.However, very few social automaton dimensions are touched on in these Xerox innovations--remarktype evaluation and training amount to partial specification of behaviors of basic units within meet-ing neighborhoods. However, what remark is made by whom when is still chaotic and free, meaningthat each meeting topic has an entirely unspecified procedure treating it. The direction for makingmeetings more productive is further specification of their abstract dimensions and contemporarymeeting methods have barely scratched the surfaces of the specifications possible. The table abovehas another example of partial further specification of meetings, called Democratic Rules of Order--here participants design an agenda, then design a series of treatments to give each topic in the

The Social Automaton Settings of Standard Meetings

SOCIAL AUTOMATON COMPONENTS STATUS CORRESPONDING STANDARD MEETING PARAMETERS

basic units only onesetting

individual persons do all tasks, within the meeting,though pairs and teams also do things outside,between meetings

rules of how basic units behave unspecified general discussing, general remark making, no stan-dard protocls/procedures

abstract neighborhoods clumpingbasic units

unspecified informal social clumps, alliances; covert personalrelationships; hidden career-ally coalitions plus for-mal roles assigned to attend the meeting

rules of how units in neighborhoodtypes behave

unspecified informal, changing, non-standard rules of behaving

rules of how neighborhood typesbehave

unspecified informal, changing, non-standard rules of behaving

system reflection functions assignedto units or neighborhoods

dictated purpose setting and result pattern recognition by dic-tate of leader, modified by employee resistance

initial conditions of units, neighbor-hoods

unspecified various, unspecified, variable

iterative application of rules unspecified no standard rules/procedures applied across meet-ings; informal norms of discussion/remarking develop

tuning automaton performance bysetting connectedness, diversity, andpatchings parameters

undone no recognition of or setting of connectedness, diver-sity, or patchings parameters, save when crisis dic-tates

spotting emergent result patterns resisted emergent results in fight with assigned results, oftenemergents seen as bad intrusions not natural results

pruning away noise from emergentresult patterns

unspecified no formal careful pruning away of noise from emer-gent patterns and results

standardizing automaton settings,configuration, rules

undone no standardization of settings, configurations, andrules--rather gradual emergence of discussion/remarking norms

agenda along with someone to lead each such treatment, then participants execute their own justcompleted meeting design, then they evaluate and close the meeting. Though all these steps can bedone via general discussion and remark making, the specification of a treatment for each topic in themeeting, can gradually introduce a level of procedure missing from all ordinary meetings today.

There is a context here that needs to be mentioned. In the 1980s artificial intelligence programmerswent around the world, asking experts at design what was on their mind every 15 or 30 seconds whilehandling tyical cases. Their answers were turned into large transcripts. Similar operators and oper-ands were spotted in these transcripts and the transcripts were rewritten using those standard oper-ators and operands. The resulting transcripts were turned into software code to make expertsystems that did some of the invention work the actual human designers did (though generally muchfaster and with less error). There were thousands of people doing this work, worldwide. Their col-lective experience was discovering, along with the designer-experts they extracted knowledge from,that very particular procedures were present in work that seemed fluid, intuitive, and carefree.Underneath virtually all expert performance was a base of highly specific highly automated proce-dures. There was nothing vague or ambiguous about the basis of their expertise. A feeling emergedthat the world had lots of expert performance, parading around as if the result of intuitive freedomand insightfulness of particular experts, but actually based on highly specific learned procedures.Meetings, as done today, lack such a basis. Underneath them is no base of highly practiced expertprocedures, unfortunately. As a result, meetings, for the most part, waste people’s time; they areunproductive except in rare cases.

Another context comes into plat at this point in my argument. There are some high performing per-sons and unusually productive groups. Examination of their meetings shows such groups build uprepertoires of ways to treat certain types of topics. Over years, these repertoires of ways to treatcertain topics can contain many dozen specific procedures. Though such procedures tend to averagefive to seven steps, with a few procedures having ten or more steps, this is a lot more detailed spec-ification than usual discussions and remark types have in usual meetings. If we took a procedure forprioritizing issues, for defining problems, for scoping opportunities or budgets, or for any other com-mon meeting task and function, taken from people expert at doing the procedure, then the issueremains of how to allocate the doing of that procedure to people in a meeting. Here is where lack ofthe social automaton framework hurts productivity, because most groups would allocate such anexpert procedure to their group by having the whole group, march step by step through the proce-dure, with only a few, specialized steps, getting assigned to subgroups outside the meeting and thelike. Social automatons, however, would find myriad ways to allocate steps in the expert procedureto individuals, to pairs of individuals, to triplets of individuals, with a step being done by one playerfor entire meetings, or that same step being done by first one player than another, during a meeting,or with that same step being done by first one person, then by one pair, then by one neighborhood.With a social automaton perspective tasks can be assigned to basic units, to neighborhoods combiningthem, so that tasks end up getting done either by individual persons, or by persons in the sameabstract neighborhoods, or by interactions between neighborhoods. Experience using a particularexpert procedure would allow a group to figure out the best way to allocate the procedures stepsacross social automaton dimensions.

Exercising the Social Automaton Dimensions of Ordinary Meetings

Since ordinary meetings, as discussed above, ignore 11 of 12 social automaton dimensions, and han-dle the 12th by leader dictatorship, we can transform ordinary meetings simply by introducing anyone of the 11 omitted social automaton dimensions to it.

Injecting a Social Automaton Dimension into Any Ordinary MeetingIt is useful to learn what the social automaton dimensions mean and imply by testing each dimen-sion via injecting it into some ordinary meeting and seeing the results.

1) select a meeting your regularly attend2) select a social automaton dimension to inject into that meeting3) further specify that social automaton dimension as follows:

a) what topic in your meeting should the dimension be applied tob) what pre-meeting contexts or work would help insert your chosen dimension to

the handling of that topicc) what within-meeting remarks or discussion would help insert your chosen dimen-

sion to the handling of that topicd) what exact steps of inserting/applying the dimension would benefit the handling

of your chosen topice) design pre-work, within-meeting work, so as to execute those exact steps

4) what results did you get that would not have been there with usual meeting ways of handlingthat topic?

Trying a Social Automaton Form to Replace Usual Meeting Forms

This is a lot of work and risky, as the idea is new to you, but, if you work with people who trustyou, you can invite them to join you in an experiment along these lines. You might have themread this chapter of this book to provide context for what you are attempting to do.

1) put everyone into a fixed array seating arrangement, for example, a row of pairs of people--fiverows of two people each in a line or three sets of four people each with each set of four arrangedin a square seating arrangement and all three sets of four facing each other in a triangular arrange-ment2) assign an overall product for the meeting to produce, and design a five to ten step procedurefor producing that product3) assign one or more steps to each row of your five rows or to each square of four people in yourtriangular arrangement of squares of people4) decide a time period for doing all steps in your procedure (some steps can be done fast, sometake more time, decide a time interval so the longest-to-complete step can be done, if some stepsis much longer than others, change step assignments so no unit has steps much longer than anyothers)5) all units assigned a procedure step have to complete it within the time period and hand theirresult to the next, neighboring unit, at the end of the time period6) set up initial conditions that the first unit doing the first procedure step gets, plus any data ortools needed by units doing other steps, and provide them7) make sure everyone understands their assigned role, where they get what form of input fromand who they give what form of output to, and what the time interval is for doing their step8) start the automaton, with all groups applying their assigned procedure steps within each timeperiod; results accumulate with the last unit in the procedure step sequence9) when a large enough number of results has accumulated, step the automaton and have a gen-eral meeting wherein all participants examine what was produced, choosing the best items andcombining their best features into one overall best result. Compare the amount of results andtheir quality with that obtained from usual meetings on the same topic.

Social Automaton Design ProcessSocial automata differ from usual human forms of organizing work in at least the following ways:

a) instead of general discussion and remark sharing, designed procedures fromexperts are followed

b) instead of each person participating in all steps of a meeting, each person hasone or more assigned roles they repeated perform

c) instead of one version of an overall output, many version are generated and thewhole group pf them later evaluated to get a final output

d) instead of tweaking or fixing some individual’s procedures or partial products,overall interaction traits of the entire group are tweaked and fixed

e) instead of only one process going on in a time period, several or many processesin parallel go on in a time period.

These represent not all social automaton forms but a practical subset that differs enough from stan-dard human meetings to constitute a new micro-layer of organization of minds that outperformsusual meetings and usual within-meeting procedures by orders of magnitude in the amount and qual-ity of outcomes it produces.

It is important to keep the above principles in mind, else one gets lost in the abstractness of socialautomaton parameters and possibilities. The above feature restrictions identify a productive sub-space in the huge overall space of possible social automaton arrangements. The core of this particu-lar subspace is repetition, or recursion. By centering a group together on repetitions of a welldefined, improvable process, a certain maximum of opposites is achieved--maximum order with max-imum flexibility, maximum quality with maximum productivity, and others. The steps, discussedbelow, for designing social automatons, assume the above subset of all possible social automatonarrangements.

Sources of Resistance to Social Automaton Forms of Work

We are all used to freely joining in or waiting out meetings. We are all used to freely deciding whatto say and when to say it. Takiing this away from people feels harsh and is resisted. Doing socialautomaton arrangements depends on getting trust sufficient to allow people to stop resisting thusly,and to get people trying out the new arrangements, trusting that their unusual procedures will pro-duce disproportionately better outcomes than usual work arrangements. It is for the sake of betteroutcomes that people can be gotten to tolerate more restrictive participation procedures.

Being well defined repeated processes, social automatons allow practice of procedures till excellenceof performance and outcome are achieved. This differs from usual forms of meeting which are tooill-defined to be remembered and repeated step by step. There is power in repetition, as computershave proved. This power is also apparent in social arrangements of work.

Computational sociality refers to new social arrangements inspired by new machine forms of compu-tation. Humans invented machine computers and then were affected by interacting with them.Humans began to think of configuring social arrangements among people, as if the people were pro-cessors in parallel arrays. Re-imaging society and social processes on computational grounds alsoallowed repetion, the power of it, to creep into human ways of organizing work. We have had repe-tion in blue collar days of mass production manufacturing. To experience cognitive repetition ofsteps seems like a throw back to harsh industrialization days of mindless workers repeating manufac-turing steps. There is a difference, however:

1) the steps repeated are cognitive challenging, a real stretch of current mentalabilities for all involved, so learning is large

2) the repetition is tuned via changing in mid-procedure various whole systeminteraction parameters so variety creeps into the sameness of repetition

3) often repetitions of procedures produce competing outcomes that are later com-pared and evaluated, taking the best ideas from each and recombiningthem.

So the repetition in social automata of cognitive procedures is not as de-human-izing as rote blue col-lar procedure repetition was in early ages of industrialization.

The Unexplored Frontier of Social Automata

Truth is we do not know most of the social automaton arrangements possible. The space has notbeen explored. Certain only extremely tiny fractions of all possible social automaton arrangementshave as yet appeared and been experienced/tested. We can conclude little about social automataoverall till more of their variety has been explored. This means experiences, even intense ones, withearly social automata forms are no reason to abandon or love later forms. The space is unexploredand we cannot generalize about it based on the tiny fraction of it known now.

We can, however, put new social automaton arrangements of work alongside existing traditionalways of work and note differences. Early such comparisons revealed that existing work forms occupyan extremely small tightly bounded subspace in the overall space of possible social automata.Humanity has been living in a micro-fraction of work arrangements possible.

Designing Social Automata

Of course the design starts wtih what you want your automaton to produce and with how many peo-ple of what sorts can be in your automaton. These are basic constraints. After this you have thestrategic problem of how much parallel effort and how much repetition to include in your uatomaton.With parallel subautomatons competing in producing the same or similar outputs you get hosts of out-comes to compare. With parallel subautomatons producing different parts of one overall outcome,you get converging partial products. With little or no repetition your outcome depends on the qual-ity of each step of your procedures. With much repertition the quality of your outcome depends onthe variety of possibilities covered by your many repetitions of the same or of slightly modified pro-cedures. So you next choose a little or a lot of parallelism and a little or a lot of repertition. Thefirst of these tells you constaints on the topology of your automaton or subautomatons--who sitswhere in relation to who else, what task sits where in relation to what other task. The second ofthese tells you constraints on the procedures done by each basic unit as part of othe overall proce-dures. So you specify parallelism and repetition degrees then you specify topology arranging yourpeople and procedures arranging steps for each basic unit in your topology’s layout. Specifying topol-ogy amounts to how many basic units in how many neighborhoods arranged how with respect to eachother. Specifying procedures amounts to what tasks are done by each neighborhood and basic unitwithin neighborhoods at each time interval in your repetition scheme. At each time interval eachbasic unit has an assigned task to complete the partial products of which are handed on to next (inthe topology layout) basic units or neighborhoods. Implicit in this manner of operation is socialautomatons at first have only one or a few basic units operating, with new ones added at every timeinterval till all are operating on partial products from prior (in the topology layout) units. Alsoimplicit is social automatons end gradually with initial basic units stopping work, one by one at eachtime interval, passing last partial products on with each time interval till all partial products havebecome incorporated in final outcomes.

The final parameters of social automaton design involve setting the reflexivity and tuning systems ofthe automaton. The reflexivity system is a special set of functions that always are found in all socialautomatons. This involves setting purposes, recognizing emergent procedure dysfunctions or faults,and recognizing emergent overall or partial outcomes of merit. The tuning system is a special set offunctions also always found in all social automatons. This involves adjusting the degree of connect-edness among components in the system, the degree of diversity in it, and the degree of distributionof various types of initiative taking in the system. Social automations have two time intervals, thebasic one that governs when all basic units do their assigned tasks, during each time interval, and ameta-interval, usually 5 or 10 or so basic time intervals, during which parameters of the reflexivityand tuning systems maintain particular values, with values changing when meta-time intervals

change. So basic units do work tasks, at basic time intervals, passing partial products among eachother, and at meta-time-intervals certain basic units or neighborhoods grouping them adust systemlevel parameters of reflexivity or tuning of performance. Somebody has to watch how things aregoing and adjust parameters to get optimal outcomes. Instead of having these meta-functionsalways being done by elites always monopolizing such functions (“leaders” this is called in traditionalhuman work organization forms), these functions are sometimes done by anyone distributed through-out the system. This is a democratization of management function embedded within and implicit tosocial automaton theory of operation.

An Example of Designing a Social Automaton

The task is, say, designing new kinds of chair unlike any that have been seen before. The people are24 college students in a classroom. The degree of parallelism is 3 teams designing such chairs in par-allel. The degree of repetition is each of the 3 teams completing one chair design every 6 time inter-vals. The topology for each team is 4 pairs of 2 college students per pair, arranged in adjacent rows.The procedures are a four step process of specifying unseen chair design types, specifying constraintsneeded to achieve such designs, making candidate such designs, and fixing errors in such designs.After all three teams have accumulated six designs, for a total of 18 in all, the three teams break upand as a whole group they examine and discuss each of the 18 completed designs choosing the bestfour and combining their best features into one overall best of best design. Time intervals will be 2minutes for doing each step. Larger scale time intervals for meta-work will be every 12 time inter-vals. Thus, every 12 time intervals (every 24 minutes) team 1 suggests adjustment or no adjustmentin connectedness degree within teams or among them; team 2 suggests adjustment or no adjustmentin diversity within teams or among teams (by switching task per pair or persons per pair or per team),team 3 suggests adjustment or no adjustment in initiative taking per team in terms of whether someunusual constraints on wanted designs should be added mid-process to some or all teams. Similarly,the first pair of each team, every 12 time intervals, announces proposed changes in pair task assign-ments needed, the second pair of each team announces proposed changes in task based on emergentproblems spotted, the third pair in each team announces proposed changes in task based on emer-gent partial results emerging. Voila! The social automaton is fully specified.

Exercising the Design of Social Automata

Social automata are designed twice--once off line, prior to the automaton existing and once on line,in real time operation, when adjustments to the parameters are made that amount to in-processredesign of the automaton.

Design Social Automata for Producing the Following OutcomesYou cannot get a real intuition and feel for designing automata till you optimize their designs forparticular outputs of diverse types. Designing a social automaton for each of several very differ-ent output types gives you a feel for what automata arrangements fit what types of output. Designsocial automata to produce each of the following three outputs (song, 30 tools, venture firmdesign) by filling in the table given below.

Social Automaton Design Form

basic core automaton dimensions

layout & steps time intervals assign units/teams to do reflexivity functions:

assign units/teams to do tuning functions:

form of output needed

num-ber of people

per autom-

aton

degree of par-allel-ism:# of

teams

degree of rep-etition:

# of steps/time

topol-ogy of teams/units

proce-dures per

unit/team

for doing proce-dure steps

for meta-func-tions

new task

assign-ments

spot emer-gent faults

spot emer-gent

results

set con-nec-tivity parameter

set diver-sity

parameter

set patchi

ngs parameter

specifynot justthe out-put but itsexactform/for-mat

specifyhow manypeople ofwhat gen-eral age,ability,and type,from whatsources

how manydifferentproce-dures willgo on atone time,or insequence:comple-mentary,competi-tive, con-secutive,contradic-tory?

how manysteps last-ing howlong eachwill eachproce-dure haveand howmany rep-etitions ofall/any ofthem willfit intotimeallowed?

how manybasic unitsper team,how manypeopleper basicunit, howmanyteams perproce-dure,seated inwhat con-figura-tion,intersect-ing otherunits/teamswhere?

what arethe stepsfor eachproce-dure used;what par-tial prod-ucts doeseach stepproduce inwhat for-mat; howdo partialproductscombineto becomefinal prod-uct?

how muchtime doeseach stepin eachproce-dure take;timeintevalsfor apply-ing proce-dures are:same forall steps,differentfor differ-ent steps,shortenover repe-titions?

how manyrepeti-tions ofbasic pro-ceduresgo onbeforemeta-functionsare done;how manytimeintervalsper repe-tition ofbsic pro-cedures?

what unitor teamchangeswhat pro-cedurestep isassignedto each orsomeunits/teams, onwhatbasis?

what unitor teamspotsemerg-ing faultsin workandadjustproce-dure con-tents ortopologylayoutsto fixthem?

what unitor teamspotsemerg-ingresults,not in theoriginalplan/designandadjustsproce-dure con-tents ortopologylayhoutsto cap-tue andusethem?

whatunit orteamadjuststhedegreeof con-nected-nessbetween unitsandteams inthetopol-ogy?

whatunit orteamadjuststhedegreeof diver-sity ineachunit orteam ortopol-ogy lay-out?

whatunit orteamadjuststhedegreeto whichinitia-tive tak-ing ofvarioustypes isdistrib-uted tounitsandteamsand lay-out fea-tures?

Social Automaton TypesThere is a mssing layer of organization in the world--we discuss and we meet with both of them, dis-cussions and meetings, having little structure, little ability to satisfy, and little productivity. Insteadof having 64 ways to discuss and to meet and mixing and combining several for specific purposes, wehave general vague discussions and general boring meetings. Because group ways of being togetherare much less productive and satisfying than individual ways of work, most people experience groupinvolvement as a giant waste of time. In some societies, because of group-favoring ideologies, thisleads to individual lives and works becoming as sloppy, poor, unproductive, and unsatisfying as groupdiscussions and meetings now are. In other societies, because of individual-favoring ideologies, thisleads to neglect and avoidance of group activity in general, often with tragic consequences--leavinggroup aims and results in the hands of the dullest, most boring mind in the room (President Bush ofthe US comes to mind).

Social automata arrangements are a way to fill in this missing layer of organization in the world.However, the dimensions of social automata are so abstract that the space of all possible socialautomata arrangements has barely been explored. We know little about all the social automataarrangements possible and hence, we can do millions of types of them without really yet knowingwhich will be best for certain purposes. The space to be explored is so huge we can do little atpresent but guess. Nonetheless, there are individuals and groups who have explored a little of thisnew territory of possible ways to organize discussions and meetings, workflows and groups, and theirinitial findings can guide us to some interesting social automata types to try out, seeing which workbest for our own various purposes.

The General Issue

What type of social automaton arrangement would be best for your particular purpose of themoment? To begin to answer this we must consider the most obvious dimensions of social automatondesign and how they might influence doing discussion, meeting, workflow, or group work.

Product1: a songshowingthe emo-tions of agrouptransfer-ring to aneedednew wayof workfor theirpresentold way

Product2: 30 dif-ferenttools forcombin-ing con-tradictoryideas well

Product3: thedesign ofa venturebusiness agroup cancreate bychangingall workcontenton oneday eachweek

Social Automaton Design Form

For whatever reason existing discussions and meetings seldom have work being done in parallel byparts of the group. Instead we have the entire group slowly and ponderously going through proce-dures of a vague nature, usually just lists of topics, at a pace that bores all. So one dimension ofsocial automaton design is the amount of serial work and parallel work within it.

Once you have the option of parallel procedures in discussions and meetings instead of just sequencesof steps, other possibilties arise. You can have meetings and discussions that gradually decomposewith one large group breaking up into several parallel teams and those teams, later breaking up intostill smaller teams, so that the overall event decomposes in personnel assignment terms just as sometopic decomposes in idea terms. Conversely you can have meetings and discussions that do theopposite--start with lots of little teams that coalesce into medium-size teams, who then coalesceinto one overall group at the end. This is a composition process of putting small pieces together toform larger pieces simultaneously done on topic and team terms.

As soon as you envision decomposing groups and topics and composing groups and topics, other alter-natives turn up. The decomposing can be into complementary topics and groups, or contendingones. That is we can decompose a topic (and associated group) into parts that complement and donot overlap each other or into parts that compete to do the same function and replace each other.Similarly for composition processes, the initial small teams and topics can complement each other bynot overlapping and fitting together to make a better whole or they can compete with each other,contending for becoming the final chosen winner or an overall competition of parts put together.

Meetings and discussions can be forwards or backwards. They can start with some future state ofaffairs that they would like to see a reality and work backwards to what must be true just before thatto make that final result possible, then working on what must be true just before that stuff to enableit, and so on, till things we can now do are obtained. They can do the opposite, start with presentcircumstances and suggest modifications of them towards some overall goal, then suggest furthermodifications of those intermediate results towards the same overall goal, till the overall goal isattained a certain number of steps later. These alternatives--backward reasoning from goals topresent and forward reasoning from present circumstances to future goal--can both be applied tocomposing and contending, composing and decomposing, serial and parallel arrangements of socialautomatons.

Fractal automata processes are social automata that break up into plural contending or complemen-tary similar such social automata. The results in social automata on several different size scales,operating over periods of time. One overall social automaton, for example, at the start breaks upinto 3 exactly similar ones, which, later, each break up into 4 still smaller social automata exactlythe same. This is fractal automata arrangements. Similarly, we can start with 12 contending orcomplementary versions of exactly the same social automaton, coalescing them into 3 identicalautomata, and at the end, coalescing them into one large automaton.

Automata can be constraints ceneterd or idea centered. Constraints centered automata have proce-dures designed to handle various constraints on a design or situation, possibly with teams and sub-teams for handling, each constraint, perhaps applying the same or a different procedure to differentcontraint types. Idea centered automat have procedures designed to handle various proposed alter-natives or ideas or visions for a design or situation, possibly with teams and subteams for handlingeach idea or proposal, perhaps applying the same or a different procedure to each idea/proposal.

Automata can deliberately ignore and cut across cleavages in the people present or they can deliber-ately use and organize around cleavages in the people present. For example, automata can divideinto parallel teams, each team a different age group, youth in one, middle aged people in another,and older people in another. Or a male team can contend with or complement a female team. Thesame for other social cleavages in a group: nationality, personality, social status, profession, com-pany of origin, and so on. Social automata can do the opposite--being careful to mix all ages, all gen-ders, all professions in some or all teams and subteams, for handling all topics or some topics.

Automata and the procedures they use can either be evidence based on experience based. You candesign automata to maximally analyze and use the experiences and variety of experience in a groupor you can design them to maximally analyze and find the evidence for positions in a group. Autom-ata can be design to find and combine experiences in a group or evidence in a library.

What procedures are in an automaton and how they are used in it can be pre-planned and dictated--a manual of steps automata teams follow--or they can be adjusted on the spot based on pre-plannedquestions or decision steps--:”let us assess the partial products we just produced on X, and Y criteria,and select the best them, then break up into one team for each of those best three, repeating ourearlier design steps to refine each of those alternatives” for example. The control of the overallflow of automaton procedures can be fixed ahead of time or can evolve based on mid-course assess-ments and judgements made. Here, the analogies between social automata and computer programsis evident. Social automata are, in effect, software-in-human-form systems, that are programmedmuch as computers are, instead of vague un-programmed procedures usual to traditional boringmeetings and discussions.

Editing is one of the primary functions that goes on in social automata of all sorts. There are twokinds of edits--supportive edits and demolition edits. In automata using supportive edits, each edit-ing team tries to improve what is before it by making edits that improve a proposal or alternative. Indemolition edits, groups want choice to result by having teams that edit alternatives by finding allthe possibly fatal faults with them and making highly visible the liabilities of each alternative. Insupportive edits the groups try to make not-so-robust things more robust; in demolition edits thegroups try to kill off completely alternatives not strong enough to withstand sustained critical scru-tiny. The idea in demolition edits is to kill off weak or flawed offerings by consideration and judge-ment rather than later suffer as investments are undermined by ideas not sufficiently thought outand strong. In social automata, edits are frequent as one team edits another team’s outputs, in par-ticular ways, and the like.

Similar to having parallel teams versus a single team following a sequential process is the choice ofdesigning automata to do one procedure one time, slowly and with quality, on some inputs versushaving automata that cycle through, applying the same procedure several times to the same input orto slightly different inputs. This choice, whether to do a procedure quickly several times repeat-edly, or do it slowly once, in a given time, depends on lots of other factors like whether repetitionsof the procedure will be by the same team to the same inputs or whether other teams will review andredo any one team’s initial work.

These are not the only design dimensions of social automata, indeed the above are only a tiny frac-tion of the design dimensions of actual automata. They have been, however, used by me and thegroup I developed social automata events for, with some success and are better guidance thanmerely stabbing out in the dark into infinite thinkable alternatives.

Exercising Social Automata Types

When you are designing a social automaton for some specific purpose, having types of automatabefore you is a great help. We can write questions you answer that tell you when to use each type.These questions, at this early stage in social automata science and research, will not be exact andalways founded in solid research results, but they will be better than stabbing in the dark withoutguidance of any sort.

A Table for Selecting Social Automata Types When Designing Specific Social Automata

By answering the questions in the table below, you can identify what type of social automaton, oneleven different design dimensions, best suits your task/purpose. Since one social automaton canlast 2 hours, 4 hours, all day, 12 hours in one day, 8 hours on 3 successive days, 12 hours on eachof 5 successive days, or any of an infinite set of other time arrangements, you can of course, haveall the types of social automata in the table below inside any one social automaton you design, justby including some of the below types somewhere in your time design for handling certain steps inthe procedures you want followed. So, for each short social automaton you answer all the belowquestions or for each part of longer-in-time social automata you answer all the questions below.

Eleven Types of Social Automata to Choose From in Desiging Your Own Social Automaton For Specific Purposes

Serial or Parallel Procedures Do you want subgroups working in parallel doing the same or different procedures orsubgroups all going sequentially through procedures?

Decomposing or Composing Pro-cedures

Do you want procedures that split hard issues into simpler smaller parts or that com-bine small parts into large wholes?

Contending versus Complemen-tary Partial Products

Do you want parallel procedures whose results compete for being selected or thatcombine to make some overall good result?

Backwards versus Forwards Rea-soning

Do you want procedures that step from present circumstances toward goals or thatstep back from goals to present circumstances?

Fractal Automata or Fractal Pro-cedures

Do you want an automaton that breaks up into smaller versions of itself or just pro-cedures that break up into smaller versions?

Constraint versus Idea/ProposalCentered Procedures

Do you want sets of steps (parallel or serial) per each constraint or per each pro-posed idea?

Social Cleavage Using versusCleavage Ignoring Procedures

Do you want subgroups or sets of steps per each social cleavage type in a group orthat mix such cleavage types?

Evidence Based versus Experi-ence Based

Do you want evidence based procedures (that operate on evidence) or experiencebased procedures (operating on experiences)?

Pre-Designed Flows versus In-Process Tuned Flows

Do you want pre-designed procedures or ones that are modified and redirected inmid process via evaluation assessment steps?

Specifying the Procedures for Particular Social Automaton TypesDesign two entirely different social automata of 16 people each to produce 50 new product inven-tions within two hours of work--have them invent completely new types of chair, such as, a typeof chair that can be folded in a certain way that transforms it into a table, a type of chair thatadjusts in every dimension to fit women or men, whoever sits on them. You are constrained indesigning your social automata to using ones that include the following automaton types--choosetwo of the three alternatives below and design, therefore, two entirely different automatons fordoing this chair invention task.

a. parallel groups, composing, complementary, forwards reasoning, no fractal procedures orautomata, constraint based, using social profession cleavages, experience based, tuned flows, sup-portive edits, multi-cycles

b. parallel groups, decomposing, contending, backwards reasoning, no fractals, proposal centered,cleavage ignoring, experience based, pre-designed flows, demolition edits, one cycle

c. serial groups and parallel groups both, decomposing and composing both, contending, forwardsreasoning, no fractals, constraint and proposal centered, gender cleavage using, evidence based,pre-designed flows, multi-cycles

Each of your social automaton designs should have a minimum of 20 steps in it, with completespecification of whether the whole group, certain teams or subgroups, do each procedure step,and to whom partial products from each basic unit, team, neighborhood are passed.

Social Automaton Types for Fixed Goals and Initial

ConditionsThis is a situation where you want something and you are pretty much free to get it any way that youwant. You have certain initial conditions and you have a goal and you are free about how to attainthat goal. The entire organization of your effort is free for you to arrange as you want. Most of thetime, in real situations, we are quite limited as to how we organize to obtain goals, having to use cer-tain persons, certain equipment, in certain well known or agreed on ways. So this type of automa-ton, not limited thusly, is unusual in real life and freer than real life situations usually are.Nevertheless it is an important type because, for most meetings and discussions, freedom to organizepeople any way you want is a realistic possibility. The micro organization layer, as has been saidabove, is missing so discussions and meetings are unstructured, too unstructured, and ineffective forthe most part. You get approximately one person’s worth of work from an entire group workingtogether in usual meeting and discussion formats, with others watching as each single person doesone unstructured step in a vague changing meeting or discussion process. In other words, a groupshould be more productive than the same number of people working individually alone or else it iswasting time. Add up the average productivity of each member working alone and that is the mini-mum productivity a meeting or discussion has to produce to not be wasting the time of all its mem-bers. The trick, then, is to come up with ways of organizing discussing and meeting so as to reachthis minimal level of productivity and beyond.

From Infinite Imaginable Hows

Overall one turns to social automaton arrangements to bypass the waste of time and energy in usualmeetings and discussions. That means one wishes to replace lack of structure, lack of process, lackof specificity of role and subgoal, with structure, process, and specificity. Social automata are waysto inject more structure into situations that dissatisfy because they lack form and structure. If youare psychologically predisposed to freeform random association doing-what-you-please, then socialautomata will not appeal to you (though all of your family and friends may want them, in part, tobring some productivity, focus, and intelligence to what you up till now have achieved via freeformwandering).

The design issue, then, is what structure for what, to add to common situations via social automataarrangements. With initial conditions and final goal to be achieved all that are fixed, how ones goesfrom initial conditions to that final goal, is completely unspecified in the type of social automatabeing discussed in this chapter. This is the hardest type of social automata to handle, because the

Supportive versus DemolitionEdits

Do you want supportive edits that improve partial products or demolition edits thatkill off weak or flawed partial products?

One Cycle versus Multi-Cycle/Recycled Processes

Do you want procedures that automata or teams do slowly, carefully once or onesthat are repeatedly applied to the same or different inputs?

Eleven Types of Social Automata to Choose From in Desiging Your Own Social Automaton For Specific Purposes

how is completely open and unfixed. There is almost too much freedom with this type--initial condi-tions and final conditions fixed, how to get from one to the other completely unfixed.

How does one begin to decide what structuring is needed and for what that structuring is needed?

The Main and Sub-Goals of Being Together

One old familiar dichotomy in designing groups is the one of organizing for mission versus organizingfor group healing. Some arrangements use the group up for the sake of attaining some mission;other arrangements diminish achievement of such missions in favor of healing a group previously tornapart. You can organize to heal groups or to use groups to attain some mission. There are lots ofsimilar dichotomies--attain a mission valued now versus attain a mission valued in ten years, use cul-ture resources for economic attainments and use economic resources for culture attainments, andmany others. How does a group, or its leaders, choose, what the group needs next and should donext?

Consider the following, reasons to structure a group and its processes:

1. for being more productive per unit time2. for the members of the group meeting each other on deeper levels3. for shutting down autocratic egotistical leaders who have abused the group4. for breaking a group free from charming conversations and inter-group relations for the bracingchallenge of changing itself by tackling a hard mission5. for using persons and talents of persons in the group but up till now ignored or slighted by thegroup6. for shutting down persons and talents of persons in the group that have monopolized it, shut-ting out contributions of others7. for opening the group to links and cooperations with other groups outside the group8. for shutting down outside relationships of the group that weaken or dilute its powers and poten-tials9. for growing member capabilities in a group by stretch assignments forcing people beyond com-fortable past norms of thought of behavior10. for updating the group by getting it to master things and developments outside it of currentimport or future promiseand many others.

It is clear, from the brief list above, that the purposes of groups are simply all human thinkable pur-poses. The hows of this type of social automaton are un-specifiable because the goals of groups areinfinitely various--as various as all human goals throughout all of history are.

Getting Practical

Social automata are new and challenging arrangements of people. They use people in unnaturalways, forcing people to apply specified thought processes rather than allowing anyone to think any-way they want at any time. They assign roles to people and arrange those roles in inter-lockingarrangements. This feels restrictive and people resist it at first. Only when they find the outcomesfrom such arrangements outshining clearly outcomes from usual meetings and discussions, do peoplebegin to resist social automaton arrangements less.

To get any group of people to use social automaton arrangements you have to give them good experi-ences of their first such arrangements. There are basically two ways to do this--achieve lots more ina given period of time with the social automaton arrangements than is achieved with usual meetingsand discussions or, highlighting persons or the particular talents of persons slighted in usual discus-sion and meeting arrangements. These two are the first two uses of social automaton arrangements,usually, because they have clear profits compared to work as usual traditions--in the first case, get-ting more or better quality outputs per unit person and time expended, and in the second case, get-ting talents or persons used whose contributions are slighted in normal work arrangements. Peoplelike getting better outcomes--more productivity. It makes everyone feel their time is not beingwasted. People like getting powerful contributions from resources, in this case persons, slighted inusual work arrangements. They like seeing resources that contributed little contributing a lot whenput in different ways of work. For this reason the first two uses of social automaton arrangements iseither for greater productivity or for greater use of human resources.

Now we have much more focussed and manageable questions--how to get more productivity and howto get more contributions from particular persons or talents. These more focussed questions quicklylead us to how to configure social automata.

Social Automata for Productivity Increments

Where does productivity come from in social automata ways of work compared to usual meeting anddiscussion ways of work?

from parallel teams or tracksfrom competing teams or tracksfrom editing of others’ ideasfrom thorough finding or exploration of alternativesfrom thorough execution or tuning of implementationsfrom in-process tuning to correct weak steps or sloppy workfrom breaking large teams/groups into smaller, less political unitsfrom using amateur procedures to using expert proceduresfrom depending on one big mouth person’s talents or “leadership” to depending exactly equallyon every member’s talents and leadership.

Many groups are simply larger than needed for doing good work. So when general discussions andmeeting are held, most of the people present are sitting and listening to things they already know ordo not find relevant. There may be three or four people for whom a particular topic resonates butwhile they get value the rest of the group wastes time and gets demoralized by doing so. When webreak up 8 and 10 person groups into three groups of 3 or 4 people, if we are careful about who goesinto each small subgroup, we get three groups all of whose members find what they discuss relevant.As the size of any group expands, wastage from general topics that rule out most of the group’s mem-bers grows exponentially. In really large groups of 40 people more, nearly 80% of participants at anyone point in time are wasting their time there.

Variety is nice but tested variety is better. Therefore, breaking large groups into several smallerones and getting those smaller ones to compete in producing some valued outcome, forces variety,represented in the small groups to do work--to be worth something in terms of proposing or achievingoutcomes better than other diverse groupings of people. Moreover, I can use social cleavage dimen-sions--age, gender, profession, background, politics, rank in hierarchy--as a basis for forming sub-groups (different ages in different groups, different genders in different groups, different professionsin different groups, and so on). In doing this I make visible what the worth of particular differencesis, when directed at specific group products.

The vast marjority of work is unedited, in part, because people enter organizations from colleges andschool systems that contain and foster nearly no editing. Students are used to handing in the firstdraft that they come up with (at the last possible moment). Editing is extremely valuable, evenwhen edits are finally rejected by authors making revisions. Merely seeing reactions of differing oth-ers changes contexts for an author and exposes them to differences they need to anticipate in whatand how they write. Moreover there are assignable type of editing--edit the ideas, edit the impacton particular audiences, edit references and citations, edit inclusion of latest news and develop-ments, edit thinkable doubts and weaknesses of argument,evidence, and position, and others. A so-so idea, edited by a succession of such specified edits, turns out much like a work of genius. Editscan take the bare bones of an idea, appearing amid unclear junk and bias, and turn it into a crystalclear breath-taking new idea. Structuring social automata to pass proposals through numerous dis-tinct types of edits, can amaze people with the quality of results that eventuate.

Alternatives are explored as lip service, for the most part, because most of us are so biased and nar-row in outlook. When procedures force us to seriously analyze, apply, evaluate, and measure resultsof alternatives, we find our familiar frameworks and biases changing. Forcing people to treat alter-native methods, outcomes, goals, measures, methods, values, as seriously, comprehensively, and asdetailed-ly as they treat their favored ones, fosters learning and expands outlooks fundamentally.Social automata procedures that assign people to distinct alternatives, or that assign people to finddistinct alternatives then develop or deploy them in great thoroughness and detail, foster learningand get people beyond their common biases.

Not a few cultures value getting the ideas right and slough off getting reality changed based on thoseideas. You find people, leaders, groups, entire cultures that value initiative and heroic posturing,and never get around to the dirty work of changing reality with ideas and postures. Overcoming thegap between word and deed, idea and reality, self and world, is a distinct culture in itself--the cul-ture of implementation. When ideas fail, it is often hard to disentangle failure from wrong idea, fail-ure from wrong implementation approach, failure from poor implementation of the implementationeffort itself. When implementations are always devalued, slighted socially, and sloppy, the worth ofideas stays hidden--ideas are never really tested. Social automata that implement ideas or makeexisting implementations more detailed, thorough, and sincere change cultures from anti-implemen-tation to implementation. They change ideas from vaguely of possible value, to of proven, testedvalue. They change realities not just egos, bragging, and careers.

Most, perhaps all, procedures are reflective at certain steps. Unfortunately, most such reflection iscarping and morale eroding bitching by people, from the beginning treated unfairly by the procedureor the leaders of it. Cultures of “this will never work” get established with amazing rapidity, andbecome self fulfilling prophesy. Leaders used to getting noticed and promoted for launching newinitiatives, never staying around them long enough to see if they work, could care less about thiscarping--they are long gone and promoted by the time implementation fails. Casual amateur reflec-tion tends to erode purposes and intents. What works better is discipline professional reflection,that is not biased or self-serving. Social automata that punctuate their procedures with ocassional

steps for thorough-going reflection, evaluation, data collection and analysis, allow wayward proce-dures or unrealistic steps to be spotted and corrected mid-process, rather than waiting till outcomesare in jeopardy to act.

When large groups are left large, they split via political factions, into bickering ideologically rigidblocks that resist compromise and agreement. When the same large groups, are, from their begin-ning, broken up into smaller subgroups that link and split, dynamically, throughout some complicatedprocess of work, then the subgroupings are various, not mainly political, and ideological bias and big-otry and inflexibility is avoided, allowing differences to combine and coalesce into better overallresults. Social automata that break large groups up early, de-politicize differences and lay thegroundwork for differences to help each other.

The procedures of normal meetings and discussions are nearly entirely amateur and unprofessional.Those few organizations with formal procedures for meeting impose one expert’s vision, usually fromdecades earlier, onto many later generations of meetings and meet-ers. It is better, by far, to searchthe world for who is best at some procedure, make a version of that person’s ways that others canunderstand and apply, and follow such procedures in social automata arrangements, instead of fol-lowing amateur procedures from whoever local is in a meeting or discussion.

We all bandy about words like “leader” as if they mean something and as if they are “normal” andunavoidable realities. When, however, you seriously examine what such words mean, you find that“leaders” are people who tell weaker others what to do, trampelling on insights, talents, interests,and viewpoints of others, to make themselves important, wealthy, and capable of garnering moreand more power from weaker others, in an ever self aggrandizing process. CEOs raping corporationsfor hundreds of millions of dollars a year in compensation for themselves are being natural good lead-ers, as the word “leader” is used by common people all over the industrial world. Leaders are thosewilling to stand up and use others lives and minds for their own personal egoistic purposes. You knowthis whenever you see on TV a CEO talking about how all he and his corporation do is “help the healthcare of others” or “enrich the lives of us all with quality entertainment” or “refresh us all with stim-ulating beverages”. We know the reality is gigantic prices for drugs of marginal benefits and direside-effects, the world’s shittiest ideas and feelings distributed widely to the world’s lowest commondenominator of person, and sugar water wrecking teeth, waiste-lines, and shortening lifespans.Such CEOs are always helping only themselves to hundreds of millions; their bland statements to thecontrary fool no one. Social automata that eliminate leaders entirely from work processes outper-form by factors between a dozen and a hundred the productivity of “leader led” such processes.Plus, there is no ego up front to monopolize all the compensation, credit, limelight, rewards, andpower of the group. Social automata arrangements are a primary vehicle for delivering leadershipfunctions to groups via means other than a special social class aristocracy of “leaders” who monopo-lize policy, thinking, priviledge, riches, and rewards, denuding entire careers, lives, and workforcesof these boons.

Social Automata for Better Contributions from Human Resources

Where does getting better contributions from human resources come from in social automata ways ofwork compared to usual meeting and discussion ways of work?

from formal surveying of member background, confidence, and capabilitiesfrom assignments for growth balancing assignments for productionfrom stripping out un-necessary leaders and un-necessary leadershipfrom combining people not naturally combinedfrom separating people locked in counter-productive relationshipsfrom assigning specific quality ways to do steps normally done sloppily or amateurishlyfrom lowering tensions by procedures working towards achievable subgoals as a route to intimidat-ing overall outcomesfrom social, artistic, emotional tactics that mobilize imagination and whole person feeling for tasksrather than assume rational work cultures from national traditions.

It is amazing but major firms and organizations today, each day, use each person who is employed bythem, without anything like a comprehensive, accurate, detailed map of that person’s interests,ambitions, and capabilities. First, bosses, on all levels, are poorly educated, especially in formaland informal methods of person assessment. Having a dozen assessment professionals in a humanresource department serving tens of thousands or hundreds of thousands of employees is symbolicallymeaningful but practically useless. Assessing one person fully is a big job and takes months, nothours on some test or minutes on some questionnaire. The reality is nearly all employees of all firmsare being used unassessed as to need, interests, capabilities, aspirations. Only the most rudamentaryout of date, rationalistically shallow stand ins for real assessment are done. As a result, for each per-son employeed, less than a tenth their actual present capabilities are employed in present workarrangments and role, making the level of excellence in work achieved at extremely low levels. Thisis nice if you are an ambitious person not wedded to business cultures--you can go into any place inany business and quadruple productivity in weeks for hundreds or thousands of people with very littleanalysis and work. Social automata procedures that start any work process with thorough and intui-

tive, emotive, and socially adroit assessment of all social indexing dimensions of members of the pro-cess thereby insure that they clearly and dramatically outperform usual work arrangements amongthe same people.

Lip service is paid to the idea of role balance in organizations. Role balance is making part of eachperson’s work each week a stretch, calling for more than they presently can do well, while makingthe rest of that peron’s work call for existing abilities, getting quality outcomes from experiencedparts of the person’s skill repertoire. Roles for exploiting present capabilities and roles for develop-ing new capabilities in the person have to be balance in each job to keep people motivated. Unfor-tunately most real jobs do not know enough of a person’s interests, needs, and capabilities to keepthis balance. Social automata the procedures of which start out with thorough assessments andsocial indexing of needs, interests, and capabilities, can outperform usual work procedures byexploiting as well as developing skills in thorough balance.

One of the most harmful parts of existing work arrangements is the dependency fostered in them byhaving fixed inventories of expensive “leaders”, monopolizing power, rewards, budgets, limelight,and information. Especially in Western cultures, such as Anglo cultures in the US, Australia, NewZealand, India, and the like, a tradition of leaders doing all the thinking themselves and forcing theirone idea onto all employees “under” them has arisen. In East Asia this is felt immoral and impossiblyunrealistic by everyone as East Asia has a very different tradition of leaders being figureheads depen-dent for power, budget, and information on their “genba” the ordinary employees who they areassigned to “care for”. This is Confucius’ primary virtue--”li” or benevolence--in action. Leadersare those responsible for care of the group, with direction setting a group function not something theleader imposes on the group. Of course in societies with billions of citizens or hundreds of millionsof them, there are exceptions, but the average Chinese or Japanese manager is morally affronted bythe spiritual brutality of US work arrangements where one “leader” denudes thought from all andmonopolizes ideas for the group to implement “his” ideas only, not their own. Social automata thereplace leaders as a special social class of people with leadership functions that ordinary employeesapply to themselves in workshop procedure form, demonstrate that social classes with special expen-sive monopolies and priviledges are ineffective means of delivering leadership functions.

One of the humorous ironies of modern workplaces is all the computer and network equipment con-stantly being sold, generation after generation, to get people in touch, to get people to communi-cate, to get people together--while the entire thrust of the monkey hierarchy of swinging glands menwho run things is to separate people by function and rank into distinct strata all of which are inferiorto their tops. Technology is applied to un-masculine-ize, un-testesterone-ize workplace made mon-key like by the males of swinging glands who run them. Technology is the primary feminizer of mod-ern workplaces. Indeed, if you examine what re-engineering campaigns in the 1990s and early 2000sdid to existing work processes you find re-engineering technologies feminized work processes andoutcomes, moving them from male adjectives to female ones. Social automata, not dependent onswinging glands “leaders”, can combine people kept separate in departments or firms or layers ofrank in monkey hierarchy organizations. This de-monkey-ization of work combinations allows ranksto mix, speeding up career learning, and allows functions to mix, speeding up other types of learning.

The ideas of--one desk per person, one job per person, the same job each week for years in a row,one company per person--are simply silly. Only people of extremely limited intellect could toleratesuch stunted work arrangements. In fact, when you examine the history of work, you find that thesesimplicities were mandated because “leaders” and “managers” were simply too mentally stunted tohandle and imagine more complicated arrangements of work. The current form of work organizationis optimized to make mentally stunted leaders capable of not getting confused by work arrange-ments--they are not optimized to get work done. Each of these assumptions is assinine--people workbetter than they have multiple desks, one per project, not one per person. Most jobs, if repeatedfive days in a row, produce lower and lower productivity and morale each day--people naturally workfar better when having two or three different jobs each week that compensate for each others’ con-tents and emphases (so that jobs requiring intimate emotional care for people are alternated withjobs that require abstract mental calculation without human contact--so burn out in welfare jobs iscaused by forcing people to spend 5 days a week in personal emotive care work when people can onlydo that 2 days a week without burn out). People naturally need within-current-ability jobs part ofeach year and beyond-current-ability jobs others parts of each year, so they can exercise masteryand so they can challenge themselves and grow. So forcing people to do the same job for years in arow, reduces mastery by lowering morale and boring people and eliminates challenge altogether.People naturally desire to work in different industries each week and year, not in the same industry.Seeing a set of functions from supplier, competitor, customers, regulator, inventor perspectives out-performs by a large margin being squeezed into just one of those perspectives for years at a time.Social automata that mix people beyond present firm, job, level, function, gender, boss, and crossingother boundaries, outperform existing procrustean work arrangement by a large margin.

If you do what I say, I will give you freedom to do it as you wish--this is an implied contract all overthe modern world of work. In Japan this is carried to extremes with great freedom in how to workgiven to ordinary employees so long as they keep quiet about changing anything beyond their own jobin size and scale and importance. In the West, ordinary employees are quite often told what to do

and how to do it, because leaders traditionally view employees as dumb tools to be ordered about byaristocratic betters. The West has strong remnants of European class systems in modern manage-ment arrangements. Social automata where procedures developed from world best experts areembedded in each workshop force participants to follow an expert procedure instead of doing things“as they wish” with amateurish procedures, untested with real data and competition. Employees inthe West resist this greatly--preferring the freedom to decide how by themselves, however amateur-ish the result. However, social automata allows ordinary employees to set what to do and to designhow to do it via finding who is world best and learning form them, as early steps in workshop proce-dures.

The baby steps of genius are small steps, each of which is doable and not too daunting but that addup to something giant and impressive. Social automata are detailed and specific in procedure,replacing amateurish procedures of various untested sorts that we normally default to. This allowssocial automata to set up small steps towards very bold goals, goals too bold for usual work arrange-ments because usual work arrangements, by keeping procedures vague and amateurish, cannot facebold goals--they would intimidate people too much. However, social automata can tackle bold goalsbecause they specify exact procedure steps for achieving them incrementally, keeping each individ-ual step realistic, though perhaps a stretch of one’s existing capabilities and self images. You wouldthink ordinary work processes could do this too, but they never specify procedure detailedly enoughand thoroughly enough, and never ground procedures in studies of world best practitioners and theirways.

Modern work organizations are monkey troops in bananaland, the swinging glands hormone leadershipsyndrome. Modern work is male, through and through. In the US and England, some of the world’sugliest women are being raised--trained for decades to spread their legs making room for swingingglands and the strutting, bragging, over the top, monkey chest bashing that goes for productivity inthe wilds of Borneo and IBM. Males do not see all of any person; they do not wish to see all of anyperson; they are embarred by and fear the feeling and emotive parts of people; they are born, genet-ically predisposed, to war, and sensitive warriors are dead warriors. So modern works organizations,being male, swinging glands in style, omit emotion till it crops up explosively, because ignored toolong. This is management by explosion, practiced absolutely everywhere in modern business wheremales rule. It is an expensive way to manage. Social automata by embodying feminine processes ofwork and stripping out male leadership styles and bragging entirely, create forms of work that use allthe mental and emotional thinking types of people--greatly improving product sensitivity, customerengagement, and market success.

Procedure Scales and Social Automata Design

Consider a procedure, any procedure. We can start with an entire 10 person group doing it step bystep with the whole group going over each step, discussing together how it should be done. We canhave subgroups of 3 persons or 4 persons each, three such subgroups of our group of ten, each do theentire procedure, with the three or four people in these subgroups discussing how each procedurestep gets done. We can have individual people do the entire procedure, by themselves, discussingwith no one, how to do each step. So there are three size scales--entire group, subgroup, and indi-viduals. There are, of course, as many groupings as there are abstract neighborhoods to put peoplein. We can have an entire procedure assigned to pairs, to trios, to quads, etc.

Next consider what happens to people and to procedures as they get done by ten, by 3, and by oneperson or vice versa, done by 1, then by 3, then by 10. When ten people get an entire procedure todo as a group of ten, endless discussion takes place as the group feels out its own contents in terms ofvariety, experience, authorities, trouble-makers, and so on. Inevitable the procedure splits into an“understand us” prior part, and an “understand the procedure” following part. When 3 people get anentire procedure to do, there is the same tendency to figure out dynamics among the members first,but, because there are only three members, these dynamics can be more rapidly identified and set-tled upon. When one person gets an entire procedure to do, there is a process of figuring out howparts of him or herself match or do not match the parts of the procedure. This is not unlike the tenperson group flailing around learning about each other before tackling the task, and the subgroupsfiguring out which two will exclude subtly or overtly a third person member they have. As we allreadily imagine, doing a procedure in a group of ten makes doing it next in a group of three, wel-come; and, doing it in any group makes doing it absolutely alone, more attractive. They each con-text each other and motivate each other. The reverse, doing it first as a lone individual, then asgroups of 3 or 4, then as a group of ten, is not so motivating. Individuals lock onto matches betweenparts of themselves and parts of the task and do not want to let go of these hardwon insights andlearnings. With the start at the group of ten level, the general dissatisfaction that all individualshave with group discussions and meetings reduces commitment to whatever the group does andchooses so that departing later from that is welcome not resisted.

Now consider parts of the procedure, say the first part, done by a group of ten, the middle part doneby a group of 3, and the last part done by lone individuals, with the subgroups of 3 competing orcooperating and the lone individuals competing or cooperating. Of course the usefulness of thisdepends on what kind of task contents the first part, middle part, and last part of the overall proce-dure have. The fit between task trait and size of group doing the task has to match up certain ways

to be attractive. Though the flow from large group to subgroup to lone individual doing is positiveand self motivating in most cases, it may not match aspects of the task. For example, when parts ofa task demand alternatives and variety, it strains lone individuals or small subgroups to produceenough alternatives and variety. It is easier in large groups. When, for a counter example, whenparts of a task demand convergence and choice, focus and depth understanding, then lone individualswill be better--they are able to find quite unique ways through ideas and alternatives and elaboratethen in great detail and depth.

Finally, consider a procedure repeated many times, with some times using a large group of ten, sometimes using subgroups of three, some using lone individuals, some using all three of these in thisorder, some using all three of these in the reverse order, some usilng all three of these but differentparts of the overall procedure done by these three. So the same overall procedure gets done, say,forty times, five times in each of 8 different configurations of task laid over groupings of people.Such repetitions of procedure using diverse layouts of task to person, amount to experiments thatteach groups how best to match task traits to personnel traits to get some wanted kind of result. Itis not so much a matter of finding right answers for all time but finding answers that work well withcurrent personnel and task types.

Exercising Social Automata for Productivity and Better HumanResource Utilization

It is very important when you are first getting acclimated to social automata, to think and feel yourway through to specifying social automata arrangements of this first, freeform, sort, where you havefixed initial conditions and fixed needed outcome, but you are free as to how you obtain that neededoutcome. With the how completely free and unspecified, you have too much freedom and it may beunclear how to proceed. This chapter is simple but useful because the few guideliness given abovefor designing social automata to maximize productivity and to maximize good use of human resourcesare the most basic commonsense for using social automata. If you can imagine automata arrange-ments that get more productivity than usual meetings and discussions and other arrangements thatuse human resources better than standard work arrangements then you can start using social autom-ata to thrive and succeed. That success becomes a reward getting you bolder and deeper in thesocial automata arrangements you invent. Once you get clear benefits from this new way to work,motivation to continue and improve is automatic.

Automata for ProductivityIn the real world, all meetings and discussions, because amateurish and unorganized, traditionaland lorded over by egos and monkey hierarchy dynamics, achieve extremely low levels of thor-oughness and productivity. People who achieve ten or more times what others achieve in thesame meeting time and members, discussion time and members, rapidly progress, and shinethroughout contemporary organizations, rising rapidly in rank, authority, and power. Being ableto use social automata for such increments in productivity is therefore, both important andrewarding. Which of the infinite types of social automata arrangements surpass existing workarrangements in productivity by large amounts? The simple guidelines below suffice.

Though above I contrasted automata for productivity with automata for full use of humanresources, both are routes to productivity. In the first case, it is a task analysis route; in the sec-ond, a human resource identification and utilization route. The second route has a distinct side-benefit--better recognition and use of human resources within each person and among persons.The table below, therefore, takes you through both these productivity analyses.

To design a “productive” social automaton, answer, in order, the 72 questions (4 x 18) below. Forexercise purposes I assign students to answer the following 72 questions for creating a socialautomaton way of getting a group of ten people to, together, design the next ten years of careermoves for each of the ten people in the group. This exercise requires students to examine andimprove their own lives using insights and research work done by other people in a group withthem. How to do this work productively, without just general vague discussions and long-windedmeetings can be a revelation to student who may have never seen effective forms of work before.

Social Automata Designs for Productivity

The Task Analysis Route

What is the procedure thatwill get you from your ini-tial conditions to the finalresult you need? What spe-cific people will do this pro-cedure?

Backward Reasoning: whatis the goal you wish toattain? What steps have tobe done just before attain-ing it? What steps have tobe done just before thosesteps? Continue in thisvein.

Forward Reasoning: whatare your present circum-stances that assist you inthe direction of your even-tual goal? What stepstowards the eventual goalcan you now take fromeach such circumstance?What steps can you takefrom the results of that?Continue in this vein.

Lateral Reasoning: what arethe best of all the competingapproaches for reaching yourfinal goal? Choose the most dif-ferent from each other foursuch approaches and developeach both by forward reason-ing and backward reasoningmaking 8 approaches developedin parallel. Implement thesesimultaneously or in sequence,letting results among themcompete.

Which parts (sets of adja-cent steps) of that proce-dure can be done in parallelinstead of sequentially?

Divide your procedure intosets of adjacent steps thatproduce identifiable par-tial products.

Which such sets of stepsuse the partial product ofthe immediately previoussuch set of steps? All thesets that do not use it arecandidates for being doneat the same time as theimmediately prior set ofsteps.

What particular steps arerepeated in different sets ofsteps which sets produce differ-ent partial products? Can thosesteps be removed from each setand put together as a singlestep applied to a partial prod-uct fairly late in the overallprocedure? If so, parallel doingof such steps later in the pro-cess may be able to replacesequential doing of such stepswithin different sets of steps inthe overall procedure.

When the same set of stepsis being done by differentpersons or subgroups at thesame time, would it be bestto have such subgroupscompete or cooperate,helping each other suc-ceed?

When several subgroups aredoing the same set of stepsin parallel (at the sametime), is the product ofthat set of steps somethingthat can be evaluated usingclear powerful criteria? Ifso, competitive doing maymotivate innovative solu-tions by some subgroups. Ifnot, competition may justinduce bias.

When several subgroups aredoing the same set of stepsin parallel, if the stepsbeing done are challengingto understand or do, shar-ing insights and break-throughs betweensubgroups, may speed upthe work and improve thequality of results of all sub-groups.

What sets of steps in your pro-cedure produce partial prod-ucts that are not really used ornot used at all by later sets ofsteps? What sets of steps inyour procedure produce delaysor waits, where partial prod-ucts lie untreated becausehumans to treat them are busydoing something else else-where? Redesign your proce-dure to not produce partialproducts that are not reallyneeded or used and to elimi-nate all delays or waits forresources, human or other.

What particular evalua-tions, editing types, correc-tion types if applied topartial products or endproducts of your procedurewill greatly improve qual-ity and usefulness of out-come?

What are the most likelyshort cuts, biases, limita-tions, likely to result frompeople doing your proce-dure? What is the pressureto get a good result in timelikely to cause to beslighted, forgotten,neglected?

What alternativeapproaches, values, theo-ries, cultures, points ofview, or stakeholdersaffected by the products ofyour procedure would pro-duce very different “takes”on it or views of it or evalu-ations of it?

What subgroups, one for eachbias/limitation/shortcut andone for each alternative(approach/value/theory/cul-ture/point of view/stake-holder) could be constituted toedit partial or final products inyour procedure so as to restorewhat is slighted, undoing bias,or take into account alterna-tives not explored by the mainprocedure? Set up editingteams for each such check andcorrection.

What alternatives or varia-tions/variants are involvedin your procedure and whatdiverse ways of finding suchalternatives and variantscould be applied in parallelor in sequence by subgroupsto make the overall varietygreat not narrow?

What alternatives are likelyto be thought of by thegroup doing the procedureand what alternatives arelikely to be ignored by orunknown to them?

What biases or shared cul-ture traits of the group arelikely to limit what alterna-tives they imagine andexplore?

What procedures, inserted intothe overall procedure, couldproduce exploration of alterna-tives unlikely to be explored bythe group on its own, actingnaturally and traditionally?


What implementation isinvolved in your procedureand what feedbacks from itand tuning of what isinvolved in doing it wouldgreatly improve its quality?

What changes in actual per-sons, conditions, flows ofresources, events are pro-posed to be done by yourprocedure? For each, howis the appearance ofchange likely to substituteitself for real change?

What parts of your proce-dure take quite some timebefore final results emergeclearly for evaluation?What changes of personnelin those doing such parts ofthe procedure are likely tomake it so no one doing theprocedure part will bearound to see or evaluatefinal results of it?

Redesign your procedure sothat the appearance of changeis never substituted for realchange and so the people doingthe procedure are rewarded orpunished, even years later, iffinal results work well or workpoorly.

What mid-course reflec-tion, evaluation, feedbackwould allow errors and mis-direction developing in yourprocedure to be found andcorrected early before itdoes much harm?

What are the likely errorsfrom each set of adjacentsteps that produce partialproducts in your overallprocedure? What are thelikely substitutions of localbenefits or conveniencesfor moves toward ultimateprocedure goals in the pro-cedure?

Where is the rational workof the procedure mostintense? Where is the emo-tive work of the proceduremost intense? Where isscrutiny by insiders and byoutsiders of the proceduregreatest? What distortionsin the work are likely fromeach of these?

Redesign the procedure so thaterrors, convenience substitu-tions, distraction by rationalintensity, distraction by emo-tional intensity, distraction byinsider scrutiny, and distractionby outsider scrutiny are explic-itly and thoroughly searched forin mid-course reflection ses-sions injected into the proce-dure.

Is the group doing the pro-cedure greater than five orsix in size, if so, what sub-groups can it be split intothat are small enough toreduce politics, factions,ideological rigidity?

What sets of adjacent stepsin the procedure have to bedone by the whole group--absolutely? Have all sets ofsteps except these, done bysmall subgroups of 3 or 4 nolarger.

For sets of adjacent stepsthat have to be done by theentire group as one, whatfactions are likely toappear and fracture thegroup during those sets ofsteps? What counter-meaures, in your proce-dures, can prevent thesefactions from ideologicalbickering and pigheadedselfish conflict?

Redesign the procedure to havea rhythm of big group work tosmall group work to big groupwork to small group work.Decide how many “beats” tohave in the entire procedure--how many big group points,how many small group pointsbetween big group points, howmany big group points betweensmall group points? Decide the“amplitude” or loudness--howbig should the big group pointsbe and how small should thesmall group points be?

Is the source of your proce-dures for this workshopcasual, informal, or ama-teurish in other ways, if so,who is best in the world atdoing such work--can youget procedures from whatthey write or from directlycontacting them?

Design the entire procedureyourself with good peopleyou know and work with.Perfect your version of thisusing library, internet, ref-erence writings and consul-tations with people youalready know. Edit yourprocedure till it is as per-fect as you can make it.

Find the best 5 people inthe world at doing this kindof procedure--this may takeconsiderable phone andinternet research. Readwhat they have writtenthoroughly getting ideas forprocedure improvement.Then contact each of thempersonally with furtherquestions or a general wellstructured interview. Usethis to perfect a new ver-sion of your procedure.

Compare your own personalbest version with the versiongotten from contacting the best5 people in the world at yoursort of procedure. How arethe two versions different?What does your version havethat is better than what theexpert version has/ What doesthe expert version have that isbetter than what your versionhas? Where does your pride andego in your own version andwork blind you to possibly bet-ter ideas in the expert’s version(it is vital to get a thoroughwritten version of this). Makea new version combining thebest of both.

The Person Capability Utilization Route

What is the procedure thatwill get you from your ini-tial conditions to the finalresult you need? What spe-cific people will do this pro-cedure?

Backward Reasoning: whatis the goal you wish toattain? What steps have tobe done just before attain-ing it? What steps have tobe done just before thosesteps? Continue in thisvein.

Forward Reasoning: whatare your present circum-stances that assist you inthe direction of your even-tual goal? What stepstowards the eventual goalcan you now take fromeach such circumstance?What steps can you takefrom the results of that?Continue in this vein.

Lateral Reasoning: what arethe best of all the competingapproaches for reaching yourfinal goal? Choose the most dif-ferent from each other foursuch approaches and developeach both by forward reason-ing and backward reasoningmaking 8 approaches developedin parallel. Implement thesesimultaneously or in sequence,letting results among themcompete.


Design a well structuredquestionnaire ascertainingall the interests, needs,capabilities, relevant con-tacts and other personalresources for the task athand, of each member ofthe group doing any and allparts of the task.

Structure your question-naire so as to ascertainwhat unique and powerfulcapabilities and interests ofeach person must be fullyleveraged and used in youroverall procedure if bestresults are to be gotten.

Structure your question-naire so as to ascertainwhat unique and powerfulweaknesses of each personmust be fully avoided orblunted in your overall pro-cedure if best results are tobe gotten.

Structure your questionnaire soas to ascertain what nascent orwanted abilities, not nowpresent in each person, couldthey develop in your procedureif challenged to do certainsteps a little beyond theirpresent capabilities? Whatresources or help might assistthem in meeting such chal-lenges so they do not fail andhurt the overall procedureresult?

For each member of thegroup identify which stepsin your procedure: fit anexisting powerful capabil-ity or talent they have, arein danger because the per-son has a weakness at doingthat step, challenge aweakness the personacknowledges and is moti-vated to fix.

Which steps have no or fewmembers of the group tal-ented at doing that sort offunction? Which stepshave more talented peoplethan needed available fordoing them? Which stepshave no one or few chal-lenged by them at fixing aknown weakness? Whichsteps have too many peoplechallenged by them at fix-ing a known weakness?

Which people have few orno steps using a known tal-ent or powerful capabilitythey already have? Whichpeople have few or no stepsdepending on them forfunctions they have aknown weakness at? Whichpeople have few or no stepschallenging them at a pointof known weakness?

Redesign your procedure sothat all steps have enough tal-ent to succeed but enough chal-lenging going on to develop newskills in the group. Redesignyour procedure so that allmembers have enough use oftheir talents and enough fixingof their known weaknesses.Redesign your procedure sothat all members, as far as pos-sible, are not asked to do func-tion they are too weak toactually do, unless it is a knownweakness they are highly moti-vated to fix.

For each step in your proce-dure, are there leadershipor management functionsinvolved or needed by thatstep. Identify all stepsneeding leadership or man-agement functions--identifyfor each of them exactlyhow much of what leader-ship/management functionthey need.

For each such needed man-agement/leadership func-tion, what are the steps bywhich local leaders/manag-ers do such functions?What are the steps bywhich people best in theworld at that function, doit? Compare the two anddefine a best procedure forexecuting that leadership/management function.

For each set of adjacentsteps in your overall proce-dure that produce an iden-tifiable partial product,identify steps there need-ing leadership/manage-ment functions. Ratherthan assigning the doing ofsuch functions to a fixedlimited social class called“leaders” or “managers”instead assign the doing ofsuch functions to particu-lar members or to sub-groups or to subeventswithin the overall proce-dure.

Assign particular members orsteps in your overall procedurethat at regular intervals of timecheck for whether any particu-lar leadership or managementfunction is needed there?When such functions are foundneeded, have a procedureavailable already defined forordinary members doing suchfunctions, rather than depend-ing on a special social classcalled “leaders” doing it. Haveprocedure steps near the endthat evaluate: 1) were alltimes/places where such func-tions were needed identified intime? 2) were all such neededfunctions delivered wellenough?

Which members of youroverall group do not natu-rally meet and worktogether? Which memberof your overall groupbelong to different func-tions, professions, genera-tions, firms, genders andtherefore think and act inincompatible or conflictingways?

What members of the groupcan be combined in pairs?in subgroups? so as to puttogether people not nor-mally together and peoplefrom different professionsand stakeholders?

What sets of steps in theprocedures, if done by suchgroupings, would benefitfrom their differences andlack of familiarity witheach other? What sets ofsteps in the procedures, ifdone by such groupings,would be harmed by theirdifrerences and lack offamiliarity with each other?

What overall sequence of newpeople to work with, does eachmember of the group face inthe overall procedure? Whichpeople work with mostly or onlyfamiliar persons? Which peoplework with mostly or only unfa-miliar ones? What balance ofworking with familiars/unfamil-iars is best for each person?How can the procedure assign-ments be designed to achievethat optimal balance for allmembers?

Which members of theoverall group usually worktogether? Which membersof the overall group whousually work together haveexisting unproductive orotherwise hostile/under-performing relationships?

Which members shouldnever work together at anypoint during the overallprocedure?

Which members should sel-dom work together atpoints during the overallprocedure?

Which members should fre-quently work together at vari-ous points during the overallprocedure?


Social Automaton Types for Fixed Product,

Initials, Behaviors, Neighborhoods; Free Flow,

Reflexivity, Tuning, Partials, IterationWhen product, initials, behaviors, and neighborhoods are fixed, you have a defined procedure anddefined basic units combined into neighborhoods, to do a defined transformation from the fixed ini-tial conditions to the fixed outcomes needed. So the purpose of the social automaton is well speci-fied and fixed and the procedure/skills of the basic units and the organization of units intoneighborhoods is fixed. This is like a complete, specified machine doing a complete, specified task.What is there left to vary, one might wonder? The flow of partial products among units and neighbor-hoods, the partial products produced by them, reflection by the automaton on its performance andhow it tunes it performance as a result--these are free. Also iteration, whether it is few and slow ormany and fast, is free. It is a fixed machine whose performance flow, iteration, and tuning is flexi-ble that this chapter presents.

What are these sorts of social automata good for and used for? Well, we already have the answerbecause there are lots of these automata in the real world that we all have experienced already.Any fixed social structuring of units facing fixed unchanging tasks fits this chapter’s type. All thatone can do is get it to perform differently than others. The whole meta-dimension of watching howit is doing and tuning performance parameters to adjust that performance you monitor is used to getexcellence from a fixed task done by a fixed social format. Most of the bureaucracies of the worldare exactly this.

For each set of adjacentsteps in the overall proce-dure that produce an inden-tifiable partial product,which such sets involve par-tial products usually donecasually, informally, slop-pily now?

For each set of adjacentsteps done casually, infor-mally, sloppily, or amateur-ishly now, what particularchanges in procedure canget it done carefully, for-mally, excellently, and pro-fessionally?

Who is best in the world ateach such set of steps?How does he or she do suchfunctions? How does thatdiffer from your own bestspefication of doing suchsteps?

Redesign the sets of steps sothat sets done sloppily end updone with very high quality, atworld best levels.

Which sets of adjacentsteps in the overall proce-dure involve partial prod-ucts that are daunting,difficult, hard, challeng-ing, or otherwise intimidat-ing to the members of thegroup?

Split each such set of stepsinto two or more simpler todo sets of adjacent stepsthat each produce partialproducts that can be com-bined into the overall firstpartial product that intimi-dated?

Check that those new setsof steps and their new par-tial products that add up,do not intimidate membersof the group. If necessaryrepeat and break these newsmaller sets of steps intosubsets that are smallerand simpler still till you getpartial products, 2, 3, 4, 5,or more of which add up tothe original one that intimi-dated but each of which,individually, does notintimidate.

Check that bold challengingoverall goals are done via setsof steps that each producechallenging but clearly do-ablepartial products. The miraclewe want is the impossibleachieved by being broken downinto do-able sets of steps.

Identify for each step ofthe overall procedure andeach member, at eachstep, how that step usesthe social, artistic, emo-tional capabilities, needs,and interests, of eachmember.

Identify all steps that omitor barely use the socialaspects of all members, theartistic aspects of all mem-bers, the emotional aspectsof all members. Identify allsteps that fully use thesocial aspects of all mem-bers? the artistic aspects ofall members? the emotionalaspects of all members?

Identify all members whosesocial aspects are largelyunused by any steps, whoseartistic aspects are unusedby any steps, whose emo-tional aspects are unusedby any steps. Identify allmembers whose socialaspects are fully used bysome steps, whose artisticaspects are fully used bysome steps, and whoseemotional aspects are fullyused.

Redesign the overall procedureso that there is a balance suchthat all members have theirsocial, artistic, and emotionalaspects fully used by somesteps in the process and suchthat all steps have power fromdeployment of the social, artis-tic, and emotional aspects ofseveral members.


Fixed Initials and Product

This is a fixed task environment with pre-determined initial conditions that get transformed intospecified outcomes (products).

Fixed Behaviors

This is a fixed procedure for getting from initial conditions to outcomes needed or a fixed skill set interms of what the basic units are capable of doing.

Fixed Neighborhoods

This is basic units configured in some fixed way. Usually, in the real world, this is a workgroups con-sisting of assignments given to individuals, with a fixed flow of partial products from one individual toanother. Sometimes this is two or three individuals feeding the same type of partial product to athird or to a third and a fourth person, who passes it on to others for editing or other work. At alarger scale this could be teams as basic units configured so that some teams feed partial products toother teams for further processing.

Free Reflexivity, Tuning, and Iteration

All systems notice what they are doing and how they are performing, and, based on that, makeadjustments. If there is iteration involved they can adjust the speed and amount of iteration.

The Challenge of the Fixed Structure Fixed Task Automaton Types

The challenge is getting great performance and great quality outcomes from a fixed task done by afixed structure. You get to adjust the flow of partial products among basic units but you are not per-mitted, for whatever reason, from changing the basic units, in number, or size, or composition. Youget to tune by adjusting system-wide parameters like the degree of connectedness, diversity, anddeployment of initiative taking, and amount and size of iteration in the system.

Anyone who has managed a bureau in a bureaucracy has faced exactly this problem--getting greatperformance or outcomes from fixed tasks done by fixed structures. There is, therefore, a built upset of approaches that work.

1. identify the critical, hardest-to-do, biggest-impact-on-outcome steps in the process2. identify the most capable basic unit of persons in skill and motivation and flexibility terms3. assign the critical task steps to the most capable unit4. intensify monitoring of the doing of less critical tasks by less capable units5. reallocate tasks to units if problems occur in less critical tasks/units till good matches are found.

It would not be an exaggeration to say that the above five steps encompass 80% of all managerialactions in most of the world. Finding best persons and matching them with most critical tasks is thelowest level of managerial competence in the world. Few managers rise above it.

Other Approaches Beyond Best Unit Assigned to Most Critical Task

Alternatives include the following:

1. two ordinary units assigned to the most critical task; all the other units assigned to the remain-ing less critical ones--here extra manpower is supposed to help the most critical task get done well2. two ordinary units assigned to each separately or competitively do the most critical task; allother units then meet to choose which unit’s output to use at each time interval and they split theremaining tasks among themselves--here competing alternatives are trusted to handle the mostcritical task3. outsourcing--expert outsiders hired to handle the most critical task while the automaton han-dles all the remaining tasks--here contracts with outsiders are trusted4. split the most critical task into as many pieces as there are basic units, then have all basic unitseach do one piece of the most critical task, then they all switch to handle their own assignedremaining non-critical task--here the whole group splits the most critical task and handle theremaining non-critical ones as well as a later or earlier phase or work.

These are not all alternatives by any means but they suffice to show how freedom to change partialproducts from basic units amounts to a powerful freedom indeed. The number and exact nature ofthe basic units that get assigned to do critical functions is an important variable for controlling theoverall effectiveness of any group.

Beyond Partial Product Reassignment, Excellence from Tuning Alone

Rather than try to design a great social automaton ahead of time, there is the approach of startingout with any decent sort of social automaton design and by careful monitoring of how it is doing, thatis, via a robust reflectivity system, noticing what it needs and making mid-course adjustments. Keyhere is what to monitor and what to tinker with; what to notice and what to tune.

What is there to notice about a fixed arrangement doing a fixed task? It is usually a matter of thehappenstances of human motivation rather than human talent distribution. Since the arrangementsand task are fixed, human talent is usually more or less adequately allocated to subtask types. It ismore often a matter of the ups and downs of human interest and motivation--sheer normalcy andrepetition cause humans to become bored. Most bureaucracies never take this into account--”it istheir job to do their work well” masters, leaders, and managers say. We design rote, routine, repe-titious, boring work arrangements then dictate that people do this work well--this is always andeverywhere a delusion of remote, incompetent leaders. Tuning of work arrangements, anyway, willnot work, will add nothing, if motivation is slack, lacking, or entirely dead. First we need motiva-tion, then we direct it smartly via better work arrangements.

There are entire libraries written about how to improve motivation so I will not present a model of ithere. Suffice it to say that all work arrangements are neurotic, partial, the product of narrow peo-ple pursuing narrow views from their own comfortable habits, traditions, and cultures. Take intoaccount all the alternatives they never imagined or experienced in their habits, traditions, back-grounds, and cultures and you have an enormous repertoire for motivation improvement. Say, then,we get a group motivated; how to direct that better?

Connectedness is a key parameter of tuning work arrangements. Some types of work require moreisolation; others require more connection. Diversity is another parameter--some types of workrequire a lot of diversity; others require uniformity. Initiative taking is a third parameter--sometypes of work require a lot of people taking a lot of initiative; other types require one dictator doingall the initiative taking on behalf of everyone else. You, then, motivate people, then tune connect-edness, diversity, and initiative taking distribution to direct that new energy.

Exercising Fixed Task, Fixed Structure, Excellence from Reflectionand Tuning

Find a Bureau and Improve ItEveryone learns this sort of social automaton the same way--getting assigned to a boring, routine,dead, bored fixed work group handling a repetitious boring task and being asked to improve itsperformance greatly. To beginners this appears at first not only daunting but impossible.

1. Find a group assigned already to do a boring fixed task in a boring fixed way getting boring fixedresults. 2. Find a reward to provide the group that they will enjoy

a. free time--rearrange work so that everyone instead of working five days a weekonly has real work to do 4 days a week (keep the time you save secret)

b. money--rearrange work so that expenses are a few thousand dollars less permonth then spend the difference on parties and trips for the group (care-fully disguised so as to appear work related boring tasks)

c. control--rearrange work so that employees themselves choose which of severaldifferent work arrangements to try any given week, and they tune the cho-sen way’s performance--reward them with control over their work struc-tures and approaches

3. Find alternative partial products for each basic unit to produce so the matching of units to sub-task types can be varied and experimented with. Keep in mind partial product assignments thatproduce the free time, money, or control you promissed your group. 4. Systematically try out the alternative partial product assignments to units that you and employ-ees most like to see with data which works best.5. Do the same with various tunings of connectedness, diversity, and initiative taking distributionin the group by trying out various tuning settings and seeing which work best. Keep in mind tun-ing types that achieve the reward type--free time, money, or control--that you promissed yourgroup.

Surreptitiously Tune the Performance of Groups You are Already InThis is a lot of fun and one of the main ways that people become leaders--by leading invisibly with-out any overt position, authority, or authorization. Learn to yourself diagnose motivation prob-lems in the group and indirect sequences of conversation that get group members interested inchanges that produce more free time, money, or control for the group members. Then do thesame distributed conversation paths to get people more connected or less connected, morediverse or less diverse, more initiative or less initiative taken.

1. Develop the habit of talking to each and all members of the group, by seeing each one, one at atime, in order, perhaps at coffee stations, lunch, or after-hours meetings at bars or sports clubs. 2. Once every two days talk to each person in the group, at first in purely casual ways, till theystop noticing you and these conversations.3. Do a talk circuit among all members of the group, asking each person about changes that mightproduce more free time for them, or more money for them, or more initiative and control forthem. Find which of the three they now like the most and what changes they might imagine wouldmove them towards that reward.4. Do a talk circuit among all members of the group, promoting a consensus on what reward theyall are interested as a whole in seeking and on what changes in work arrangement connectivity,diversity, and initiative distribution might achieve it.5. Implement the reward via the changes consensed on by the group in your conversation circuitamong them.

Social Automaton--Some Design Dimensions:

Parallelism, Blending, CleavageThe Granularities of Parallelism, Blending, and Cleavage

These three--parallelism, blending (versus contending), and cleavage using (versus ignoring)--areamong the main design dimensions for social automata. If I tell you an automaton is highly parallel,highly contentious, and uses all possible cleavages, you, from that description, know most of whatyou need ultimately to know about it. All three of these dimensions have degrees, expressed best asa granularity for each.

The granularity for parallelism is how many parallel work streams are in existence at once, given thelimitation of a certain number of people, overall, in the automaton. A low granularity parallelismarrangement would have two groups working in parallel on the same task. A moderate but still lowgranularity parallelism arrangement would have ten two-person-each pairs working in parallel on thesame task. A moderate but almost high granularity parallelism arrangement would have ten two-per-son-each pairs working in parallel on the same task. A high granularity parallelism arrangementwould have ten two-person-each pairs working in parallel on different tasks at the same time. Twoseparate dimensions of work can be parallel--task can be sequential or parallel, being done one afterthe other or at the same time, and people can work sequentially or in parallel, having one person doa task at any one time or having many people do that task at any one time. So granularity of paral-lelism goes from low person-usage and taks-usage parallelism both together to high person-usage andtask-usage parallelism together with intermediate settings that are mixed--low person parallelismmixed with high task parallelism and vice versa high person parallelism mixed with low task parallel-ism.

The granularity for blending and contending is how much of overall time or task work steps is cooper-ative versus contending. I can have 90% contending with 10% cooperating or any other ratio betweenthem. Lots of cooperating is called for by procedures that people naturally do not exaggerate, lieabout, or foster illusion about. Less cooperating and more contending is called for by proceduresthat naturally call for lots of exaggeration, lying, or illusion. When I can trust people to be honestand accurate cooperation is called for. When I cannot, contension exposes exaggeration, dishonesty,and illusion.

The granularity for cleavage usage is the total number of cleavage dimensions used and the degree ofemotional depth of the ones chosen for usage. I can divide a group into old and young, male andfemale, wealthy and poor, ambitious and lazy, and various other dimensions. Each cleavage dimen-sion (the use of “cleavage” here is from political science and voting behavior research) has its ownemotionality and comfort level. Splitting a group by old and young may irritate some people butsplitting a group by lazy and ambitious will anger a great many people, perhaps everyone. Whichcleavage trait you use to split a group has a direct bearing on the feasibility of doing work, and thefeasibility of splitting the group at all. Little cleavage uses obvious non-threatening not-so-emotion-ally laden traits for splitting the group. High cleavage uses less obvious, more threatening, emotion-ally charged traits for splitting the group.

How Much Parallelism, Contention, Cleavage to Use

The table below summarizes high and low values for each dimension and when to use these extreme

Three Design Dimensions and When to Use Particular Parameter Values for Each of Them

Parameter Parame-ter Value

Definition When to Use

Parallelism:

the purposeof high par-allelism isgeneratinglots of alter-natives tolater evalu-ate or com-pare, usefulwhen groupsare new to adomain ortask (varietycan comefrom varietyof persondoing a task--person paral-lelism--orvariety oftasks peopleare doing--task parallel-ism)

low per-son paral-lelism

few subgroupssplit a group into a few sub-groups working in parallel witheach other

when the task’s steps are not soeasy to do and need more than oneperson for some of the work

high per-son paral-lelism

maximum subgroupssplit a group into the maximumnumber possible of subgroupsworking in parallel with eachother

when the task’s steps are simpleenough that one person alone orfew alone can do them well withinthe time allotted

low taskparallel-ism

different tasks per groupeach subgroup of a group workson different task than othersubgroups at the same time

when different approaches/meth-ods are being tried for the sameend, that is, when we do not knowwhat method is best for a task

high taskparallel-ism

same tasks per groupeach subtroup of a group workson the same tasks as other sub-groups at the same time

when we know the best approach/method for a task and expect vari-ability from who the people aredoing the task to be a quality threator a quality benefit

Cooperation/Contention:

the purposeof high con-tention isunearthingburriedlatent con-tents thatpeople natu-rally suppress

low coop-eration,high con-tension

looking for faultsmost tasks/steps done withother subgroups challenging/correcting/editing work ofother subgroups

when the steps of a task are func-tions that people naturally exagger-ate, are dishonest about, or haveillusions/neuroses in handling

high coop-eration,low con-tension

looking for positivesmost tasks/ steps done withother subgroups assisting andsuggesting improvements inwork of other subgroups

when the steps of a task are func-tions that we can trust people tonot exaggerate, be dishonestabout, or have illusions/neuroses inhandling

values.

The Power of Parallelism, Contention, Cleavage

Daihatsu, a Japanese car company now a part of Toyota, removed usual management from a cardevelopment group and replace them, with all women. Remarkably, these women produced a cardesign utterly unlike anything the company, and its customers, had seen before. It was a van with aheart like shape, when seen from the side, and a smiling face appearance when seen front on. It wassold as a limited edition vehicle to women owners of small businesses and sold out rapidly, gettinglots of free media attention for the company and for those women customers. No men would havedeveloped enthusiasm for heart shapes and smiles in car designs--only women could have done thisdesign. All the product design groups now operating in the world who are a mix of usual companyemployees, are, thereby, dominated by male view and values. Even bra and female underwear com-panies typically have women designers proposing designs approved by men at the top having finaljudgement--a way of emasculating the women and turning them into decor. This is a story about thepower of cleaving a group by gender--male auto design teams versus female auto design teams.

Canon created analog technology teams and digital technology teams competing to generate amachine design. Thus Canon did not protect analog designs from technology forces and did not pro-tect analog designs from market fores. Canon made the transition from analog to digital copymachine and office equipment without the huge drop in profits and customers that Xerox and Kodakexperienced. For Xerox and Kodak protected analog sales, because of their high contribution to rev-enue, till revenue dropped hugely, while Canon let each design team pair--analog team competingwith digital team--fight out the overall business case for their design, product by product, making thetransiton from analog to digital smoothly without huge sudden market share drops. This is the powerof small scale parallelism of effort--spending double to let the old technology and the new competefor each business case and product opportunity.

I set up artificial intelligence circles programs in drug companies of New Jersey, as a Cooper andLybrand consultant. Where my bosses sold one or two huge million dollar projects per year, takingmonths to specify and negotiate each project due to its complexity, I decided to sell 30 much sim-pler, faster, smaller, less risky artificial intelligence projects by recruiting 100 proposed software cir-cles from across an entire workforce, choosing the best 30 proposals in a one day workshop event,and providing 4 hours a week of knowledge coding training per circle and 4 weeks of professionalsoftware coding assistance per circle, during an 18 month project completion timeline. I sold two ofthese programs a year at $3 million each ($100,000 per circle project) while my bosses sold at most$2 million during the same time. My 30 circles were done in 18 months with little risk and muchlearning by circle members; their projects were often into year 3 or 4 before being finished andalmost half of them never completed due to manager, priority, or budget changes. The 30 com-

CleavageUsing/Ignor-ing:

the purposeof high cleav-age emotion-ality isgetting psy-chically deepforms of vari-ety intodesigns/work; thepurpose ofhigh cleav-age numberis using allthe varietyamong mem-bers of agroup

low cleav-age emo-tionality,low cleav-age num-ber

few obvious groupssubgroups for each of a fewtraits of a group that are notthreatening or emotional

when a few particular obvious kindsof variety among people in a groupcan help the doing of a task, inject-ing useful variety in how it is done

low cleav-age emo-tionality,high cleav-age num-ber

many obvious groupssubgroups for each of manytraits of a group that are notthreatening or emotional

when just about all the variety in agroup of persons can help the doingof a task, injecting useful variety inhow it is done

high cleav-age emo-tionality,low cleav-age num-ber

few threatening groupssubtroups for each of a fewtraits of a group that arethreatening or emotional

when a few specific kinds of varietyin people that they normally hide orsupress have great possible benefitfor the doing of a task

high cleav-age emo-tionality,high cleav-age num-ber

many threatening groupssubgtroups for each of manytraits of a group that arethreatening or emotional

when nearly all kinds of variety inpeople that they normally hide orsupress have great possible benefitfor the doing of a task

Three Design Dimensions and When to Use Particular Parameter Values for Each of Them

pleted circle projects then competed against each other for roll out funding--each making its owncase and critiquing the cases made by other circles. Top management had 30 completed systems toevaluate instead of 30 vague ideas. This was the power of contention--ideas critiquing other ideas.

Learning to Choose Parallelisms, Contentions, and Cleavages

We are not used to using the parallel work possibilities in a group of people. We are not used to usingthe critique possibilities in a group. We are not used to using the trait differences among membersof a group. Parallelism opens the door to critique and trait difference usage. When we envision a 12person group as six pairs, working on the same steps at the same time or working on different stepsfor the same end at the same time--we can also envision having the pairs critique each other’s work,or support each other’s work, and we can envision having each pair specialized by age, gender, pro-fession, ambition, or other task-relevant trait. We can use variety when we parallel-ize the peopleor the tasks we are doing. Parallelism opens the door to using variety in a number of powerful ways.Most groups, by not using parallelism, only nominally and rather randomly use any variety in them.Often variety comes out as sheer incoherence in their views and accomplishments.

Whenever we have twelve people for one hour or eight people for two hours, are we using their brainpower at a rate and intensity such that they leave our meeting having learned more and done morethan working alone by themselves they could have done in the same time period? This is the qualityof being together question for me. My leadership’s quality is measured, in part, by whether I usepeople’s minds and hearts, when having them together, as well and intensely as people use their ownminds and hearts alone. By far most meetings in my life have slowed me down, reduced my learning,bored me to tears, wasted my time on monkey rank competitions among male monkeys. Socialautomata arrangements solve this quality of meeting and quality of using people’s talents in meetingsproblem.

Fractal of Contents

Are You Creative?60 Models of Creativity and the 960 Ways to Improve Your Creativity that They Suggest

By Richard Tabor Greene

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KNOWLEDGE

EVOLUTION

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FractalRecurrence

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60Models of

CreativityCopyright 2002 by

Richard Tabor GreeneAll Rights Reserved

US Government Registered

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CATALOGRecommen-dations

Traits

Question Finding

Darwinian Systems

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These models aregiant collectionsbased on single themesor matrix frameworks.

These modelssee creativity as social relations & dynamics of sorts.

Thesemodels see

group-nessmaking

procedurescreative

Thesemodels see

dynamics amongideas themselves that

become creativity

These models seesolids disolved into hypotheses making creativity.

These modelssee non-linearaspects of realitythat make creativityinevitable inthe universe

Thesemodels see

some singlefunction or

aim that donepurely resultsin creativity.

Thedynamics

of creatinga self if

extendedresult increativity.

Within the mind dynamics or constructs that “create”.

Thesemodels are

mixtures, blends,and combinations

of things.

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Page 8 Overview of the 60 ModelsPage 22 Catalog Models HexagonPage 32 128 box Recommendations ModelPage 70 to 74 Scientific Creativity Quad-HexagonsPage 93 48 Culture Dimensions TrianglePage 100 Career 64 QuadPage 114 64 Social Processes QuadPage 121 Catalog Models CombinedPage 124 Blend Models HexagonPage 173 Power Creation Quad 64Page 168 Social Automaton TablePage 202 Blend Models CombinedPage 205 Social Models HexagonPage 260 Cluster Creation SpacePage 281 Social Models Combined

Page 533 System Models HexagonPage 559 Simonton HexagonPage 573 System Effects 128 TrianglePage 579 326 System Effects HexagonsPage 604 Adjacent Beyond 64 QuadsPage 641 Systems Models CombinedPage 644 Purity Models Hexagon

Page 711 Creation Engineering 128 TrianglePage 712 Purity Models CombinedPage 716 Self Models HexagonPage 805 Composing/Performing 128 TrianglePage 806 Self Models Combined

Page 809 Mind Models HexagonPage 876 Mind Models CombinedPage 879 Flow Diagrams of All 60 ModelsPage 982 Grounding Example Tables for All 60 ModelsPage 1056 ReferencesPage 284 Group Models Hexagon

Page 329 General Computation Page 331 Social Process Event Types

Page 332 Ways Orgs Learn 64 QuadPage 336 Organizing for Meta-CognitionPage 344 64 Ways Orgs Learn TablePage 367 Rise of Science TablePage 373 Group Models Combined

Page 376 Knowledge Evolution HexagonPage 444 96 Simple Program IntuitionsPage 457 Knowledge Evolution CombinedPage 461 Experiment HexagonPage 530 Experiment Models Combined

Introducing:

Fractal Concept

Models

Covers:Kaufmann’s InvestigationsWolfram’s Simple ProgramsAbbott’s Chaos of DisciplinesSimonton’s Origins of Genius

and many othersRoot-Bernstein’s Discovering

A Self-Study Text

on Creativity that Any

Person & Any Course

in Any Discipline

in Any College or

Company Can Use

Today!

The MostComprehensiveBookon Creativitythat has Ever beenPublished!

The Next StageAfter R&DArts & Sciences

Individuals & Groups

NGOs & Companies

Ventures & Styles

Fashion & Tradition

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60 MODELS OF CREATIVITY Brief Paragraphs for OVERVIEWING by RTGreene Copyright 2012Set of 10 Which of 60 Paragraph description of each of 60 models NOTE SPACE

CATALOG Recommendations

1. I make a creative life by making interior and exterior room for creating and by embracing paradox enough to do wide-ranging mental travel, then that creative life I built builds a creation machine consisting of myriad small subcreations of tool and motive and then uses that machine to make actual history-changing creations. That machine makes my thinking terribly productive, to which I add conquering blocks that appear, and pruning noise from signal to reveal emergent creations.

Traits 2. I collect traits that creative people, works, domains, and fields have, organize them, and regularly review them to improve the creativity of my work, including: problem finding,bricolage, violation, ingenue sources of variation; metaphor, abstraction, index events, and multiple worlds sources of combination; counter intuition, multi-scale articulation, aesthetics, and saturated fields sources of selecting variants; explanatory power, tool and frame invention, question invention, and zeitgeist tipping points sources of reproducing.

Question Finding

3. I collect ways that creative people find great questions to tackle, organize them, and regularly review them to improve the creativity of my work, including: reverse, depersonalize, undo successes, expand models sources of finding opportunity/gaps; fully represent, socially index, seek intersections, index sources of idea leverage; change measures, scales, adjustments, models sources of re-representing issues; relate, imply, unify, cross-fields sources of change of logic.

Darwinian Systems

4. I notice how persons and works in my domain, and how my domain itself and the people who run it, all four, foster the basic evolution functions of variation, combination, selection, and reproduction. I use the result to position myself for maximal creativity.

Combined Thought Types

5. I select certain types of thinking and develop them individually as well as exploring possible combinations of them till creativity results, including: thought inputs like empathy, doubt, observation, pattern recognition; thought operators like associate, arrange, play, represent; grounding ideas like abstracting from cases, concretly apply to cases, extrace from routines, turn ideas into routines; outputting thoughts like images, analogies, models and syntheses; and combinations of all the afore-mentioned types of thought.

Garbage Can 6. I use nearly all fundamental parts of my existence from personal identity to social dynamics around me to ways of work to develop partial creations of life and work style that become tools for making creative works, including four cycles: my creations change the environments I work in which change what I create, my creations change my career and the cultures I work in which change what I create, the subcreations of tool and method and motive I create change the amount and type of mental flexibility and breath I apply which change subcreations I invent, my creations change my process of creating which changes what I create.

BLEND Culture Mixing

7. I use the various cultures I have been exposed to, have within me, or live among now, blending them till creation emerges; this involves: several ways of mixing cultures applied to the culture of several aspects of life and work (gender, era, devices, nation, etc.), the results of that steeped till mixed cultures start to penetrate each other, producing four creativity effects—repertoire of ways expansion, exercise in spotting and handling surprise, vacuum of old ways drawing out new unused potentials, metaphors mapping things in one culture to things in others.

Discipline Combines

8. I use the various fields I have been exposed to, have mastered, or live among now, blending them till creation emerges, by: seeing the intellectual forces behind fields that generate and kill them, by seeing how new ways to access the world and data generate fields and changes in fields, by mapping similarities/differences among fields, using multiple field views and methods applied to any one case, choosing unlikely fields to apply, using the questions/methods/tools/results of other fields, combining fields in various ways, and spotting and avoiding meretricious superficial fields and methods.

Tuning 9. I position myself between extremes and polar opposites, tuning my approach toward subtle points between extremes where creativity happens, tuning—relations between me and my field of work, between me and each of my works, between levels of detail in my works, between thoughts in my mind—optimizing A while keeping costs of A acceptable and keeping minimal levels of B for all As and Bs in trade-off design relations in my process, work, and the people in my field encountering my work.

Paradox Doorway

10. I seek out paradoxes and force myself against them till they, in turn, force my thinking out of its ruts and into lateral, peripheral new paths that open up creativity to me; this involves: paradox generators (circularity, assumeds, anomaly, incompletenesses; negation-caused-gaps, anxiety defenses, social origins of identity, costs of talents/skills) forcing on me self anihilation, which opens new associative breadth (peripheral seeing, mental laterality, forced abstraction and alternatives, forced emotional laterality—jokes, forced framework repertoires and balances among their aspects), which, if I have the courage to see it, exposes the non-linear nature of reality to me (path dependence, message stickiness, centrality illusion, magic solving fetishes around and in me), making me creative.

Scale Blend 11. I seek out phenomena on multiple size scales, aligning them by similarities of various sorts, till phenomena on one size scale solve major problems on other size scales: theorize on all scales, model inter-scale effects, model within scale effects, measure and tool inventions, till better measures, discovered scale effects, remaining within/between scale anomalies reveal the utterly new to me.

Idea Marketing

12. I market ideas within my own mind to various viewpoints I can develop mentally, then select best fit ideas to market, again within my own mind to representations of actual social market forces in my field, till I come up with a creative work as the package that transmits that idea to those social market forces in my field effectively: market problems/approaches/partial-solutions etc. to myself within my own mind, packaging an idea tailoring it for myself, my field, history, competitors, and changes needed in history, till modifications of idea-packaging, target audience, the idea message, and channel make it creative, regardless of the idea's actual content.

SOCIAL Community of Ideas

13. I assemble possibly relevant ideas and let them interact as their own natures dictate, noticing how they pair up, conflict, sequence themselves and in general inter-relate, till powerful interesting such idea assemblages come to my attention as possible creations; this involves stretching between my conscious calculative brain's direct interventions/generations (from conscious monitoring and meta-creating) and my unconscious associative brain's observings and lettings go, idea associations, idea blends, emergence of idea structures and frames, till a final emergent creation appears., in a kind of ecstatic experience of beyond-myself “flow” consciousness.

System Model

14. I influence the social judgement dynamics of that field of people who judge what works are creative or not in the domain in which I work by tuning the dialog among myself, my creative work, those judges, and rules of the domain till creation appears; this is done by: managing some populations of interacting things (creators, works, fields, domains), turing aspects of how they interact (diversity connectedness, patchings, reflexivity), and tuning social properties (dialog, social judgment, group process, and properties of groups), mindful of the social psych, power politics, culture transmission/assumeds, and encounters-with-the-other dynamics these tunings involved, till better than imagined results emerge. ,

Social Computation

15. I am in the midst of a community of people among whom flow various social computations having inputs, outputs, and processors consisting of layers each more flexible than the next of hardware, firmware, software, in each layer of which are operations each having input, output, and processor (repeating the above endlessly). I manage that flow till at where I am in the community a critical mass of ideas appears that becomes creativity—most of the time each function just listed is done by well structured mass workshop events—develop interest events, process design events, isolation and combination events, events that design such events, processes for coordinating/tuning/switching/monitoring events, and finally product and research events for developing interests profoundly into creations.

Social Movement

16. I am in the midst of a community of people among whom frustration builds up till released into a social movement of new ideas by the slightest particular new idea, avalanching the entire community into a new overall idea configuration.

Space Sharing

17. I share the same intellectual space with a community of like-minded others, inventing tools that intensify that sharing and pursuing competitively similar intellectual goals till rather unpredictable slightnesses among us and the ideas we work with cause creativity to appear somewhere among us.

Participatory Design

18. I notice how in modern societies specialization of function has stripped certain kinds of thought, thinking, collaboration, feeling, from entire populations concentrating it in profit-making centralized industries and create by undoing important pieces of that harmful over-centralization and over-concentration.

GROUP Mass Solving 19. I define a certain solving process and get many people to simultaneously apply it while interacting with each other tuning their motivations, interactions, and configurations till creativity emerges.

Process Deployment

20. I come up with one interesting process after another and deploy them across certain social configurations of people, tuning motivations, interactions, and configurations till creativity emerges.

Optimize Ideal Flow

21. I identify the intended flow of energy through particular systems and optimize the design, environments, conditions, and controls of the system to get as close as possible all of the energy to flow in the intended path through the system till performance or qualities never seen before emerge.

Meta-Cognition

22. I organize my tools, facilities, collaborators, associated institutions and relationships for heightened meta-cognition--awareness of how we think and work till creativity emerges.

Social Connection-

ism

23. I work in certain idea layers and social relationship layers combining and selecting what comes both to my conscious symbolic mind and what comes to my unconscious associative mind, coaxing ideas and relationships through phase changes till creative new patterns emerge.

Demystifi-cation

24. I return power to people who have been habituated to giving power to things outside themselves via creating works that communicate a demystifying-of-the-world-message--that makes people conscious of how they have given power and options to things outside themselves that rule them unwholesomely.

KNOW-LEDGE EVOLU-TION

Dialectics 25. I find myself embedded in large evolving forces and patterns, defining myself by opposing large established ways, as younger ones gradually define themselves by opposing my work as large established way.

Compilation Cycle

26. I work with many different traits that knowledge has, compiling knowledge from one format to another watching how that affects those traits till gaps, distortions, elaborations or the like in those traits reveal creative possibilities to me.

Relocating Idea

Ecosystems

27. I work in several different ecosystems of ideas and by bridging particular ideas from one ecosystem to another or from one idea ecosystem to a different social ecosystem, I turn them into creations.

Idea Waves 28. I find myself in an ocean of ideas where waves of coherent different sets of ideas wash over the diverse parts of society, including me, regularly such that by setting up tools and workstyles that catch these passing waves and combine ideas across them, I end up creating.

Fractal Recurrence

29. I live among different schools of thought that arise and oppose one another, fuse and split, so that I use how very abstract idea polarities and oppositions keep reappearing through time and on different scales of thinking to, by doing the next inevitable step in this process, create.

Simple Programs

30. I analyze situations till I find a way to model all the interesting and important complexity of the situations using the simplest thinkable system types yet capable of generating all that complexity, then by changing such simplest system parameters I generate hosts of creations.

EXPERI-MENT

Solution Culture

31. I notice how people often choose exactly those solutions guaranteed to perpetuate their problems, how failures and missed opportunities are not accidents so much as logical extensions of entire “cultures of failing” that build up unseen in people--by reversing traits of such failure cultures I invent and apply solution cultures that then create solutions to long standing recalcitrant problems.


32. I try certain strategies or policies in order to generate data about how reality is really working, then use that revealed data to redefine the problem and devise better strategies and policies revealing in turn better data on the basis of which to devise better strategies and policies, repeated endlessly till creation emerges.

Creation Events

33. I gradually find and combine components of an idea or approach, assembling various people, resources, ideas into a series of events, designed around particular idea or people combination procedures, taken from experts, from which emerges a final creation.

Fractal Model

Expansion

34. I organize ideas into multi-scale hierarchies, tightly ordered vertically in layers and horizontally in idea-categories, then I expand the geometry configuration of the ideas, inventing new ideas at every level and category, coming up with dozens of creations at once.

Social Automata

35. I tune the interactions among many interacting people, arranged in certain neighborhoods and trained in certain behaviors of interacting, adjusting connectedness, diversity, and deployment of initiative-taking in the system till creations emerge.

Create by Balancing

36. I envision my domains of thinking and work using very comprehensive abstract models to spot slighted dynamics and over-emphasized one, then create by devising tactics that rebalance the domain by emphasizing slighted dynamics on my abstract models or slighting over-emphasized ones.

SYSTEM Non-Linear Systems

37. I build models of my domain as a network of non-linear interactions among populations of agents with butterfly effects, system avalanches from one attractor to another, first mover advantage, and I tune interactions among agents till better than expected results simply emerge from sudden system-wide avalanche events.

Darwinian 38. I set up competing ideas, approaches, relationships, or events, such that traits of successful ones combined with variants I invent populate a new population of competing entities, the whole system evolving till a creation emerges from this natural selection like process.

System Effects

39. I, like everyone else, suffer from surprises as system effects, unanticipated and unanticipatable in the non-linear realities of our lives, intrude, but, unlike everyone else, I catalog, explore, and develop tools for using these non-linear effects till they become dependable creations.

Surprise 40. I catalog and study system effects and I catalog and collect unusual frameworks for viewing matters in my domains, using the former to anticipate surprise types and the latter to reveal surprising phenomena, till one such surprise turns into my creation.

Adjacent Beyond

41. I start with small tiny creations, that accumulate and combine with each new such creation I make, to make myriad new combinations, some of which are creative, which when identified, pruned of noise, and combined with my past creations, spawn still more combination possibilities, some of which turn out to be creations, exponentially continuing my stream of creations.

Population Automaton

42. I manage populations of interacting ideas on multiple levels of ideas-in-mind, feeling responses, performance moves and improvs, parts of organizations till insights as non-linear system avalanche events happen, generating creations.

PURITY Subcreations 43. I invent little tools and processes, decor and arrangements of my personal living and workplaces to help me create still more creative tools, processes, decor, and work arrangements, in a continuing exponential stream till later ones turn out to be creations or to enable me, using them, to create what others, lacking such tools and work arrangements, cannot imagine or produce.

Productivity 44. I generate a lot of ideas and throw away the bad ones, and, by generating ways of producing more ideas than nearly anyone else in the same periods of time, and accumulating experience from throwing away bad ones, more and more of my ideas become creations.

Performance 45. I understand that I am a performer, and my performances are the ideas I produce, which perform before various audiences, using an anthropological stance of seeing the limitations of culture of my audiences and the theological stance of seeing the limitations of life itself and how my audiences position themselves within them to make my ideas creations.

Influence 46. I seek to influence people and the world via explosively producing disillusionment with existing frameworks with what I create which must be timed and positioned, packaged and expressed so as to influence the field of people in my domain.

Investing 47. I manage a portfolio of diversified investments of time, idea, and effort in parallel simultaneous projects attempting unlikely outcomes, mixing venturesome and conservative strategies, till one is a hit, and turns creative.

Info Design 48. I find myself in webs and configurations of structured information such that particular structural features of these information distributions result in creativity--so I work to locate such webs and locate my self and my work in such webs till I am where creativity emerges in them. I study operations on accumulated past creations that produce new ones then extrapolate them to invent my own creations.

SELF Courage 49. I have the strange ability to fully appreciate the worth and inventiveness of others and traditions around me while simultaneously challenging and overthrowing all of that in everything I do, resulting in occasional creations where my challenges get accepted.

Anxiety Channel

50. I notice how the fundamental anxieties of existence inevitably get side-stepped, omitted, and slighted by people in my domain and the works they generate till I spot such slightings and by correcting them reconnect my domain to the deep realities of life, hence, a creation.

Extended Self

Development

51. The first creation I made was myself, which I made by undoing automatic parts of me put there by where and how I grew up, substituting the best from history and the contemporary world, and continuing this invention of myself seamlessly turned into creating in every field I entered as the idea of extending my self via works I create.

Interest Ecstasy

52. I pursue interest in everything I do, balancing myself at the very edge of all my capabilities and motives, till I am transported beyond myself where forces of the universe take hold of me and use me as a vehicle for their own creating.

Career Invent

53. I create my self, then I create my own career through this world, then as I transition to bolder and more interesting career paths, I run out of pre-made ones and start inventing new career paths never seen before, till one of these transitions becomes creation.

Performance Creativity

54. I get ideas to perform before me till one set of them captures my interest then I organize ideas into performances before others in the form of works that audiences respond to till creation emerges.

MIND Insight 55. I alternate engagement and detachment as I apply known frames to a challenge, till I run out of existing frames and have to invent new ones, accumulating failures till they begin to specify, inversely, what eventual solutions must be like, till a slight new idea avalanches the entire set of ideas before me into an emergent sudden insight, that when carefully pruned of noise, reveals a creation.

Cognitive Operator Extremes

56. I drive my use of certain common cognitive operators in the mind far beyond the intensities of use of them by others till results that no one has seen before obtain, some of them later being judged creative.

Making Sense

57. I find nearly everything in the world flawed, sloppy, half baked, deeply unsatisfactory, and lacking basic sense, and I cultivate this negative vision capability till I see hundreds of ways to improve virtually everything in life around me, focussing on a few which I actually fix till judged creative.

Percept Invent

58. I am drawn to the paradoxes, contradictions, gaps, omissions, anomalies, circular arguments in everything around me, seeing spaces where everyone else sees objects in scenes, till I dislocate my own perceptions enough that I see things to fix that when I fix them become creations.

Experience Realization

59. I keep careful track of my experiences accepting no common thoughts, explanations, without making sure they make complete sense to me and completely explain my experience of things, till I find something everyone else accepts and depends on that has a deep gap in it that does not fit my experience--by fixing it I do what others judge creating.

Substrate Update

60. I watch as a never-ending stream of new substrates for doing functions enters the world, from global commerce, research, and technology every day and year, and observe when existing functions and institutions hold onto past substrates at great cost way past the time when there are good alternatives substrates--by pioneering replacement of past substrates for doing functions with new ones from that never-ending stream, I create.

There are MANY ways that we create:Inside DESIGN PROCESSES are process

SINGULARITIES where models of creativity conflict, compete, suggest themselves.

[email protected]

CULTURE DESIGN

CULTURES OF DESIGN, DESIGN OF CUL-

TURES, AND DESIGN AS CULTURE WORK

The great designs escape from design cultures, the cultures of their profession. Designof cultures is almost never done though greatly needed. Design itself is the un-doingof prior cultures of design and their dated assumptions. The papers in this section allhandle these ideas:

1) fractal models of culture2) leveraging diversity3) powers of cultures4) predicting product pricing, version, sales via maps of interactions of culture of devices and users5) the role of culture in systems engineering6) the culture work of innovating.

The first three papers present lots of models of the components of culture, ten in all,and each of the ten having from five to 64 component parts. The first paper presentsthese ten component fractal models of culture’s part. The second paper presents 33tools that people expert at handling cultures used to achieve their results. The thirdpaper presents 9 powers of cultures that we either anticipate and handle or experi-ence later as disasters.

The last three papers all concern using such culture components to do things of use.The fourth paper presents successful predicting of prices, versions, sales by maps ofhow the culture of devices/products interact with the culture of users/markets. Thefifth paper presents a thorough map of the role of culture in large-scale systemsprojects. Included in that are highly interesting components like the culture of toolsof all sorts and ten cultures-of-use they all evolve through. The sixth paper presentsinnovation as the doing of culture work, generally, the countering of habitual andunexamined usual cultures that people are not even aware of. Part of the surprise ofsuccessful innovating is the laying bare to consciousness of assumption world we werenot aware were operative within us till the project got enacted.

Overall this section of papers lays out the vast unconscious stuff inside us that controlswhat we notice and consider doing--limits to this stuff, from when and where it gotinto us while being raised or being a member of some group or other--become frontiersfor great designs and designers. Those able to spot highly unconscious and long-stand-ing shared assumptions, are thereby empowered to amaze us all and to change funda-mentals in our lives and work.

Proceedings of TMCE 2012, May 7–11, 2012, Karlsruhe, Germany, Edited by I. Horváth, A. Albers, M. Behrendt and Z. Rusák Organizing Committee of TMCE 2012, ISBN ----

THE CULTURE WORK OF INNOVATING—GETTING REAL ABOUT INNOVATING IN BUSINESS 27 WAYS

Richard Tabor GreeneMaster

De Tao Masters Academy, China & [email protected]

ABSTRACTThis paper presents a model of Innovation as an Operation on Cultures, in a larger culture of Faking Innovation (innovation noises). Once you realize it is people, who innovate, who become engineers (not a few anti-socials hiding behind maths), who become MBAs (not a few psychopaths maximizing only their own individual benefits), who become rich (not a few people split into charming faces and bitter climbers), who develop Silicon Valley technology (not a few Asperger's syndrome people for whom this world has only themselves as person), who manage big firms (not a few psychopaths according to recent British research), you see an entirely different landscape for innovating than tools, techs, and systems. This paper uses a small survey of product managers from global firms to display real world conditions of innovators, countering the overemphasis on analysis by professors. It is much more emotive, social, relational, cultural, human things than analysis that produce innovation. This paper presents specific failures in current academic and industry approaches to innovation and suggests correctives: moving from single right-y models to diverse repertoires of models, moving from techno-male innovation approaches to innovation-as-culture-change approaches, and related others. Once you subtract forced infrastructure upgrades out of innovation measures, what is left often is the rare places where culture work gets done, often as male-techno “systems” installed in such a way as to make systems more creative or productive by feminizing them (reducing weaknesses from excess maleness of systems).

KEYWORDS: NOVELTY SCIENCES, CREATIVITY SCIENCES, BIOSENSE, MECHANOSENSE, NATURAL SELECTION

1. SOME IMPONDERABLES, AMBIGUITIES, & FAILINGS OF OUR MODELS OF INNOVATION

Kodak is scary to us all. Christensen at Harvard made an entire career on advising everyone how to

not do what Kodak spent the last 20 years directly doing. Kodak hired him repeatedly and applied all he said, and died. Such is the power of Harvard's innovation model one [1]. Procter and Gamble spent a few million dollars and a couple of years tweaking 42 environment variables (Amabile) to generate a culture supportive of creativity---their Corporate New Ventures program. After all they their results---copying a hit Japanese hot pad product 8 years after it was a hit in Japan [2]. Harvard reported this as a creative outcome; most of the rest of us view delayed copying as, well, something less than creative. Such is the power of Harvard innovation model two. A Procter & Gamble spin-off firm, doing Chesborough's outsourcing of research functions in the form of idea-needs posted to retired R&D folk, showed early creativity increment as people, newly connected, learned each others' ideas. But, unpublished, a rapid, near total drop in participation happened as users got tired of pawing through reems of same-ol-same-ol ideas boringly familiar [3]. Such is the power of the Chesborough MIT innovation model one. The profitability of the entire Japanese consumer electronics industry, for an example abroad, comes, every year, not from Sony walkmans and the like but from mid-1950s laws on housing standards, put forth by consumer electronics not construction companies, that guaranteed too little insulation and no central heating vents. Every year the profits of Japan's vaunted consumer electronics firms come not from hot products and latest techs but from selling same old heaters and coolers, air cleaners and humidifiers---one for each room of Japan's lousy housing standards-conformant houses [4]. The “innovativeness” of Japan's consumer electronics firms does not produce profit---legally enforced terrible housing standards produce it instead. Korea correctly estimated the subsidy given for semi-conductor and LED panel TV development implicit in heater-cooler-cleaner-humidifyer sales and by exceeding it, out-invested Japan and defeated their entire consumer electronics industry [5]. Such is the power of the Japanese innovation models by Nonaka, Mirai, Kawakita, and

THE CULTURE WORK OF INNOVATING—GETTING REAL 27 WAYS1

others. [Note: the Koreans won by ignoring academic models and measuring real factors at work—in Japan's faking of innovation; this will be a very important point in this paper; one could extend this example to include German banks extending loans to irresponsible parties who buy German exports but that is impolite given the venue of this paper and conference.] We could go on and on, model by model, showing that Harvard, and other top colleges, have never published a model of creativity that, when sincerely and thoroughly applied, works. Harvard does not have a clue as to what innovation is, if we rigorously assess their results. Why?--do great universities like Harvard delude themselves and us that they understand and can do innovation? Why?--do great corporations throw money at Harvard faculty for advice that manifestly in published evidence produces little or no innovation?

If we keep our focus on this, above the usual innovation research questions, answers like the following appear:

● innovation noise from career systems—managers hire Harvard consultants because appearing creative is enough [6], being creative is not career-necessary (note: there is lots of research showing that males settle for mere appearance at huge rates compared to females of similar rank and responsibility—this apparently has a hormonal basis, a drive for rank not care, praise not deed).

● single models of anything (creativity, design, innovation, venture launch) have virtually no impact power only plural balanced diverse repertoires of models have impact power [7]

● the maleness of all industrial systems (made by males, led by males, operated by males) and the maleness of all academic consultants and approaches (made by males, led by males, bought by males, applied by males) constitutes an immense neurotic blindspot guaranteeing that lack of innovation from excesses of maleness of system are made worse not better by excesses of maleness on proposed methods for innovating from male consultants at places like Harvard [8]

● lowest common denominator standards of change, innovation, creation—via universal social collusion effects, everyone can appear innovative if everyone accepts and celebrates tiny, not to say, silly innovation victories as if great heroic accomplishments, given how hard it is to get anything of value done in our immense organization bureaucracies [9].

● There is a huge change-tiredness in us all. The basic technical infrastructures of all communication, collaboration, display, info organization, design, and like functions are continually evolving at slightly accelerating rates so that perpetual change and updating, with attendant learning costs and efforts, are the “normal” condition of all work systems—basic change rates leave little morale for, interest in, or need for larger levels of innovation (except for the Kodak effect). Who can thrill to big changes when myriad quotidian changes are sloppily or incompletely done, consuming all our extra capacity to care[10]?

● Western people and cultures are so Descartian, so biased towards concept over affect and deed, that executives stop when they feel they have right ideas and never suffer much attention or care on actually getting the world to change with those rightnesses [11].

● New tech sales of only things that remove the isolation and differences innovation requires. There is a commercial pressure from vendors to pay for false and exaggerated stories of “success” of an endless stream of technologies that increase connectedness and harm isolation, concentration, and undistracted investments of interest. So the departures, difference, surprise needed for innovation is ever less likely and easy to obtain [12].

● Ideas at work serving largely an entertainment rather than work function---modern systems requiring more educated persons, thereby require people hard to motivate and keep attentive without continual streams of new-ness. So innovation models are bought regularly to entertain not improve, though all pretend otherwise [13].

● Enterprises need mostly consistent execution for profits not innovation but career forces need appearance of innovation regardless of need—so amount and type of innovation becomes an inverse U function that enterprise parts tune to optimal settings of---too much innovation and stable execution reduces profits, too little innovation and managers lose limelight and promotions needed for stable execution that enriches CEOs [14].

● Therefore, slight or mere appearance of innovating is all that is demanded by enterprises and fits well with single right-y

2 Richard Tabor Greene

Proceedings of the TMCE 2008, April 12-16, 2008, Lausanne, Switzerland

and analytic methods of academics having little power to impact anything. Industry demands and accepts the mere appearance of innovating which is what academics can most readily supply. Think this---Procter & Gamble's immense research organization as “a marketing tactic” more than a knowledge or innovation tactic [15].

This study shows that innovation questionnaires that are not biased around academic categories and analysis, but that strictly ask manufacturing innovation practitioners for what they innovate, how, and why, with what results, find a totally different domain of innovation dynamics---far more human, cultural, emotive than usual academic models of innovating. This paper pioneers portrayal of this unpopular, uncomfortable, but powerful and true aspect of innovation.

This is the Honda case of Harvard Business School repeated at larger scale [16]---Honda employees in the US had no money so brought along their cheapest bikes for commuting, which bikes got envied by neighbors and co-workers till Honda made more selling those bikes than what they came to sell. Harvard faculty mis-read this, neurotically, as great executives back in Japan, seeing a “soft-underbelly” to the US cycle market out of their intellectual brilliances (from great colleges like Harvard with great professors like them). They made the mistake of publishing their self-loving biased distortion as a Harvard Business School case study---the Honda Case---that became global proof of faculty neurotic blindness, rewriting reality to make professorial analysis seem key to all good outcomes.

Summary: 1) innovation noise from career systems 2) single right-y models lack impact 3) blindspots from the excess male-ness of all industrial systems 4) social collusion agreement to praise petty levels of innovation 5) change exhaustion from technical substrate evolution pace 6) Descartian bias of Western persons and cultures for idea over act and emotion 7) vendors push systems that erode the isolation and differences innovation requires 8) Ideas serving more entertainment than work functions 9) amounts and types of innovation as inverse U function, too much innovation undoes stability needed for profits, too little undoes manager promotion and morale 10) for show, for marketing research and innovation as useful or more useful than real innovation.

Remember---the above 10 points come when we direct our research at why and how the best innovation models of the top universities have zero

impact when sincerely and expensively applied by highly competent private sector organizations.

2. PLAYING MANUFACTURE IN OUR NEW GLOBAL WEB SPACES

Now let us consider the relation between manufacturing's evolution and innovation models.

Silicon Valley [17] is filled with several thousand venture technology businesses: most have 7 employees total (they outsource everything possible) most founders are 36 years-old refugees from big firms (those big firms are the venture first customers), half the ventures one way or another sell to defence funded projects with 8 to 10 year long budget cycles (nominally cycles are shorter but in application they are longer). In the Valley, ideas, funds, technologies, and people flow till they find homes, places to love them [18]. The places that generate these often do not love them and one way or another release them into the flows that swirl through the Valley. Silicon Valley soundly defeated Route 128 in the last 30 years, producing approximately seven times more funds to GDP, for one measure. It did this via a California culture against East Coast US culture, via a Stanford culture against MIT, via a business culture against blue suit pompous monkey hierarchy, via a tech culture against central broadcast media like TV and Hollywood that make users passive. Silicon Valley generated the PC and the web as new spaces of appearance (Hannah Arendt). Old media use laws to prevent the new business participants and forms that new spaces enable.

This new space—accepts and invites some but not all functions in manufacturing and business. It invades functions and sucks others into its world. The anti-ness [19] of the cultures that created this new space infect the specs and functions it affords (ecologically). When it invades or when functions get sucked into it, they become contaminated (one could say) by these anti-nesses.

Malone predicted, in his coordination theory group around MIT [20], that this new space would form manufacturing into a bi-polar distribution with some immense centers (locality's powers) and an immense crowd-source on the periphery of local invention and application (devolution of making to anyone). Consider Ponoko in New Zealand—local laser making of things for anyone, amplified by the Printing of Things—are these the unseen peripheral developments that by revolution replace global Fortune 500 firms in a generation? The middle

THE CULTURE WORK OF INNOVATING—GETTING REAL 27 WAYS 3

would de-populate as mediating production, decision, info aggregation, distribution would become unneeded. Things that had been done via internal hierarchies could be instead done via horizontal markets and auctions. Rapid assembly of product and venture production aggregates would replace staid huge centers, except for a few immense centers, made more immense via instant low-cost accumulation from across the globe. Cellularization does not greatly affect this scenario. Nor does GPS facilities being added.

Table 1 Trends in Manufacturing 2012 FIFTEEN YEAR-2012 TRENDS IN MANUFACTURING

Manufacturing has become a Software Only Business (Integration costs dominate)Where Manufacturing is Headed in 2012

(according to: ACM, IEEE, Gartner, Booz-Allen, NSF, SME, JUSE, METI)Google search results using “Manufacturing Trends in 2012”

Our Great Transition

1) diversification, global distribution real-time integration of everything = industry convergence with military set up, take down, logistics capabilities

9) Politics emerge as primary block to enterprise wide systems; wider = higher=more dated

Diminished culture of development: loss of reliable near future = investment down and tentative

2) Audit to ERP to PLM to DCS to PLC to SCADA integration and interoperability = huge IT hiring

10) Generational divide becomes defining trait = IT proposals from youth to non-IT deciders/execs

Rise of the stem cell biology industry as baby boomers die and get old

3) Pressure to instantly gratify customer = death of planning (real time response replaces it)

11) End of SAP era, that scale of integration supplanted by whole enterprise and whole supply chain emergent net standards = inter-vendor platforms emerge

Replacement of mechano-sense with bio-sense commonsense---huge generational divide

4) Global process standards = global worker capability systems and standards

12) End of manufacturing---E-Businesses will step by step compete with own suppliers = replacement by periphery of new manufacturing of new types of product with integrated core doing all old ones together

Culture management emerges as THE single leadership capability

5) From reactive to statistical predictive systems (maintenance, load balancing) = single standard component networks among plural manufacturing supply chains

13) From agile to mobile to relocatable to inter-combinable = evolution of analogous systems and functions

CHINA as smart, big, unapologetic, and style leader---sell to shrinking rich Western elites or sell to getting-middle-class hordes in Asia

6) Push to cloudy systems will over-reach security = IT disasters as primary emergent manufacturing risk

14) The social web is not social, this is being discovered = pressure because project managers are not now agile, they are trained to run, not to set up, take down, modify, fuse, split systems

Germany either self destructs unable to see beyond own virtues and performance superiorities to who buys their goods or Germany leaps to web-enabled disaggregated-to-localities new forms of manufacture by anyone

7) Push to cloud will surprise with increases in expense as small firms encounter global standards above their former IT use = stall of cloud-ization

15) Commonsenses conflict = hardware/project commonsense mis-functions when IT infests systems and devices.

The USA launches global wave of disaggregated manufacture: by users, by anyone, by all---as web enabled laser/printing of parts turns



drive manufacturers into publishers

8) Design for sales collapse, design for weather catastrophe, design for nation-relocation = survivable systems within which sustainable systems get embedded

16) HTML5 + CSS3 as the new web = self managing expands and deepens = agility by massive multi-industry manufacture sites as one pole, and local global distributed fab labs as the other pole (a la Malone coordination theory)

Technology stays where long term defense contracts are big and many = China and USA.

3. CHANGES IN MANUFACTURING SYSTEMS

From the 15 changes in manufacturing for 2012, gathered from a usual assortment of sources, we can derive a few powerful images that help us keep in mind the lay of the land and its direction of evolution.

● Military Campaign Logistics--Manufacturers will move closer and closer to this ideal---designing and building in any part of the world complete cities within months and taking them completely down, for relocation, in months.

● Supply Chains Become Districts--The convenience of local low costs and materials will pull nearly all global producers to a succession of locations. Supply chains move as wholes to such districts in succession and therefore become extremely short except for a few material inputs, a few information inputs, and global distribution of their outputs.

● Polarization—pole one: global districts where diverse supply chains concentrate for locality benefits; pole two: dispersed new local labs where anyone can invent and make small runs of parts via laser shaping, layered printing of things, and like technologies; pole three: large manufacturers look more and more like publishers.

● Making Becomes Sheer Calculating-- Manufacturing already is mostly IT running derivative machinery. Manufacturing will add layer after layer of networking, data standards, systems integration-inter-operability till the IT tail wags the machinery dog.

● Executives Become the Problem--The generational divide will be exacerbated as more and more IT proposals are delayed and mis-judged by older executive generations operating in mechano-sense not newer younger bio-sense. Bone is stronger than

steel—it grows stronger where most stressed, detects flaws and repairs them.

● A Smarter Web, a Web of Things, Enables Things to Have Commonsense--HTML5 allows things to know what they do and do not do, have a sense of themselves, and of other things. This makes reconfiguring immensely faster and easier, allowing combinations and changes of scale of operation that today are slow and laborious. A huge increase in agility of systems results.

● Security, Disaster-Resilience, Commonsenses as Limitations--The reliable intermediate future, in cultures of development, that allows investment, will be badly eroded, as every business entity shifts focus to China (Germany becomes almost no longer European).

● Cloud Computing Stalls Badly--It is neither as cheap as it says, not as secure as it says, nor as responsive as it says. It undermines CIO standards and cost controls, while leaving CIOs responsible for any messes engendered.

● From Reactive to Statistically Predictive Systems Maintenance--load balancing, supply chain reconfigurations, and the like, using Big Data facilities from the likes of IBM, become predictive. Meanwhile the Death of Planning occurs as real time data and agility make plans both un-needed and inaccurate.

● Fractal Bottom Up Integration--Our multi-decade long journey of integration, stymied by lack of standards, then bigger scale integration, stymied by need for larger scale multi-vendor standards, continues. Vendor communities continue to combine and re-combine for platforms of mediate-scale integration for their own benefit.

● The Eco-System and Natural Selection Dynamics of Systems--The biologic nature of industrial, technology, systems will become more and more evident constituting a new commonsense that will conflict with


older people having mechanosense, not biosense, default views of what is good, excellent, satisfactory, and the like.

4. IS THERE ANYTHING TO MANUFACTURING OTHER THAN INNOVATION?

Tony Affuso [21], the CEO of Siemens PLM Software, in 1986 predicted that more and more of design and manufacturing knowledge would one way or another get into software (called CAD/CAM systems at the time). He predicted that humans would do vertical work—getting in the mind things out into systems form, and horizontal work, getting local systems and ideas to integrate and operate in standard ways with adjacent others on wider and wider size and time scales. His firm became the world's dominant CAD vendor by long term persistence in precisely this direction.

So the information layer on things, machine, sites, products, is designed to evolve, to capture vertically and horizontally. The new cellular GPS social web HTML5 space around us all and the world of things,

is just going to make both the vertical and horizontal jobs above, faster, easier, and more robust. In that sense manufacturing systems are innovation systems. But is this innovation? Or is it continual technical substrate evolution that makes extra innovating both unneeded, distracting, and delusional? It is forced, perpetual, outside-in, not the usual traits of what we call “innovation”.


EXPOSUREget lazyand ridiculousRIDE GROWTH OF OTHERS

PLACE LOTS OF BETSLOVE TO EXTREMES

INTENSITYget naked

INNOVATIONS AS ANTI-INSTALL CALIFORNIAS

SOCIAL FUSION & PLASMASSELF CANIBALIZE =

Fund Self + Anti-Self

PERFORMcreativity is a disease

EMBRACE EMOTIVE WORK OF INNOVATING

MANAGE/INVENT AUDIENCESBECOME THEORISTBECOME A STORY

LEANeveryone else is your

libraryINVENTION INDEXED

LEARNINGREPERTOIRES NOT

RIGHTNESSESFIND/USE UNIT OF

INTELLIGENCE

UNDO BIG-NESSESdon't believe anyone especially

yourselfSTOP INNOVATION NOISE

REMOVE EXCESSES OF MALENESSREBALANCE PROFESSIONS CULTURES

ESCAPE 5 CULTURES OF BUSINESS

BE COMPREHENSIVEplay the glass bead gameINTERACTION OF 18 NOVELTY

SCIENCESTURN HEROS INTO SEQUENCES OF

OPERATIONSUSE 60 MODELS OF CREATIVITY

KNOWLEDGE DYNAMICS

make ideas danceFROM PROCESS TO EVENT DOING

CORTEX-UALIZE THE WEBGET NATURAL SELECTION RIGHT

HIGHERSTANDARDS

break into people's hiding places

ACHIEVE DIRECTEDNESSINCREASE SOCIAL INDEXING LEVELS

HUMANIZE ENGINEERS

NOBEL PRIZE FUNemotion richness enables

design decisionsFUN IS THE FUEL, THE ONLY FUEL

PULSED SYSTEMSORTHOGONAL DEVELOPMENT

FEMININITY OF CREATINGINVEST AT TIPPING POINTSj

Figure 1 Summary of the 27 Ways to Innovate Data from 40 Product Managers


Figure 2 Eighteen Creativity & Novelty SciencesTHE 18 NOVELTY SCIENCES [22]

Creativity generates Novelty which generates Selves, Knowledge, Discovery, Invention, and Exploration

1) The Liberal Arts of Each Novelty Science Come from the Evolution, Natural Selection dynamics, of Each;

2) Novelty Comes into the World via FIVE Types of Creation and TWELVE Levels of Creation

EVOLUTION IN

(Natural Selection

dynamics of):

STORY-COMEDY-HISTORY-

PHILOSOPHY OF

CR

EA

TIV

ITY

NO

VEL

TY SELVES

Educatedness (self creating persons)

Careers

Creating Others (Leadership, Parenting)

KNOWLEDGESystems

Cultures

Quality

EXPERIMENT & DISCOVERY

Innovation

Ventures

ART & INVENTION

Design

Compose

EXPRESSION & EXPLORATION

Fashion

Perform

INNOVATION NOISE, FROM PRACTICE AND ACADEMIA REDUCED BY THE 18 CREATIVITY & NOVELTY SCIENCES

There are three innovation noises [23] each with their distinct own source: a) career system forces (you attract top manager attention when doing something innovative regardless of whether it lasts or works when implemented fully; b) academia is a profession that “sells” analysis and models it makes as the key to good outcomes (almost entirely without solid data showing such things get fully implemented and have impacts when implemented well) with each professor suggesting his/her model is better than models by others and nearly no one building on or combining models by others; c) the continual pressure from outside vendors trying to sell new generations of technology and from peer competitor organizations upgrading to platforms in ways that force your organization to at least match them, makes continual upgrades, learning, adaptation a tiresome perpetual trait of all work systems. All three of these: 1) exaggerate the amount of innovation being done 2) exaggerate the importance of what innovation is being done 3) highlight changes not impacts from

changes 4) furnish strong incentives to look innovative regardless of actual increments of novelty installed 5) create a silly deleterious atmosphere that all change is change, all change is good, all innovations are implemented, all innovations have impact (none of which are true)

Somehow we have to reduce all three of these innovation noises and blunt the powers generating them. One step is changing from doing innovation on its own, to doing it in the context of 17 other creativity and novelty sciences, described immediately below. Another step is changing from use of single right-y models (of some academic claiming to his/her model is “better” than models by others) to use of balanced diverse repertoires of models of things like innovation, creativity, and design, such that one chooses several diverse models compensating for each others' flaws, fro any actual implementation. That is discussed in the section after the passage below.

Normally we all engage in innovation on its own terms, ignoring where creativity is found in it, ignoring its relation to design, ignoring its venture technology business launch implications, and instead getting caught up in the background of noise of innovation that career


systems in giant groups of males, hierarchically organized, produce---where innovation (looking innovative) helps draw top monkey (male) attention one's way and propels careers. There is a vast exaggeration of the need for innovation and of the amount of it achieved by careerist males striving to move up their respective hierarchies. This is called innovation noise, fueled by the increases in primate behaviors as organization size increases, the hormones of males in organizations with few females, and by commercial forces in corporations towards making this year's “refrigerators” look “innovatively” different than last year's such products. Similar forces work in academia—in large departments of mostly male professors exaggerating the power of their own models compared to models by others.

When instead we put innovation in the context of discovery, invention, creation, design, venture launch, composing, performing and the like (for the other 11 creativity & novelty sciences), we prune away innovation noise, reduce primate-behavior driven exaggerations, and reveal a core inside innovation where 60 or more creativity models bring the new into our world. Innovation done in the context of the other Creativity & Novelty Sciences, is very parsimonious and careful about where, what amounts and types of novelty are born and raised from baby-hood to adult status (where new ideas can stand on their own and defend themselves from assimilation established forces and ideas).

The above figure is used here merely to suggest one causal ordering of creativity sciences. Using the above figure, creativity brings novelty into our institutional world via discovering what might work or works and experimenting to see if and under what conditions it works. This takes two forms---venture businesses launched and innovation, enduring changes in systems and institutions.

As cited at this article's start, single right-y academic models of creating do not work, of innovating do not work, of designing do not work, of venture business launch do not work. What the 18 Creativity & Novelty Sciences table above represents is an idea—plural diverse balanced repertoires of models replacing the single right-y models academics now present. Why do academics study each of the Creativity and Novelty Sciences in isolation? Why do they study each of the below 54 models of innovation in isolation? Why do they apply single such models though they get no or tiny impacts from such applications? Why?

It is the culture of academia. Ideas there are for publishing and personal rank enhancement. The game is—my model is better than yours, not---my model

fixes a slight flaw in yours so the combination of mine with yours is great, not---my model actually produces great increments in creativity when applied. There is a flaw, a neurotic fixation perhaps, in academic culture that leads to ineffective single models published and applied.



Figure 3 54 Models of Innovation 54 Models of Innovation [24]

How people make creations impact society and real human needs & situationsAcademic models that correspond to the empirical ones below are indicated in each box.

NOTE: a version of the below just as detailed as the one for 120 Creativity Models exists but it is not presented in this article for reasons of space. This model of models is the basis for a four semester course at KEIO SDM on models of innovation.

FUTURE PRESENT FIGHT IDEA SOCIALITIES IDEA ECOSYSTEMS

CATASTROPHE SPOTTING

PATTERN SPOTTING

TREND RIDING

SOCIAL PHYSICS

LIVING INNOVATIONS

IDEA FARMS

abstraction inversions inhabit the future code changing action factors

educatization Silicon Valley clusters

DISRUPTION missing competitor caused disruption-Christensen

SUCCESS FAILScore bribes firm to ignore periphery = future--Bower

CAREER STEPmoving one's career forward not by competing for established fixed positions and roles but by inventing the entirely new --Greene

RIDE KNOWLEDGE DYNAMICS using knowledge dynamics, dialectics, pole-swings, loser enbeds---Abbott

GO OUT = GO IN globals invade locals, locals invade globals = all locals global—Jun & Wright

AUTOMATE INVENTION---nurture circuits among in-process projects, power of absurd concentration; many tries, many fails = many invents--Edison

SEARCHsearch in possibility spaces--Perkins

VALLEY INSIDE Silicon Valley introject (inside you)--Nevens & Lee

SELL PASSIONventure as enthusiasm package & presentation—selling motivation---Kawasaki

MOVEMENT successive successor-contexting social movements—Welch at GE

SELF BUILD ANGST educativeness burden of daily life+self-build= flee to what fled from (authority)--Giddens

VALLEY--flows of ideas, people, funds, techs seeking home to love them + tiny, fast, smart, adaptive coalitions of specialties--JSBrown

CUSTOMER INVENTORSdemocritize innovation= empower lead customers--Hipple

SUCCESS RIDING niches finding/ riding niches---Greene

TRUST BANKSethnic-immigrant trust-bank ventures--Granovetter

SOCIAL REVOLUTIONsocial revolution via liberty, free invents, public happiness, historic dream, conserve novelty--Arendt

DEMASSIFYde-mass-ify=global standard items make locals globally diverse make institutns. not fit---Toffler

VENTURE---anti-big, anti-East, from zero = new ways with new techs; anti-passive, anti-money---Greene, Jobs,

sociality social cognition co-evolution co-adaptation social grammar Natural Selection

PRACTICE GEOGRAPHYsocial life of info—Brown & Duguid

UNORGANIZEinnovating = de-organizing---Sutton

FIT TYPE MENUinfo ecosystem device species proliferation—Davenport & Huber

INVESTMENT CULTUREculture of development: reliable near future--Grondana

RE-SOCIAL-IZE social relation type mixes: share, rank, reciprocate, price--Fiske

RECURSIONS--- natural selection systems within natural selection organisms--exaptations--Michod

NETWORK INVENTORnet not individual hero--Hargadon

SPREAD ONE HOME RUN INSIGHTmental practices of inventors---Schwartz

TAP GLOBAL DIVERSITYopen business, invention outsourcing--Chesbough

CULTURE FARMSculture farming—raising ecosystems of evolving cultures---Darwin, Boyd & Richerson

LEVERAGE DIVERSITIES use diversities: un-see un-do, balance in space in time diffce type/task type---Greene, Page

LEVELS---evolve repertoires of neutral traits, till envt. change makes some vital; evolve levels not fitnesses--Kimura

FOLLOW PROBLEMS ACROSS radical innovation=mix frame, anomaly, tool, crisis research; patron research mixed with cllent research = combine artists, scientists, designers, engineers---Steffik

RE-SEE PAST FROM FUTUREinvent by managing mental models—Wind & Crook

PRODUCER CUSTOMERSmedia microcosmization = all consume all produce in all media --Jenkins

MISCOPYINGorthogonal copying in new context = invention---Westney, Roehner, Syme

ATTRACT CREATIVE ELITEtech, talent, tolerance = attract creative elites—city-fy as insight process--Florida

SOFT-HARD LEVELS----egg computer, DNA program, junk DNA control statements, wrapping modules; behavior to software, firmware, hardware---compile un-changeds to hardware—Gould, Dawkins


non-linear dynamics

emergence wave catching universal inventivity

non-linearity Interface Media Re-invent

NEW WAYS AIMS TECH AT ONCEtech life histories, firms fail by adding new means to old ways/aims---Wheelwright

POSITION IN SOCIETAL ENTHUSIASMS society lust--Lienhard

NOT ENOUGH IDEA HOMESmaximize creations without maximizing failures = R&D, finding fits, trashing non-fits---Matheson

TWEAK CON-NECTEDNESS TILL TAKE-OFFself organizing criticality—manage social automata till critical points---Bak, Gladwell

OTHER SCALE SIDE-EFFECTSunplanned order emerges = goods from bads (Mandeville) bads from goods (Schelling)--Krugman

REPLACE PROSEractal page formats replace prose---see count, names, order at a glance, read and write structures---Greene

NETWORK ECONOMIESincreasing returns to scale (net economies)--Arthur

SPAN HOLES spanning network holes---Burt; policy as experiment—structure for tries and data on tries---Thomke

INCREASE & VENTURIZE FRUSTRATIONSmanage knowledge by capturing frustrateds in org learnings + ventures ---Greene & Blackwell

ADJACENT BEYONDeach invention increases expo-nentially combines with past ones--Kaufmann

POPULATION OF FLUXING COALITIONSlowered coordinatn costs=tiny venture coalitns on web, big firm dissolve into market outsources, giant firm coalitions--Malone

REPLACE MEETINGSsocial automata replace meetings & discussions = missing micro-level = using humans in arrays, assigned cognitive/social functions as if CPUs in cloud computer network---Greene, Ignatius

SPOT PRE-RATIONAL HINTStechnology tipping points via gap detection--Burgelman

RECRUDESING IDEAShistoric necessity+ function lust+emergent process---Van de Ven. Woodman

DESIGN AS PROUNING from intruding Platonic forms design to pruning away detritus design, androgy style--Postrel

SIMPLE PROGRAMSsimple programs = iterative idea tries in neighboring environments till homes found---Wolfram, J.S.Brown

INNER/OUTER TIPPING POINTSnon-linear society net/web dynamics+ non-linear within mind message-stick dynamics = tipping points---Gladwell

REPLACES PROCESSESevents replace process replace bureau = evolution from fixed to flex---stratified responding, to response to response, to structural cognition--Greene

5. 54 MODELS OF INNOVATION Medium and large manufacturing organizations and supply chains are good at inputting variable people, sites, customer requirement changes, evolving technical substrates and getting globally standard dependable products and product performances in variable climes and nations out. Their particular genius is turning variation into stable performance.

Innovation is the exact opposite of this---it turns stable performances into variable evolving exponential accelerations of novelty. Why would one every ask organizations great at turning variation into stability to be also great at turning stability into variation?

Monastic organizations [25] invented such things as the job (this mal-functioning mania for having one person work 40 hours a week for years doing one sort of task—a non-brilliant idea perpetuated nearly everywhere in businesses). Only recently in Holland have women invented taking two part-time jobs in entirely different industries and companies calling for entirely different parts of their selves. The Rinzai Zen monasteries of Muromachi Era Japan invented the wall-less everyone-spying-on-everyone-else and other forms of so-called “Japanese” management; the Spanish Benedictine monasteries of Europe invented the job as each man earns his own food and upkeep, not rich guys living off of servants. Now that 800 to 1200 years since these inventions have passed, perhaps it is time to update what a job in Japan and the West is---this is Innovating

by Changing the Most Mundane Basics of a society and person---how we read, split time, associate, transmit. This is one of the 54 approaches to innovating any one and any organization can deploy. The other 53 models cannot be explained fully in the space allowed here, but they are of similar robustness.

This model of 54 models of innovating shows readers what might happen if 5 or 6 diverse such models got applied not a single right-y model from one professor trying to defeat other professors. The idea of broad, diverse balanced (one model chosen to compensate for flaws in others) repertoires of models is new and completely anathema to academics and academia—but such combinations of models produce results and impacts [26].

6. 27 WAYS TO INNOVATE We all know what the books and media say about innovating and the self praising models of academics (where innovation always comes from smart analysis enabled by expensive professor consultants for some reason). To get beyond their noisy chaff, 40 product managers from Fortune Global 500 firms were SKYPE-interviewed on what they were innovating, how, and why. Highly unusual interviews were delivered using protocol analysis items from expert system programming and process modeling items from total quality methods. A couple of example items are given now. “If you subtract out forced upgrades of software,



hardware, and network infrastructure that regularly appear, what remains of innovation in your actual personal systems of work? For each innovation you observed or experience in the last couple of years, how much that was actually new did it install, describe in your own words and score on the following scale?” Some descriptive stats: mean age: 45, range 34 to 66; time at this employer--mean: 11 years, range 1 to 22; years of graduate education-- mean: 3.2 years, range 1 to 6 years; 38% American, 33% European, 29% Asian. The result is the 26 ways to innovate and issues in doing it given below:

● One Lie Big versus Liebig---Lie Big (the American way?) or Liebig (the German pharmacist who invented research universities)—innovate by becoming more German, that is, empower masters at the top, drive second best out to be masters elsewhere, the genba learns by working under master orders, parallel tracks of mastering past practice while at the same time going beyond past practice (organize past practice learning around future practice inventions = opposite of colleges that force dozens of past practice courses before getting to present and future techniques)----- a German pharmacist invented the modern research university by training disciples not on past work and examples, but assigning each a different unsolved organic chemistry problem to solve and publish—German SAP French CATIA out-did USA software via similar excellence of math and model

● Two Repertoires Not Rightnesses--replace single right-y models of innovation with large diverse repertoires of models, (my 45 models of innovation as an example)----- academics each seek One Right-y Model of innovation (my model is better than yours type adolescence) but when the best such models get applied, pitifully small increments of change/innovation result = no single academic model can be trusted for producing real impact; single models are illusions;

● Three Shut-Out Innovation Noise--shut out the background noise of innovation from career forces that exaggerate the amount and utility of it in firms-----career advancement most depends on visibility to top ranks, such as doing innovative things produces—this generates a continual phony background noise of “innovations” that only help careers not firms

● Four Embrace Emotional Work of Innovating--admit the emotional work of

doing home-run innovations—undoing cultures and selves----- longest biggest innovations un-do deep cultural unexamined parts of selves and cultures—men avoid the emotional work involved and flee the biggest innovations as a result

● Five Remove Excess Male-ness-es--learn to feminize systems and products as a short cut to major innovations-----most innovations feminize work systems—for obvious reasons—but most engineers are male hence invent “technical” “device”, that is, male ways to achieve such feminizations of system;

● Six Rebalance Professional Cultures--continually rebalance the cultures of engineers, marketers, and managers for innovations that sell-----when engineers dominate firm cultures, too many features and buttons proliferate; when marketers dominate, too many me-too features proliferate; when MBAs dominate, present profits erode all future capabilities

● Seven Manage Invent Audiences--lean systems lack the slack, hiding places, dis-focus that innovation requires so innovating often requires un-lean-ing-----big organizations suffer from simultaneously too much audience and too small audience for each candidate innovation—being “lean” forces causes to be visible, but when early innovative ideas are visible too early, they die, babies beaten up by adult ideas already funded nearby

● Eight Achieve Directedness--find new ways to deliver the 64 basic functions of leadership other than fixed inventories of a special social class of people called “managers”----- big organizations suffer from total lack of directedness because leadership functions are being delivered by ineffective fixed inventories of people, a special social class called “managers”

● Nine Increase Social Indexing Levels--measure the social index level of a group then how new devices, leaders, systems change it if you want big innovations in performance-----big organizations when measured are found to operate at terribly low levels of social indexing (between 3 and 7%) while high performance teams operate at social index levels above 20%

● Ten Totalize New Bodies of Knowledge--totalize and globalize bodies of knowledge (other than quality knowledge)----- the biggest single innovation in manufacturing and engineering in recent history was the


total quality movement, where one body of knowledge,

● Eleven Totalization & Globalization of Bodies of Knowledge---quality, was “totalized” 22 ways and “globalized” 33 ways--what other bodies of knowledge if similarly totalized would produce massive innovation world-wide

● Twelve Cortex-ualize the Web--see the web as a civilizational, cultural cortex for all that is human then play your role in that indexing cortex layer of the planet----- the primate brain for eons had smart modules for vision, hunting, monkey troop empathy, territory fights, vastly improved by an outer cortex that indexed all that via abstract ideas, categories, and words, the web is such a cortex across cultures, markets, civilizations;

● Thirteen Innovation as Anti-Matter, Dark Energy--understand that big firms innovating is an oxymoron---innovation un-organizes----- big firms are good at taking variety of human, market condition, technology inputs and outputting standard product/service functions at standard pries to much of the planet---they turn diversity into uniformity—but that is the opposite of what innovation requires, hence, innovating is dis-organizing, un-doing, un-learning, anti-big-ing.

● Fourteen Install Californias--see Silicon Valley as anti-blue-suits, anti-big-ness, anti-central-media, anti-monkey-hierarchy-----interviews of 150 Silicon Valley founders revealed a deep anti-ness to all they aimed for and did---un-organizing, de-big-ing, T-shirts not blue suits, democratize monkey hierarchies and media

● Fifteen Get Natural Selection Innovation Operators Right--replace mechanosense with biosense as our new commonsense = imagine what is the biosense version of everything we have and intend-----bone is stronger than steel---it grows where it is stressed and repairs itself where cracked = change our ideals of excellence, but also revise our amateur image of natural selection (it is not optimizing fitness via variation, combination, selection, and reproduction)

● Sixteen Learn Inter-Relations of the 18 Novelty Sciences--study and apply innovation as one of 18 NOVELTY SCIENCES—how it comes from and spawns design, creativity, discovery, invention, self making, career making, systems thinking and 11 others-----design crippled by rote blind

appeal to mere brainstorming as the doorway by which the new enters—60 design approaches from actual designers show at least 120 distinct doorways beyond mere brainstorming, including social automata

● Seventeen Turn Heroes Into Operator Sequences--decompose Steve Jobs, other innovators, into operator sequences: a) democratize technical capabilities (commercialize hobbies), b) layer types of appeal (design beauty on top of function sets), c) redesign for price-cost-points 5 years hence, d) master content for new democratized technical capabilities, e) generalize user need/want imagery (library products not delivery channels), f) daily-life-ize democratized techs, g) automate input, use, output not just use h) generalize daily-life-ization for all media and channel types (Toy Story, music, reading, buying, texting, phoning, TV)-----Steve Jobs' career is a sequence of operations on innovation chances---that sequence is repeated in all of us, though often at smaller, less lucky, less driven scale

● Eighteen Become a Theorist--become a theorist---people masters of more theories, more diverse theories, more abstract theories notice what we cannot see and take actions we cannot image = they live in larger worlds than we do = they appear continually innovative to us-----practice can be studied (Taylor's ancient scientific management), continually improved (Japanese kaizen), peripherally observed (communities of practice a la Lave, Wegner, Duguid, JS Brown)---but the big changes, the big innovations, come from interpolating or extrapolating along abstract dimensions of difference or interpolating/extrapolating new such dimensions themselves = theorists out-innovate great practitioners

● Nineteen Humanize (De-Neuroticize) Engineers--turning engineers into people---add humanities and arts to make them capable of all forms of design (not mere function alone), add social sciences to make them capable of engaging, pleasing, and amazing customers, add management of power and finance so they can defeat MBAs-----humanize engineers---turn them into full people---by getting them to be less male, less machine oriented, less cowardly about human relations and handling emotions;

● Twenty Escape Common Cultures of Global Business---escape the big five cultures that



undermine innovation: American, monkey hierarchy, technology, capitalist, male cultures----- what we adapt ourselves habitually to traps us (always) and blinds us (all ways)---design means escaping these big five cultures—male, technology, capitalist, monkey hierarchy, Amerian cultures

● Twenty-One Temper Genius with Editing Voices from Below---a person of immense wealth, power, and success to force simplicity on teams of teams of engineers, ranks and ranks of monkey-like managers, but who does not undo superb systems and design aspects-----great leaders produce wimpy dependent narrow blind followers, only leaders corrected continually by their direct reports do not self destruct = the Unit of Intelligence is NEVER one single human, only trios of 2 women and 1 man (one man as leader is a lie and illusion, always)

● Twenty-Two Zen Design and Designing Zen--spot and ride profound idea rails, while spotting and riding growth of current nearby products/services---hitch a success rather than create a success-----zen is a practice of repeatedly watching your own mind at work till you become disgusted at how your own mind always leads you to the hell of worry, it is that disgust that frees you from your own concepts enough to see beyond them, to innovate

● Twenty-Three Inhabit the Future Early, Place Diverse Bets--place enough bets, diverse enough bets, moderate enough bets, till history/something grabs one and makes it immense---find success to ride not make some turkey idea of yours a success---you do not “make” successes, they find you if you make enough tries-----no one makes success (IBM calls Gates; Jobs expected $20,000 of Apple II sales), they try and by trying give luck many chances to strike

● Twenty-Four Find Your Love, Take it to Absurd Concentration Levels--find something you really love and do it till absurd levels of concentration = becomes a target for luck-----only things carried to absurd extremes of concentration become visible in our civilization and lives

● Twenty-Five Assess How Much of Which of 60 Creativity Models You Allow, Fund, Use--there are at least 60 diverse ways to create, most famous big firms allow less than 1/5th of them and support less than 7 of the 60 = more creativity by supporting entirely ignored models than trying harder to support

models already supported-----master the play-book on inventing/creating/designing, the 59 other ways to invent when any one way becomes blocked or does not work

● Twenty-Six Become a Story, Products as Stories--all innovation and creation and design is performance before audiences (living and not yet living) = the theatre of choosing\composing ideas, of naming them, of announcing them, of product-izing them, of launching them-----all of life is a fight for attention, interest, care = the attention dynamics of the mind (we dislike what interests us, and we like what bores us) make what is innovative, great design, creative highly transient and relative; the anthropologic release audiences seek from limits of their tribe, the theologic release audiences seek from the limits of the human condition---are what all novelty science inventions, innovations, creations, designs do.

● Twenty Seven Replace Processes with Mass Workshop Events--innovate by becoming more Chinese, that is, 200 to 2000 people in two to five 14-hour days of 20 or more parallel workshop groups, whose partial products combine to make some powerful overall result = fast doing in days of what small staffs take months to do, = massive organizational learning, = huge visibility of methods and results, = each of the 20+ workshops uses protocols from experts, so teams master expert protocols for many functions-----total quality got the world not looking vertical to bosses but horizontal to suppliers and customers; Managing by Events uses buildings as libraries of departmental skill groups, crossed by web-enabled global processes, punctuated by mass workshop events where intense face-to-face-ness makes departmental and process work viable and directed well = innovation from fast, intense, massive, visible, expertly done products of work (Siemens Invent Events or GE's Workouts).

7. 27 POINT SUMMARY AS 9---HOW REAL PEOPLE DO INNOVATION

Below I summarize with anchor images the twenty-seven points above.

● PERFORM your creativity—Creativity is a disease that spreads from one area of your life to others. Innovating is emotional work—letting go of things you built and therefore that are parts of you. Innovating is performance before particular audiences—ideas start out performing inside your mind but they must


dance among and in minds of others and in shared conversation or they die. To innovate you have to become a theorist in two ways: recognizing how all you now notice comes from past theories others put inside you while you grew up, and deliberately expanding what you notice and can do by installing new theories inside you. To innovate you become a story, you enact a drama, make life dramatic for your chosen audiences.

● INTENSITY To innovate you must get naked, that is, strip lots of stuff from in and around you, make yourself bare, exposed, unprotected. This takes the form of being an anti-person, anti-culture, anti-idea. You grab and eagerly enact the powers of negation that society and nice people avoid as impolite. You dare to dump on everyone else's sacreds. This also takes the form of installing Californias everywhere you go—places where people flee from hierarchy, status games of male monkeys. This also takes the form of living densely, till density of interacting of person and idea strips both of ego and status concerns, creating a social plasma and idea plasma where fusion into immense energy releases occurs.

● EXPOSURE use what is outside you. To innovate you have to ride the growth of others instead of trying to do growth entirely by yourself. You have to place lots of diverse bets of your time, effort, ideas, resources, attention. You have to love a few select ideas and people to extremes.

● HIGHER STANDARDS To innovate you have to relent where others resist. You have to give way, let the future rush you. You have to break into places where you do not belong. Part of this is achieving directedness of self and of others. Most leaders are tepid leaders in name only, for they lack anywhere to lead people to. They just want to look and feel strutful, important, play the top banana. Innovators increase social indexing levels in themselves, others, and groups, finding more interests, needs, and capabilities of others and getting people to act on them. Innovators humanize MBAs and engineers adding the social psych anthro polysci commonsense that turns nerds, psychopaths, and greedy narcissists into caring laid back human beings, worth being with.

● KNOWLEDGE DYNAMICS Innovators make ideas dance, with fun and love and verve and art. They ravish self and other, mind and heart with connections and separations, patterns and repatternings. They move tasks from slowly being done by small staff processes into rapid exciting mass events doing them. They cortex-ualize the web, using it as an indexer of all that lives and works, around the globe, a

kind of planet-wide brain. They get natural selection right, knowing it does not maximize fitness usually and instead accumulates large repertoires of useless neutral traits that some sudden environment change might turn into powerful survival skills.

● BE COMPREHENSIVE Innovators play the glass bead game. They role and jiggle, bet and play dice games with ideas. First they get the 18 Novelty & Creativity Sciences to interact and help each other. Second they turn the biographies of heroes into sequences of innovation operators to repeat in their own minds and lives. Third they master many or most of the 60 models of creativity and when stymied in one, effortlessly without bitching switch to any of 59 others.

● UNDO BIG-NESSES Though most of the world worships success (forgetting how most of it is luck) and bigness (forgetting the psychopath persons that become biggest), innovators use these weaknesses in us all to get beyond us all. They do not believe anyone and anything without experiment and testing. They doubt all. First they disbelieve the constant noise of innovation coming from career systems in large monkey-farms of competing males. They bathe regularly to slough the washes of male hormones at most workplaces. They undo malenesses at work to get the biggest changes and innovations. They rebalance cultures of professions, stopping MBA dictatorship over engineers, stopping wimpy engineers grovelling before finance and power skills they avoided in college. Most importantly innovators avoid capture by the five common cultures of business: male culture, monkey hierarchy culture, chasing money capitalism culture of fake products and fake variety, USA culture of snobby war-mongering autistic technology development, and technology culture of weak men hiding their cowardice behind equations and devices, afraid of their wives and daughters and all that needs care in this world.

● LEAN Everyone is the library of an innovator. Innovators have a huge mind extension called their cognitive friend network—people who perform vital cognitive functions for them. Each innovation and invention is used by them to re-index knowledge, ideas, proposals, and the world. They re-see all based on each novel system and invention. They collect not single right-y models but large plural diverse sets, repertoires, of models. They do not decide themselves, but rather form a trio of two females with one male, as that is the social unit that thinks and acts intelligently where individuals definitely cannot.



● NOBEL PRIZE FUN Innovators play with the rules, boss-fears, money-chasing delusions that intimidate the rest of us. They are free of the workplace's male delusions and hormones. They move mentally and tactically in ways and along routes those round them are entirely unable to see. Innovators feed the dragon's beating heart—that is, they fuel a dragon of ridiculosity, of absurdity. Innovators innovate not to improve the world so much as playing with fate is fun. They enact pulsed systems where isolation alternates with connection, detachment with engagement, locality with globality. They develop orthogonally, changing brand and identity after each feat and projecting new capabilities just developed in entirely new directions. They are the most feminine, revolutionizing all by making systems, products, people, events more feminine. They invest at tipping points others cannot see because innovators are theorists, framing events in highly diverse abstract ways only theory reveals. They develop, therefore, along unseen rails and avenues. Their fun serves another bigger purpose---bringing emotion into male workplaces. For fMRI studies show that emotion-denying-avoiding males thereby make themselves make fewer design decisions, unable to weigh differences of impact of alternatives on end user situations [27]. Designers, engineers, managers who feel the heft of impact differences, make more design decisions, and end up with better, and better articulated designs. Maleness stunts design quality and amount.

7.1. CONCLUSION--COMPARE THESE NINE TO THE 10 HYPOTHESES THAT STARTED THIS PAPER

I repeat here the summary of explanations that started this paper on why academic models of innovation that do not work yet get developed, published, and sold in expensive consults: 1) innovation noise from career systems 2) single right-y models lack impact 3) blindspots from the excess male-ness of all industrial systems 4) social collusion agreement to praise petty levels of innovation 5) change exhaustion from technical substrate evolution pace 6) Descartian bias of Western persons and cultures for idea over act and emotion 7) vendors push systems that erode the isolation and differences innovation requires 8) Ideas serving more entertainment than work functions 9) amounts and types of innovation as inverse U function, too much innovation undoes stability needed for profits, too little undoes manager promotion and morale 10) for show, for marketing research and innovation as useful or more useful than real innovation.

Compare these with 1) ideas performing in minds between minds 2) anti-ness impoliteness of innovations 3) place lots of idea bets where ideas and possible homes for them flow 4) stand outside of and cure the neurotic limits of cultures, persons, systems, professions 5) knowledge dynamics of the insight rhythm in pulsed systems of alternating engagement-detachment, locality-globality, emotion-ideation 6) the combinatoral glass bead game of idea mixes using comprehensive repertoires of diverse models 7) the counter-investor who avoids what others find popular 8) the extended mind not just brain mobilized 9) adding the emotion male systems avoid to make choice and design powers proliferate.

We can rearticulate the above as culture factors:

First 10 ● discount career-system exaggeration of innovation● undo academic culture for single right-y models● undo excesses of workplace male-ness● undo cultures of colluding on low standards● disaggregate change handling and leap innovating ● undo Descartian bias to idea over emotion ● techs pushed that erase innovation needed isolation● innnovation ideas for entertainment not improvement ● innovation as trade-off inverse U function---too much hurts stable execution needed for profits, too little hurts manager promotion and limelight needed for morale● for show innovation need not be real in impact

Second 9 ● culture of seeking excellent idea performances● anti-ness culture at work ● culture of tries not discussions testing ideas● stand outside usual workplace cultures● pulsed systems rhythms replace cultures of steadfastness● mixtures of large diverse repertoires of models● countering popularities and trends, norms and cultures● thinking beyond lone individual brain cultures of schooling● empowering design choice via feminine embracing of emotion.

Our initial list of 9 came from possible explanations for why top university innovation models, when fully applied, have zero impact. Most of those 9 factors suggested cultural forces for toy like innovating and against serious innovating. Our closing list of 9 came from real product managers and engineers


doing innovations of various sorts in normal firms. Their 9 factors suggested innovation involves countering human and cultural forces more than involving technology details and factors. These two lists are largely consonant. The main theme visible in both is similar---the cultural content of innovating and the dominance of human changes over technical ones in getting it done. Innovating is an operation on human norms, habits, expectations, imaginings, routines more than it is an operation on ideas, systems, technologies and the like. Professors miss it entirely—they want it to be the latter not the former.

Compare the 19 bullet-items above with the 54 models of innovation in the table much above. No models on the table correspond fully to the 18 bullet-items above. However, a few of the 54 models are close---social cognition, co-adaptation, and wave catching model types (9 models). These deal with innovation as anti-process, as undoing culture norms and comforts, and as and escape-from-bigness-and-hierarchy process.

This paper's data suggest existing models of innovation are too technocentric, too gullible about forces exaggerating innovation, too male hence blind to innovation as an anti-male anti-big, anti-monkey-hierarchy operation. Innovation as a Culture Operation is a good name, in sum. It suggests using and trusting the same old academics (largely men), the same old diffusion-outsourcing models (largely by men), will continue to leads us to new ways to innovate that do little or no innovating (outsourcing ideas leads to initial increment in creativity as formerly separated ideas blend, but rapid decay of participation to nothing as net participants get rapidly used to, familiar with, and bored by what is endlessly repeated on idea nets).

Finally this paper suggests there is a Culture of Innovation that fosters largely ceremonial pretend amounts and types of innovation (knowing substrate technology updating will deliver continual improvements without big additional efforts). So big innovations are attempted to help this or that career by this or that male manager, not to help any enterprise. This Culture of Innovation Faking is a huge factor, that, if measured, may dwarf all measured effects of all university models now published. Research to do this would offend virtually all professors and organizations doing innovation and is not likely, therefore, to be funded or tolerated, but it is vital to demonstrate how deeply we all are embedded in the Culture of Innovation Faking that this paper suggests. You could re-frame this a bit more nicely as a kind of jungle tribe culture of males in businesses---they need to dance and sing

innovation songs for morale reasons, though the songs and dances do little that is actual. Meanwhile substrates get upgraded daily and quarterly, and that is enough change to pay for CEO exorbitant salaries. In my experience only people under 40 years old find this a cynical idea.

7.2. EPILOGUEI started this paper with several things, eventually presenting a table summarizing predictive literature on manufacturing's future. Here, at the paper's end, it is “culturally” powerful to return to the technocratic, male, systems styles, views, ideas that absolutely drench and dominate all innovation discussion and systems in corporations. I want readers to read the following Epilogue using Innovation as Culture Operation eyes from the Conclusion section immediately above. Feel the gap in style and content between what this paper presents and what we all usually read and write on innovation. To wit:

Structurally, manufacturing evolves along five rails into new web spaces that technology creates. 1) The hardware layer, the software layer, the human layer learn at different rates and achieve mastery and high quality under different conditions. So innovation in manufacture has to handle these change-speed differences by layer. 2) Evolution speed of customer wants, product offerings, global distribution differs. Again, manufacturing innovators handle these layer speed differences. 3) Businesses excel at taking variable diverse material, market, human inputs and turning out global standard outputs-products. They excel at variance decrement not variance enhancement. Innovating systems that are excellent at stable reliable performance poses a fundamental, ontological challenge to them therefore. It is rather paradoxic and cannot be fathomed by simple minds. 4) In particular as the scale of what is to change increases, upper management, generations older than the new techs attacking them, become the primary political and cognitive block, and it is they who must understand, choose from, and approve myriad changes. This is the primary block to manufacturing innovation today. 5) Mobile global personal HTML5 GPS aware social systems, using Malone's coordination theory concepts, move us toward a bi-polar systems world of multi-industry fab lab cities (manufacturers as publishers), and myriad local fab-invention distributed networks of custom good suppliers (anyone as maker). The result is a fractal model of hierarchic cascades of waves of innovation and suggested institutional supports. Increasingly manufacturing is not located in merely one place, site, company, nation. Increasingly it is both distributed globally and mobile, uprooting and replanting itself.

Readers probably greatly feel at home reading the above—it is what prevents innovation in this paper's



model and view. It is a popular ubiquitous collective illusion and perhaps the primary block to serious sized innovating in systems today.

SHORT 100 WORD BIO Richard has an SB from MIT in AI software, from Wellesley College in creative writing (Bradbury, WH Auden, Lillian Hellman, Robert Pinsky) and 3 degrees from U Michigan (MA in Chinese/Japanese business, MA in web communities, PHD in total quality improvements of research processes). His first job was visiting, literally, the richest 2000 Americans for donations for an NGO doing citizen planning meetings in 41 poor nations. His first academic job was Lecturer in Quality Management & Statistics, Univ. of Chicago, Grad School of Business, after work in 7 global firms: in Japan (Panasonic, Sekisui Chemical), in Europe (N. V. Philips, Thompson Electric Paris), in USA (EDS, Coopers & Lybrand Consulting, Xerox PARC. He launched 3 Silicon Valley tech ventures (2 live, 1 died). He is Master of Design at De Tao Masters Academy Beijing/Shanghai and Professor of Design, Creativity, and Innovation at Keio University's new Grad School of System Design and Management. He is founder of THE NOVELTY SCIENCES SCHOOL and RESEARCH INSTITUTE. His Kimono SportFormal fashion lines are being sold in China in 2012. He is author of 18 books at http://www.youpublish.com/people/1201 and more info on him than his mother has is found at http://independent.academia.edu/RichardTaborGreene/About . He loves long distance cycling on his Tartaruga Type F bicycle, 3D photographs of Japanese & Chinese scenes, composing songs on the YouRock guitar by Inspiring Instruments, comedy performance, and writing poetry and books for people not yet born.

REFERENCES

[1] De La Merced, (2012) “Eastman Kodak Files for Bankruptcy”, New York Times, 19 January.

[2] Amabile, Teresa, (1997) “Corporate New Ventures at Procter & Gamble: Case”, Harvard Business Review, 23 January.

[3] Personal Communication from IBM Cloud consultant old MIT classmate of mine about Larry Huston's 4INNO (2009)

[4] Porter, Michael (1998) On Strategy, Harvard University Press.

[5] Daniele (2012) “Samsung under the Public Eye”, East Asia Gazette

[6] Greene, Richard Tabor (2008) “45 Models of Innovation” chapter in Getting Real about Creativity in Business; youpublish.com.

[7] Greene, Richard Tabor; (2012) A Model of 42 Models of Creativity, dual language edition Chinese-English, forthcoming De Tao Masters Academy Press.

[8] Tannen, Deborah (2001) You Just Don't Understand, Morrow.

[9] Lave and March (1993) An Introduction to Models in the Social Sciences, University Press of America.

[10] Giddens, Anthony (1991) Modernity and Self Identity, Stanford University Press.

[11] Damasio (2005) Descartes Error, Penguin Press.

[12] Cain, Susan (2012) Quiet: The Power of Introverts in a World that Can't Stop Talking, Crown.

[13] Woolridge, Adrian (2011) Masters of Management, Harper Business.

[14] Feldman,David Henry (1999) Changing the World, Praeger.

[15] Abrahamson, Eric (1991) “Managerial Fads and Fashions” in Academy of Management Review.

[16] Pearson and Ehrlich (1989) “Honda Motor Co. and Honda of America: Case”, Harvard Business Review.

[17] Zhang, Junfu, (2003) High Tech Start Ups and Industry Dynamics in Silicon Valley, Public Policy Institute of California.

[18] Brown and Duguid, The Social Life of Information, 2004

[19] O'Mara, Cities of Knowledge: Cold War Science and the Search for the Next Silicon Valley (Politics and Society in Twentieth-Century America), Princeton, 2004

[20] Malone, The Future of Work, Harvard Press, 2004

[21] Personal history—he was my first boss at EDS.

[22] Forthcoming, Multiple Models of Creativity, in Springer Encyclopedia of Innovation and Creativity, 2012

[23] Forthcoming, Multiple Models of Creativity, in Springer Encyclopedia of Innovation and Creativity, 2012

[24] Greene, Richard Tabor; (2012) Innovation: 54 Models; dual language English-Chinese, forthcoming De Tao Masters Academy Press.

[25] Hall and Toyoda, Japan in Muromachi Age, Univ. of California Press, 1977.

[26] Greene, Richard Tabor (2011) High Tech Venture Dynamics: 420 from 150 Silicon Valley Venture


http://www.youpublish.com/people/1201

http://independent.academia.edu/RichardTaborGreene/About

http://independent.academia.edu/RichardTaborGreene/About

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Copyright 2015 by Richard Tabor Greene, All Rights Reserved, US govt. Registered

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surface invisiblesthat make overtswork or not work,

map local blindspotsneuroses

what makes them call Xa problema solutn Creativity

is NOT:sheer novelty =

not done before

Creativityis NOT:selfexpressn.

Creativity isNOT: merefashion/style

Creativityis NOT:

sheer freedomsheer divergencerule-less-ness

enough to prior works

to aspire beyondtheir standards

but not tooverly respect

their

exposure

ways

accept NOclient need,problem, situationRETHINK reframeall dozens of ways, violateall boundaries

percept,sketch,

write, talk,discuss, model

muse, play PUTINTO CONVERSA=

TION

how would nature,aliens, robots,ancients, quanta DO IT?

bewaregeneralizatn:

discover whatwe are seeing

not seeing

preciousparadoxes:dwell in them

let them force us out of usual frames

seeings

hidden intentnarrows attention:process steps asimprov platforms,question goals

proceed but not in 1 directionnot to 1 goal =try X for explore

not to solveproceedingthat splits,

repeats, reframes,changes goal

&direction

reverses,

guessing askind of logic:invent processreinvent goalinvent thenew

dwell in/onuncertainties,

explore them,see what they

keep open for us

solving the problemof MBA direct

problem defining & solving

male-ish

NIGEL CROSS:personality

methodsworkstyle 6 method

design strategies

reframeproblem,

aim at impossibles,holist view,pulses,contradictory

tries, proceed inparallel

many directions

CONTRADICTIONS:client needs, designeraspiratns, physics/tech-means =circularconversations

EXPERTISE--from trial percepts,discount reals/ideals,speedy accurate routines,

fast focus & solving,meta-operation

fast casebased solving of

obvious parts;explore unobvious,

diverse triesaccount forexpected human/orgflaws--trojanhorse tactics,wrap bold inagreeds

MARTIN:Mystery-Heuristics-

Algorithms:software + Asian

thinking in each =

compile--obvious questions to deeper ones,

rapid prototypes to new alternatives untried,

promissing prototype to solid repeated

procedures forms

go West to CA then Japan

KUMAR:catch idea/need/

hype waves to reimaginegoals/means/worth/

opportunity

locate goals/needs/etc.

inside ecosystemsof interacting firms,forces, ideas, trends,

markets; anthro=live inside client worlds;

turn fog into models,find frames, expand/invent new better frames/meanings;try all that is bold,

proliferatedirections shuffle novelties

till great few patternsjell out = ways to go;explore test realiza-

tions alternatives

DORST:formulate fogs,

repreesnt diversely,moving framings,evaluating promises,

parallel lines ofdoingfind hidden problemgenerators; use paradoxes

to reframe; find \deeperprob to solve; context;

drivers/congeal

frame

expand find deep themes; new

KEELEY:DESIGN OF--

profit model,eco location,relations structure,

processes,product

performance,

DESIGN OF--productsurround/system; service, channel, brand,

customer engagement

the endof single

right-y academicmodels

replaced by repertoires

of pluraldiverse

models

54 excellencesciences=how8000 rose topof 63 fields in 41 nations

Design, Create,Invent, + 27 othersCREATIVITY

SCIENCES = several of the 54

cul-de-sacDesign community

and Design culture=competes with

29 other Creativity

Sciences120 creativitymodels as grammarof design processcontents/singularities

PLATO’squestion:

What is “the good”“excellence”

in our time?=designcontext

empirical/sciencebasis of defining them:

8000 from 63 pro-fessions/41 nations

mugebuddhist

no obstructiondesign; trans-

parent processesTQ Japanese design

TQ systems

totalizatn.design;social

connectionism,social

computationdesignJapan

Japanese & Taoistaesthetics: YIN

negation of YangTaoist design

embody pluralcreativity sciences

design

stewingnew techsdesign

knowledgere-organizations design

reality & imagination

sciences design

ComputationDialog design; Totalization of bodies of

knowledgedesign

designs alleviate

anxieties of existence; designs

that

fixing dysfunctions of belief

designsfixingus-themexaggerations

into devaluingdiffering others

design openingscience sources of awe, humility aslocal emotionmanaging

congregationforms selves, design,

culture, high performance ALL

THE SAME

designsenabling dynamics

of making/managinga self

everyone isa designer, of:self, life, career, family=self consciouslyevolvingsystems

designsmeeting

challengesto being a

self in today’scircumstances

design by selves,for selves, as selves,as self expression

designsenabling the 64

ways organizationslearn

designs thatfind and use

knowledgetipping points

design of geometries ofthought & toolsthat enable

themtools expanding:

coverage, detail,diversity, levels of

thought operations

designs of

knowledge idea &

dynamics: waves,dialectics,

evolutionnaturalselection,fractal structure,recursivefunctions

loser ideainclusion in

victor ideas,then used

2 generationslater in overthrows

recompile ideasacross fields/formats, idea flows & homes

brainscapemindscape

socialscape

culturescape

bioscape

systemscape

technoscape

emotionscape

computescape

genderscape

the insightprocess

dimensionsof difference

design as un-design

breeding ideas,crossovers,

mixes

culturecrossing

un-doingcultures

nestedanti-cultures

designprocess

topologies

rootthinking

transparent- izing

re-constitutingthe world:

emergentsas designs

counter-

designs

fractalmodelexpansions

nestingstrojandesigns

magician mis-directiondesigns

minimalelement

grammars=designs assentences

void probes

social

design

automata

massworkshopdesign events

designercleavage

shiftsstratifiedrespondingsresponse to

responsematrices

consequenceas environmentsmatrices

culture ofdevices vs.

culture of usersmatrices

feminiza-tions

cultures of risk, development,highperformance

social indexlevel changing

designs

social processrebalancingdesigns

globalizatnsof bodies of::percept,concept,know-ledge

64purposes

of all arts

extending mind extensions

biosense- izations

kinds of space

kinds of reflectiontheory power

persuasion dynamics

leadership function--alternate delivery

vehicles of them

new unit of intelligencein NOT any 1 person

180 flaws inthought,

Western/Easternforms of causality

story design,stories as insight processes;

DIETER

useful, aesthetic,understood

unobtrusive,honest, lasting,to last detail,eco,

designing

RAMS:

minimal

NORMAN:visibleoptions, feedback,

signage,mappings,constraints,

consistency

VISUAL ELEMENTScolor, uses, colorattributes, shape,shape types,texture, spaces,layouts,

forms INTERFACE:task suited, self explaing,

controllable, expectatn fit,error tolerant, customizable,INFO: clear, distinct, concise,

consistent, detectable, legible,understood

INTERACTION

consistent, shortcuts, feedback,dialog closures, error capture,

error recovery, action reversal,internal locus of control,

low memory load

TRAITS:

SOFTWARE DESIGN:extend without modify,insert abstractn betwn modules,keep interfaces void of work fns.,keep object pure =

derived classes able to replace derived from classes

1 reason to change only

APPEARANCE DESIGN:unity/harmony,

self organizing forms,balance, symmetries,

similarities/contrasts,hierarchy, scale proportion,

dominance/emphasis

HOW THE BRAIN WORKS--HOW SOCIAL RELATIONS WORK--

design elements must engage how within & between mind

dynamics actually operate(policy=behavrl econ)

APPROACH GENRES:root think, computational

sociality (& swarms),minimal grammars, user needuser routines engaging, cricical designs, service around

product design, co-aginglifespan thru designs,

speculative designs

DESIGN METHODS:problem space to

solution space dialog; re-specify;

invent process,prototype goals/

processes/products,trendspotting

DESIGN AS META-DISCIPLINEdistinguishes professions from sciences; the practice of getting from pasts/thrupresents/to futuresconverts ideas intorealities into new

ideasPRODUCT DESIGN--

fashion, industrial, auto,

app, interior, architecture,

landscape, urban, graphic

SYSTEMS DESIGN:info architecture,

instructional, interaction,modular design,

process, service,mass multi-field,

DIGITAL TECH DESIGN:apps, devices, interface,social life of info,software, web,

user experience, haptics,virtual & 3D

game

ART & EVENT &INSTALLATION DESIGN:

applied arts, materialdesign, experience

designs, playgrounds,fantasy worlds, music, concerts,

theme parks, movies,

novels

CIVILIZATIONAL CHALLENGE & DIRECTION POLICY DESIGNS:

global warming, anti-terrorism, drug handling, economy tuning, welfare updating, war/peace, refugees, earth protection

PERSONS & GROUPS:

clients, stakeholders,imagined stakeholders,swarms across the web,

IDEA OPERATIONS:culture unwrapping,

undesignings,community of idea

conversations,idea wave

catching

designer professionals

CREATIVITY MODELS:scale changes, dimensions ofdifference, solution cultures,extreme product extrapolate,social process balance,tipping point finding

GENERAL CHANGES:trips, substrate updates,culture crossings, inhabit the

future, de-masculine-ize,counter brain flaws

DESIGN METHODS:social automata, glass beadgame modules, brain mode mixes, landscapes of

design, extreme metaphorsDESIGN PROCESS--

framework tries-shifts,120 creativity modelsconflicting at processsingularities,

420 dynamics,apply principlesacross designgenres

Silicon Valley

ROT THINKING:

find problem generators,find goal generators,

find causes East/Westfind solutions

find implementations,find errors/impacts,

normalize solutions

EXTRAVAGANT THINKINGS--transplant across global religions

of business, use methods fromother professions, design to wipe

out client goals/needs not provide solutions

The InsightProcess &

The DespairDoorway

PersonalGrowth

Process ofChanging

Be X to Have X

Change OutThere by ChangingIn Here--Going OutRequiresGoing In

Design asSolving the

Problem ofUsual Problem

Solving

Proceed byReversal,

OrthogonalsCircuitous,Routes to

New Goalsnot

Solu-tions

All Designas Un-

(Whole WorldComes to UsAlreadyDesigned)

Designing

Invent:goals, needs,

contexts,frames,

processes,results,

simultaneously

Strip Away What Blocks Creativity in Lives, World, Designs, Clients, Designers

230DESIGN

DYNAMICS

DESIGN DISGUISESGREATNESS INACCEPTABLES

DESIGNS NUDGEALL TO FOSTER

CREATIVITY

THE GREATESTDESIGNSEMERGEDWITHOUTHUMANINVOLVE-MENT

ALLDESIGNS

AREUN-DESIGN-

INGSANTI-

CULTURESPRO-

POSSIBLES

Design of News Ways of Thinking,

Design of Tools thatEnable Them

by Richard Tabor [email protected]

THINKSTRUCTUREgeometriesof thought

THINK STRATA read points, causes

emotion, etc. hear; noticings, feelings, etc.

THINKMODELS:

model based seeing, acting, reading

THINKORDER:

regularized--count, names,order

THINKGRAMARS:

minimal operator operand,

combines

THINKFEELACT

GEOMETRIES

THINKMIND-KINDS fast emotion sets what we slowly notice

THINKCLEARLY

counts, names,order, response

needed, time

THINKSIGNAL/

mark geometriesof thought, strata,

models etc.

CLUE

THINKCULTURE

transpant Xacross

dimensions, powers

cultures,

THINKRECURSION

(meta-thinking)meta-cognitn.,

meta-creating, meta-design

ADAPTTHOUGHT TO

MODES

THINK NEWUNIT OF INTELLIGENCE

THINKWITH

EXTENDEDMINDeducate

minds not just brains

no lone males leaders = trios of women

+ man

THINKINDEXIALLY

what increases social failure, brain index levels

THINKORTHO-

GONALLYdeeds to

new ID to new

biggerdeedsTHINK FIX MIND

FLAWS wrap intentin mind flaw countering

packages

EFFECTIVETHOUGHT

THOUGHT

THINKCAUSALLYdeep,

distributed,multi-level

delayed

THINK RHYTHMICALLYpulsed not connect

systems, insight process pulses

THINKBALANCESrebalancefractalmodels, fix social

neuroses

THINKCOMPREHENSIVELYtheory repertoiresguide noticingsalternativesimagined/

done

THINKPLURALLY not

single right-ymodels but

repertoiresof models

GRASPREALITIES

ADULT &EDUCATEDTHINKINGhave yourInitial FactorySettingsinsteadof beingthem

THINKDEMYSTIFY

take back powerunconsciously given

see hidden interests

THINKCOUNTER

de-mystifyundo neuroses

anti-cultures

THINK LEADnot person

but function,make/keeppromises =

power

THINK PERSUADE

simple similar gift-guilt familiar

extremes sandwichconform to

self/others

IMPACTFUL

THOUGHT

ReplaceMeetings with:scientific rules oforder, all attendeeslead handling some topic

ReplaceDiscussions

With: stratifiedrespondings =all hear layersof reaction by all

THINKNON-LIEARITY react to

reactions, step 2 envt.

step 1 different than

THINKEMERG-

vast complexityof actions/patterns

from iteration ofvery simple rules

ENTLY

THINKFRACTALmulti-level causes-effects =

Schelling Mandeville

THINKCOMPUTA-

TIONALLYsocialautomata,computation

type dialog

THINKEVOLUTION

survive via largestrepertoire of now

useless traits; tune ownmutation rate

NON-LINEARTHOUGHT

THINKABSTRACTLY dimensions of difference

THINKDIFFER-

metaphor,solution

culture,extremeproductextrapolate

ENTLY

THINK DISRUPT fallow resources now data can find/use, Jobs’ void powers

THINKCREATION

flows & homesgeneralinsightprocess

THINKREVOLUTION

liberation,found freedompublic happinesshistoric

dream

CREATIVITYTHOUGHT

Replace Work Processes& Classes With:mass workshop

events

ReplaceBrainstormsWith: social

design automata

ReplaceProse Text

With: fractalpage forms

that showpoint count,

names, order

Replace Personal

tipping pt.find-

ing

Influence With: envts. to adapt

oneself to

Replace

cultures

Usual SolvingWith entire system

tweaks + find tipping points + erect anti-

1

2

3

4

5

6

7 8

9

10

11

12

13

14

15

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20

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THOUGHT DESIGNSTHOUGHT DESIGNS

REDESIGNING DESIGN: When Technologies & Grandmoms Conquer Fake Variety & Design CowardiceBy Richard Tabor Greene, Master of Creativity, Innovation, & Design at De Tao Masters Academy, Beijing-Shanghai

Professor at KEIO System Design & Management Grad School email: [email protected] Great Design must Amaze = Catch Attention, It Must Engage 40+ Brain Modules for Interest, Liking, Attention

SOME DESIGN STORIES

ANA AIRLINES: DESIGNING BUSINESS CLASS IN BUSINESS CLASSIn business class flying to Beijing, the man next heard me refer to wine-sleep-read class seats. This led to a mini-workshop on design of Real Business Class—seats etc. designed for doing work on 17 hour flights from Manhattan to Tokyo or Beijing:1) seat that turn facing other seats with table between for 4/6 person meetings in flight2) displays on seat backs of partners in non-adjacent seats for video conversing3) cafe food service from menus people order from at any time (no aisle carts)4) sound privacy screens around seat pairs and quads MAIN POINT: SPOT PRIOR DECADE CONTEXTS PRESENT IN PRESENT DESIGNS & UNDO

GIANT BICYCLE: THE COURAGE OF NON-DESIGNER DESIGNERSCapitalism is full of fake variety : 100 shampoos with 99% the same ingrediants; R&D groups that invent fake “improvements” aimed only for advertisement contents; bicycle stores with 1000s of identical bicycles. SO I suggested that Giant in Taiwan form a committee of grandmoms to design a new bicycle—result: the Revive Spirit:1) chair type seat sliding on rail2) driver rotated from racing position 45 degrees to upright (crank forward)3) handlebar switchable from open (entry) position to next to stomach (no shoulder ache) position MAIN POINT: DESIGNERS ARE TRAINED INTO WIMP-Y-NESS BY SCHOOLS & FIRST CUSTOMERS = NEED COURAGE

HERMAN MILLER: ALL DESIGNERS ARE BLINDMost designs leave out half the human race. To wit, the chair. Design-by-cleavage = putting new TYPES of people in charge of all design roles. Women designing chairs for Herman Miller corrected 8000 year old design errors = made height and tibia length adjustments so chairs fit women. Just reported by USA---1,100 women a year die in accidents that men survive due to chair safety equipment in cars being sized for men not women! Design blind spots are killing 1100 women a year! MAIN POINT: FAKE VARIETY WITHIN DESIGNERS AND DESIGN FIRMS REPLACED BY REAL VARIETY IN WHO DESIGNS AND HOW THEY DESIGN = REPERTOIRES OF TYPES OF PEOPLE, TYPES OF CREATIVITY MODEL.

GENERAL DYNAMICS F-16 FIGHTERS: HISTORIC SCALE SOCIETAL NEEDS = LONG TERM DESIGN APPROACHESSo many designers are trapped in mere intuition---lacking the strategic knowledge and dedicated strategies that deliver decades of designs that meet huge long term growing societal needs. In our day, information is growing exponentially so all of us are lost all the time in it, and information functions are coating everything in the world. This leads to anti-busy-ness designs that make inputs to systems and situations easily more well ordered; this leads to new forms of writing and display where patterns of visually stunning regularity context all individual idea contents. Fractal page models replace PROSE ITSELF as a new web-era form of writing that self indexes its own contents visually. MAIN POINT: ORDERINGS REPLACE VOLUMES AS SOURCES OF INFO VALUE TODAY = DESIGN STRATEGY

KIMONO SPORT-FORMAL FASHIONS: GETTING EXPERIMENTAL DATA ON SUIT DESIGNS ENTERING ROOMSIn one door ordinary celebrities and CEOs with wives entered the ballroom, in the other door students wearing my own suit designs entered, with mezzanine cameras counting which door got eye attention first and held it longest. Unusual, unexpected results appeared---humanly done random patch patterns got attention slower and held it less than computer-generated random patch patterns; assymmetric patterns got and held attention better than symmetric patterns; suits combining incompatible design styles in the ratio of 63-37 (area of coverage) outperformed all other ratios, and many others. MAIN POINT: DATA FINDS DESIGN PRINCIPLES NO DESIGNER INTUITION CAN FIND

UN-CULTURING DESIGNS

Everything in the present world was designed out of some past context/need, most of them no longer dominant or viable. Present designs as results of problem cultures continued.

Designers go to design schools and pick up a wimpy design culture of cow-towing to finance & power & conservative customers. All fit designs need pairing with challengers

Designers who stay merely visual, pawing through design photos & examples, never pick up abstract deep ideas for real design innovation

MAKING PRESENT (adapting to present) past designs, COUNTERING DESIGNERS and what they like, DIMENSIONS OF DIFFERENCE spotting leads to real innovation in designs

DESIGNING IS CULTURAL WORK to a great extent it is un-doing what made past designs, and what made you yourself, and what now makes everyone competing around you.

BIOs AS DESIGN OPERATORS: Steve Jobs

GENERALIZE YOUR “I WANT”S1) sell it 50 PCs by hobbyists only Jobs was so poor he had to sell now = hobby to product 2) idea theft visit Xerox and immediately apply their ideas to your work idea tours always

SOCIAL LIFE OF ALL DESIGNS3) different is good delaying MacIntosh to cool without fan noise inflate differences 4) different is bad he optimized the product/design not the users app investment & failed = ignore not the social life of designs

PAIRS LEAD: POINT + CORRECTOR4) self is enemy he opposed Toy Story but, fired, he relented to his engineers and Toy Story won = admit own limits at times strategically 5) entire media industry structured around past non-digital technology = new substrate to old functions designs after Pixar

RIDE SPEED-OF-CHANGE DIFFERENCES7) design for 5 year out component price points the Next computer was designed to be affordable 5 years after release = the future is expensive now 8) update concepts not functions the walkman played part of your library of music, the iPod played all = when quantity become quality difference

SOFTNESS OF HARD THING DESIGNS9) iPad on 3rd try failed without interface for viewing, playing, browsing = “voice is coming” replacing old tactile inputs means 10) iPhone, iWatch, iContacts, iFashions all objects become system info loci = strategic direction for many redesigns

DESIGNS as CULTURES INTERACT-ING

64 Dimensions of the CULTURE of: 1) Designs devices have their own cultures (attitudes/behaviors they foster)

64 Dimensions of the CULTURE of:2) The Social Practices that surround particular Designs these have their own cultures too

64 Dimensions of the CULTURE of: 3) Users (practices inside them from past buys/lives) Users have habits and thing arrangements optimized for past designs

The INTERACTIONS of these CULTURES:A) designs that require new practices have to have great interfaces supporting themB) designs with easier, faster to learn practices can supplant prior designs/practices

PREDICT SALES, PRICES, VERSIONSUsing culture interaction matrices we can predict future sales, pricing, and versions of products. We can predict design fates.

BRAINSTORM-ING IS A DISEASE & Hiding Place

50 Years of Design ResearchRESULT: the only way the NEW gets into heads—brainstorms. But nothing from that that real designs and designers can use—just more and more ineffective brainstorms.

The PROBLEM with Brainstorms1) NON-INTERACTION others' ideas are seen but not used 2) RAPID CONVERGENCE on first idea to appeal not best idea 3) FRAMES UNCHANGED ideas the win brainstorms do NOT challenge existing cultures of use and frames

ALTERNATIVES THAT WORK BEST1) IDEAS MUST INTERACT we want each person to use and operate on ideas by others 2) DIVERSE RESULTS need to be generated that explore design possibility spaces 3) PRESCRIBED MENTAL OPERATIONS great designs oftimes require unusual mental operators be applied

COMMON ALTERNATIVES THAT WORK1) IDEA AUCTIONS people have limited point to allocate to “winning” design directions/ideas they favor2) DELPHI PROCESS proposals in writing/video are edited and commented on by all via circulating documents3) EDIT REPERTOIRE TEAMS teams assigned improvement points of view to apply to proposals4) CREATIVITY MODEL REPERTOIRES several of 60 models of creativity applied

BEST ALTERNATIVE:SOCIAL AUTOMATA people arranged in arrays with each person/pair assigned specific mental operation to apply to intermediate designs handed them by other persons/pairs, iteration at small time intervals accumulating alternative designs for final whole team evaluation .

WHAT THE WORLD'S BEST SAY

GIANT LIBRARIES = many students become designers to avoid reading and depend on intuition but the greats read in dozens of fields 1000s of books

MORE THEORIES MORE INNOVATION = people with more theories notice what we miss, live in bigger worlds of design possibility = the social neuroscience of design FRONTIER

MASSIVE COMPREHENSIVE CHECKS =the best have internalized huge comprehensive models of aspects/dimensions to check

TROJAN HORSING, WORKS IN SELLSAll designers dream of ambitious bold clients but get conservative = fit what works inside what sells Design Sneakiness

FRACTAL EMBEDS OF ABSTRACT THEMESThe best embed strategic long term design themes fractally in every size scale of a design from social, culture, practice to item.

mailto:[email protected]

RESULTS: A TENTATIVE MODEL OF THE DIMENSIONS OF AN ANTHROPOLOGY OF DESIGNDESIGN AS: UN-CULTURING, ANTI-& UN- DESIGNING, CIVILIZATIONAL SELF NEGATION

FORCES INFLUENCING DESIGN (Design's Evolutionary Dynamics)Design as anti-right-ness Globalization Sociology of knowledge flows Relativize absolutes Business religions Web/media/devices & social

computational analogs

The globe as designer: creation automata

Each home a global museum = CULTURES INTERACTING

Communities of practice & scope of trust networks = CULTURES OF PRACTICE

“The” way becomes “a” way = CULTURE REPERTOIRES replace my culture is best

Blue suit religion replaced by T-shirts = CULTURES CHALLENGED

Expansion of who designs = INCREASES IN CULTURE INVOLVEMENTS IN DESIGN

DESIGN ONTOLOGY DESIGNER DESIGN PURPOSE DESIGN PROCESS THE DESIGN ITSELF DESIGN KINDS

Crossing fields • From either art of science to both together = CROSS FIELD CULTURE

• At rare tipping points or at whole system tweaks = CAUSAL CULTURE West or East

• As meta-ness: meta-design, design of design processes, recursions = ALGORITHM CULTURE

• Of experiences of designers, of users, of self of others = CULTURE OF PERFORMANCE ARTS

• Of design processes and design events = ALGORITHM CULTURE

• Lock in of design process to right ways or design objects to first wins not best due to learning costs = ACADEMIA AS CULTURE BREAKER/EXPANDER OR CULTURE-BARRIER

Un-doing bias • Culture of designer anti-science pro-art bias to educated designers = CULTURE OF DESIGNERS

• As de-industrializing = STRIVE TO UN-DO STRIVE ORIENTED RAT RACE CULTURE

• Done by brainstorms, by TRIZ = limited fractions of all possible Social Automata = CULTURE OF ALGORITHMIC USE OF HUMANS

• Archaeology of design = everything extant is “a design”, decoding what it was designed for, to do, then updating for today = CULTURE OF DEMYSTIFICATIONS

• Indigenous wholes = INDIGENOUS CULTURES

• industrial fragments = INDUSTRIAL CULTURES

Scale & scope expansion • Lone to crowd/wiki via web, to whole culture designing in indigenous cultures, to all in the social computation that was TQM = INCREASED CULTURE DIVERSITY

• For media amplification = POP CLTURE BUBBLE RIDING

• Social automata with cooperating or competing plural models of creativity or innovation etc. = CULTURE OF PLURAL APPROACH ESCAPING RIGHTNESS

• Scale of designing monumental, ideational, human = AUDIENCE TYPE CULTURES

• Designs of proesses, events, devices, interfaces that never existed before = INVENTING USER EXPERIENCES (Jobsisms)

Design deployment • Professionals to users = DEMISE OF PROFESSIONAL CULTURES

• As de-organizing = VENTURES AS ANTI-BIG-FIRM CULTURES

∙Capture/enhancement of everyday designing by us all, like MIT neighborhood make labs = WHOLE POPULATION DESIGNS

• Of objects, processes, events = FIRMWARE CULTURE LEVELS

• Designs of events, systems, flows that turn ordinary non-design activity into design outcomes = INTRASTRUCTURAL DESIGN CULTURE

Negation power • Male (now society-wide dominant, negation Y chromosome) to female = GENDER CULTURES

• As un-culturing, de-designing = DESIGNS AS ANTI-ESTABLISHED DESIGNS CULTURE

• as in general an anti-ness = NOVELTY AS UN-DOING

• As un-culturing = DESIGN AS UNDOING ENCULTURATIONS

• Designs as anti-designs, undoing prevalent designs, Palm as anti-Zaurus, Flip as anti-Sony = NEGATION AS WHOS, PERFORMANCE

• Moral fits or challenges to slots/niches = CONSUMER CULTURE ESCAPES

Audience • The designer as performer = SHOW CULTURE, ARTFULNESS

• For flow ecstasy = UNDO CULTURE OF DEVELOPMENT, DE-CIVILIZE

• as performances = LIKE VS. INTEREST BRAIN TRADE-OFFS IN AUDIENCES

• As culture penetration, operations on culture, blends of cultures = DESIGN AS CULTURE WORK

• Designing the designer, the design release, the design functioning as performance = AMAZEMENT, SHOW BUSINESS CULTURE

• Designing processes, releasing designs as performances we design = META-SHOWS, META-PERFORMANES

Model repertoires replace rightnesses

Equals 3 LEVELS: NOVELTY WITHIN, AT, BEYOND CULTURES: design protocol singularities are where creativity models clash; 54 models of innovation each with different roles for design and culture

Equals ESCAPING ACADEMIA'S AMERICAN BIAS escaping academia's dominance by American culture's mania for single narrow empiric models, impact from repertoires of diverse models not single right-y “my model is better than yours” adolescence

Equals CULTURE EVOLVING, CULTURES AS ABANDONED NICHES natural selection not the optimization of fitness via variation, combination, selection, and reproduction

Equals AUDIENCE CULTURES DIFFER designers are a different audience for designs than users/receivers of designs—both engage designs as performances

Equals DESIGN PERFORMANCE CREATES CUTURES the story determines the meaning ,value, salience, sales and historic worth of designs = Steve Jobs effect

Design as art, dance, performance, show

In relations to design, innovation, creativity models

In relation to plural not single right-y models

In relation to evolution processes In relation to performance In relation to composing stories and games

DESIGN AS NOVELTY SCIENCE (1 OF 18)--Its Relation to Inventing, Innovating, +

AN IDEA VALIDATED TODAYCompanies like White & Case, Baker & McKenzie hire small companies and individuals, for $20,000 to $50,000 a week, like Jump (Dev Patnaik), Ideo, Kotter International, Vijay Govindarajan, Ram Mudambi, & Christensen to think creatively for them, in effect. BUT your own people do not develop into legal pioneer inventors this way.Dade Blackwell, Myhrvold Associates, P&G Corporate, other major IP firms have hired Harvard & other professors who tweak legal thought and firm process variables in statistically valid ways to create legal excellence without real technology literacy. BUT after millions spent impressively they get trivial increments in novelty and improved case types and handling. We at Law & Justice Design studied ALL their cases, theories, publishings, and persons. We have measured the number and size of their flaws. You do not swap money for MERE appearance and stat impressiveness with us. OUR methods actually work with LARGE measurable impacts, not mere bragging points. WE CREATE LEGAL PIONEERS, new legal service inventors, out of YOU!!

OUR PRODUCTS & SERVICES:

14 Seminars for Legislators, Legislatures,

Lawyers,Judges, Mediators, NGOs

SITUATION ISSUES EMERGE

PRIVATE ADJUST-MENT EMERGES

NORMSPOLICIES STANDARDS EMERGE

MEDIA ISSUES EMERGE

LAWS INSTI-TUTIONS EMERGE

ADJUST-MENT OF LAWS

ADJUST--MENT OF JUSTICE

Reframing Methods & Culture Methods

Leadership Methods: Community of CareNegotiation-MediationMethods

Processes for Emergent Design of Guidelines & Norms

Processes for Emergent Design of Standards and Policies

PR design Before PR Need & Tragedy Prep

Design Approaches for Behavioral Compliance

The Social & Technology Life of Laws, Enforcers, and Sanctions

The Social, Emotive, Intellectual, Media & Technology life of Norms, Cares, and Outrage

Expert Witnessing--APPARENT EXPERTISE VS. REAL EXPERTISE

--sticky messages, social neuroscience, social psych, media image

Legislating--DESIGN SCIENCE FOR LAW DESIGNS

--in/dis-centives--behavioral legality--norm deployment--sneaky law design (intending 2nd order + delayed effects)

Causality in Law--ATTRIBUTION FORMULAS

--distribute responsibility via science-validated causal chains--distribute causes and consequences via science-validated causal chains

Interfaces for Law--REGULARIZED FRACTAL CONCEPT MAPS

--accurate vs. deliberately obfuscating names--indexing--regularizing trees

Predictive Law--CONCURRENT DESIGNS (Laws asInventions/Ventures)

--morphological forecasts of new laws--case based analogs--dimensions of difference law designs

Redefining Law--REPLACEMENT FOR LAWS

--the injustice via legislative speed gaps--exponential acceleration in justic gaps--legal biosense organics

Technology is Accelerating, leaving justice & laws behind!

WH Y US?W H AT W E H AV E

THAT OUR COMPETITORS LACK▼ Tools that make thought more: comprehensive, diverse, detailed, multi-scale, appliable—fractal concept models cause-distribution maps, tipping point locators▼ Replacements for prose—an ancient ineffective inter-face for writing—fractal page models▼ Replacements for ordinary meetings and discus-sions---social automata, scientific rules of order▼ Methods to measure and invent new cultures—64 culture dimensions, Manage-by-Events, 9 culture powers▼ Tools to see and manage emotional responses---re-sponse stratifications, response to response matrices▼ Tools that measure social power---social indexes, 64 social processes, 64 functions of all arts ▼ Comedy Tools---that make our work ecstatic fun!!! ▼ Dozens of Invention Tools---like extreme product extra-polation, dimensions of difference, solution cultures ▼ Beyond 6 Sigma Quality Tools---22 customer sat dimensions, 60 customer surprise dimensions, TRIZ fits

WHERE THIS IDEA CAME FROMFIRST there was MIT research on Structural Cognition, how genius applied ordinary mental operators to dozens of ideas at a time not 4 or 6. . SECOND there was research on knowledge and practice tipping point finding at the Univ. of Michigan and Santa Fe Institute in the mid-1980s. THIRD there was extensive research on Silicon Valley and Venture dynamics at Stanford and Xerox PARC decades before Harvard and MIT got onto it.FOURTH there was the demise of single general models of creativity – they were replaced by diverse plural creativity models each specifying different thought protocols for inventings—applied to law at U of Chicago. FIFTH there was work on the culture of devices, genders, business practices at Santa Fe Institute and Kwansei University in Japan, allied to law at Keio. SIXTH there was work on mapping all approaches to design at KEIO Univ. in Japan. SEVENTH biosense replacing mechanosense at Harvard & MIT's 6 new bio-dynamics departments. Biosense forms of law and technology are emerging.

PRIOR CLIENTS►P&G bought our question finding tools►White & Case bought our design thinking training►General Dynamics bought our fractal law interfacesBIO OF RICHARD TABOR GREENERichard started as an expert trial witness (software), moved to IP technology consults, then Predictive and Systems Law Design seminars for Newt Gingrich and the US Congress. Richard is a grad in artificial intelligence programming at MIT and creative writing at Wellesley, who visited the richest 2000 Americans raising funds for NGOs, got an MA in Chinese/Japanese business, an MA in web communities, a PHD in TQ research improvement at the Univ. of Michigan, presented at the founding seminars of the Santa Fe Institute, set up 9 new businesses for EDS, Coopers& Lybrand, and Xerox PARC (in the US), for Matsushita Electric & Sekisui Chemical (in Japan), for Thompson Electric and N. V. Philips (in Europe). He taught MBAs 5 years at the Univ. of Chicago, wrote 14 books (2 by McGraw Hill), and now teaches Design Creativity & Innovation at Japan's top private university KEIO University. Consults at P&G, White & Case, ING, Baker & McKenzie Anderson Mori & Tomotsune , & others. He guest lectures as visiting professor at several top 5 Law and Med schools.

WE GIVE YOU:new strategies new billable predictive legal servicesnew case types profit from new tech accelerationnew customers ordered maps of ideas & possibilities

YOU LEARN NEW TOOLS:INNOVATION 45 models SYSTEMICS 72 tools CREATION 60 models CULTURE 64 powersDESIGN 64 approaches KNOWLEDGE 64 dynamics

YOUR SECRET ADVANTAGE: Multiply productivity of traditional legal thought types by factors of 10 to 20 while mastering then inventing entirely new legal thought types. Out imagine out invent your rapidly evolving competition, systems, & clients.

OUR SECRET ADVANTAGE:The ASIAN Factor—our decades teaching & managing Chinese & Japanese mean we release Confucian and Buddhist images, methods, & powers via emerging new Western thought infrastructures!!! Improve the best of the West with best of the Rest!

LAW & JUSTICE DESIGNCreate, Invent, Discover, Venture, Design, Innovate

A K N O W L E D G E E P I T O M E B U S I N E S SYOU GIVE US:

fast global technical evolution inundated legislators, lawyers, & courtsemerging quickie alternate law systems

EXPONENTIAL EVOLUTIONin Technical Means

Means AN IMMENSELY EXPANDINGJUSTICE GAP

1) How new technologies create new issues/cases for old laws plus new laws2) How new technologies create new ways and forms of mediating, negotiating,

leading3) How new technologies are making ordinary issues and cases more global,

cultural, and multi-cultural4) How laws have a social and technical life that determine their meaning,

impact, and uses-to-which-they-are-put5) How to design laws that work, with 2nd order side-effects that work6) How to design laws that enact the social supports that obviate their use7) WHAT is technology evolution doing to:privacy ownership info leaks surveillance data-securitydefamation liability job-security bullying legal-discoveryintent standing tort damage-harm actionable-cause8) WHAT is replacing law---by the time laws are enacted the technical basis they

address has died and been replaced---we need something faster, less permanent, self-ending & pro-active---something DESIGNED

9) WHAT HAPPENS when we make laws before interest groups figure out how to abuse and misuse new technical platforms?

THE ANSWER is in Our 14 SEMINARS!!!

Contact: [email protected]

We MAKE LAW & LAWYERS, LEGISLATION & LEGISLATORS as Innovative as Silicon Valley via DESIGN THINKING

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ONE YEAR COURSES OF ONE WEEKEND PER MONTH, Each = 8 TOP UNIVERSITY COURSES by Richard Tabor Greene Via De Tao Masters Academy

QUALITY SKILLS FOR GLOBAL EXECUTIVES

TOOLS FOR HANDLING CULTURES OF ALL SORTS

BRAINPOWERS: TOOLS FOR EXERCISING 15 AREAS OF MINDS

YOUR DOOR TO CREATIVITY: 42 GENERAL 12 DETAILED MODELS

30 CREATIVITY & NOVELTY SCIENCES STUDIED TOGETHER

NON-LINEAR SYSTEM FORMS OF HIGH TECH LEADERSHIP

QUALITY OF: Thinking, Production,Sales, Presentation, Self, Career,Profit, Leadership, Globalization,InnovationTOOLS: social automata replacebrainstorms, fractal pages replaceprose text, stratified respondingreplaces discussions, rules of orderreplace meetings

CULTURES OF: Innovation,Design, Business, Nations, Lead-ers, Organizations, Devices,Users, Products, Business Prac-ticesTOOLS: 64 dimensions, 64social processes, find tippingpoints, transplant X across cul-tures, stratified responding

BRAIN AREAS: 50 Mind Types,Language-Art, Mind Extensions,Theory, Structure, Reflection,Happiness, Social Automaton,Evidence, Career, Culture, The-ories, and Design

MODELS: Traits, Question Find-ing, Darwinian Systems, Insight,Social Automaton, Culture Mix,Subcreations, 4 CyclesINPUTS: Creative CareerDynamics, Social Processes,Culture Dimensions, Workstyles

Creativity, Design, Innovation,Composing, Performing, Lead-ing, Educating, Parenting, Fash-ion, Exploration, Inventing, SelfExtension, and 18 more.

Tuning Interacting Populations,Social Plasmas and Idea Fusionsin Silicon Valleys, Flows andHomes of Lubricated Ideas-Funds, Steve Jobs Style DesignImposed on Engineers?MBAs,Anti-Cultures, Un-designingDesigns

WHICH OF THE BELOW DO YOU WANT NOW FOR YOU

Copyright 2004 by the author, All Rights Reserved, US Government Registered

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AWARE DESIGN ZENby Richard Tabor Greene

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YOUR DOOR TO CREATIVITY---2 DAY COURSE----AT BEIJING UNIVERSITY 19 & 20 OCTOBER 2013----Copyright 2013 by Richard Tabor Greene, All Rights Reserved, US Government Registered PAGE 42

meta creation

META-WORK META-COGNITION META-CREATION

In the 1980s the Japanese suddenly defeated 11 industries in the USAand EU by having higher quality, invading markets from below, andproviding superior service. Their anti-profession treatment of bodiesof knowledge, particularly QUALITY, defeated top university “profes-sionalization” of such bodies of knowledge.

The invested 15% of all work time in every workgroup improving HOWthey work (their processes). This “meta-work” results in world bestprocess performance in tens of thousands of processes in 11 indus-tries.

Meta-cognition research decades ago by Flavell and others showedthat people aware of and changing how they learned, learned betterand more.

Meta-creation is analogous--creators who study how they create, howothers create differently, end up more creative than others.

ACADEMIA IS OUR ENEMY. Academia’s mania for single right-y models,produces models what, when sincerely applied by highly competentprivate sector organizations, produce laughable results (Harvard’sAmabile at Procter & Gamble installed a Corporate New Ventures pro-gram that changed 42+ environment variables producing (she wrote)copying a hit Japanese product 8 years after it hit in Japan. Delayedcopying is not a robust form of creativity.

CEOs wonder--what is the chance that our one academic-consultant’smodel is the only right one? The answer is repertoires of manydiverse models OUTPERFORM any one narrow academic’s model, how-ever “right-y” it be in journals published.

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48 Genres of Systems Thinking

NEW SCIENCES

NEW SYSTEMS

NEW UNITIES

NEW MEDIA NEW SOCIALITY

NEW SELVES

NEW LEVELS

NEW KNOWING

EXCELLENCESCIENCESThese are 54 routes tothe top of nearly any fieldor profession.Tool: map routes to thetop, yours, others

SYSTEMEFFECTSThese are ways ourthoughts and actionsblind us to all theirconsequences andconditions. Tool: response toresponse matrix

DESIGNSCIENCEThis is identifying thepast contexts thatgenerated bad thingstoday and fixing them. Tool: dimensions ofdifference

NEW DATATYPESThese come fromglobal web media &phones. Tool: experiencereport forms

SOCIALNEUROSCIENCEThere are 40+ brainmodules with knowninteractions all messagesmust interact through. Tool: image-frame-name

DESIGNINGSELVESSelves, cultures, highperformances are thesame?accumulatedpractice of routines.Tool: self told self story,other told self story

PERSONAL P.R. Big organization liveshave too much & toolittle audience at onceTool: brand insertionevents

COMPILINGKNOWLEDGEACROSSFORMATSThe new comeshidden unexpressednot in reports. Tool: find convert theinchoate new

NOVELTY &CREATIVITYSCIENCESThese are 18 ways theutterly new comes intoour lives and world. Tool: map models ofcreating your, others

COMPLEXITYTHEORYThis is study of populationsof interacting agents that co-evolve into inventions andorder no one plans orintends. Tool: find tippingpoints

BUILDINGINTERFACES This is a morepowerful way toinfluence thanleadership. Tool:meetings wherepersons meet, ideasmeet, change appears

NEW MEDIAThese are interactive,every-one-a-com-poser-performeremer-gentsTool: informativevideo

SOCIALPHYSICSOur fast emotive mindsets contexts for our slowconceptual mind. Tool: subtract-add fastmind contexts of slowmind

GENDERESCAPESWe grow up blind tohalf the world and itsways. Tool: 28 braindifferences,magazines of othergender

TEAMEFFECTIVE-NESSYou set 13 conditionsto entice a team tosteer itself well. Tool: 13 factorsmodel; structuralremark making

STRUCTURINGKNOWLEDGEFOR USEApply usual mentaloperators to dozens ofideas at a time not 4 or6Tool: structuralcognition

REALITYSCIENCESThese are four ways we tellaccurate complete truths toour selves and others. Tool:you as the comedy, yoursacreds as the joke

QUALITYThese are 22 operatorsapplied to one body ofknowledge by Japan tomake it world powerful.Tool: totalize, globalize

FRACTALITYThis is turning eachpart of our selves andlives multi-scale asour world is Tool:multi-scale plans/images

ARTArt is how we manageour emotions andfeelings of others. Tool: functions of allarts

SOCIALREVOLUTIONLiberation from pastssometimes leads tofounding freedoms.Tool: manage bybalancing, creativitypowers

CULTUREPOWERSEach of culture's 9powers needs recognitionand handling Tool: highperformance, transplant Xacross matrices

ORGANIZA-TIONALLEARNINGDYNAMICSGroups use few of themany ways they canlearn to change. Tool: 64 ways

BREEDINGIDEASMaster 3 ways we inventthe new by blending whatis uncombined. Tool: ideatransplants, exaptations,bricolages

ERRORSCIENCESThese are 12 ways we skipto alternate easier realities athuge cost to self and othersTool: forcing yourself tochange= uses of error

EVOLUTIONThis is the 64 dynamicsof the most creativeprocess known, modifiedfor our use. Tool: social automata

EVENTSThese are faster morevisible ways to dowork. Tool: mass workshopdoing fast of slowprocesses; responsestratify

EMOTIONEducatedness starts ajourney to adulthoodat 20 and arrives asadult at 55 or so yearsold. Tool: skills ofemotional intelligence

MINDDILEMMASDeep in mindpolarities tempt us toharmful extremes. Tool: self and otherassessment andrebalancings

CAREERDESIGNGreat careers aredual?weekly doing whatwe qualify for andopposite. Tool: despairdoorway, be to have,peripheral participation

LEADERSHIPFUNCTIONDELIVERYSYSTEMSMany managers but littledirectedness delivered bythem all. Tool: needassessing, deliveryalternate means

KNOWLEDGEEVOLUTIONDYNAMICSIdeas follow knownpatterns in how theyevolve Tool: ideawaves, dialectics,fractal embeddings

PRESENCESCIENCESThese are how we staypresent, perform, &present to others.Tool: establishing yourvideo self

BIOSENSE--NEWSENSESThis is a change fromsteel as strong tobone?it grows wherestressed and fixes itsown breaks Tool: undo-mechanosense

CITY-FICATIONSThese are how weturn wild and newplaces into homes. Tool: spaces theory

MINDEXTENSIONSIt is tools outside usthat make us smart,not brains alonTool: educatingextensions not brainsonly

SOCIALINFLUENCESupersalesmen usethe mind's modulesto sell. Tool: un-do ads, see-use persuasiontriggers

THEORYREPERTOIRESPeople with moretheories live in abigger world. Tool: observationbefore after newtheory

DELIGHTINGCUSTOMERSATISFIERSPeople stop at 1satisfier, omitting21. Tool: 22 satisfactiondimensions

KNOWLEDGEFLOWS THRUMINDS &SOCIETIESComprehensivefractal regularizedidea maps. Tool: glassbead gaming

INFLUENCESCIENCESThese are ways webuild worlds andenvironments othersadapt themselves to. Tool: meeting someoneelse's need

CHANCE-ERROR-RISKTHEORYThis is why and howwe depart from needsharmfully. Tool:compensate for thoughtflaws

TECH CLUSTERS:REPLICATESILICON VALLEYThese are the socialfusions into socialplasmas that generateventures. Tool: flowsand homes

STORYRECURSIONWe are stories insidestories?tragedies orcomedies?Tool: characters asstories

SOCIALPROCESSESWe are alwaysshowing upimbalanced in oursocial aims andmeans. Tool: assess-correctimbalances

HAPPINESSDYNAMICSOur arrivalhappiness systemhas striving systeminside it = dramaTool: extremeconcentrations

NEW FORMSOFINTELLIGENCE Schools mis-educate, exposuremakes us smart notmemory. Tool: surface areanot ideas stored

KNOWLEDGEAPPLICATIONUsing fractal ideamaps to plot trends,possibilities, futureactions. Tool: need spotting,solution spotting

YOUR DOOR TO CREATIVITY---2 DAY COURSE----AT BEIJING UNIVERSITY 19 & 20 OCTOBER 2013----Copyright 2013 by Richard Tabor Greene, All Rights Reserved, US Government Registered PAGE 17EXAMPLE: DESIGN FOR BRAINSCAPES--GENDER

DISCRETE FOCUS VERSUS WHOLE BRAIN MOBILIZETALK VERUS LISTEN, RANK VERSUS CARE, DEFEAT VERSUS COLLABORATE

THE CHALLENGE--DESIGN A FEMININE ONE DAY HOTEL TECHNOLOGY CONFERENCE

1. 2000 EDS salesmen sold nothing to their biggest customer, General Motors, in 2 years.

New York Times and other media coverage of the bad feelings between EDS and GM,

making fun of EDS posturing and lack of results. A public relations disaster.

Survey of GM managers and EDS managers showed the cause was EDS’ overly male overly

militarist “take the hill” culture. What EDS presented with pride looked silly, tiny, and

pitiful to vastly better educated, more technically capable GM people. EDS was so busy

boasting, feeling insecure, how great they were, they did not notice GM people laughing

at them and their presentations.

2. The Solution (that actually made the first sale from EDS to GM by any ordinary man-

ager or employee not CEO): A Feminine High Technology Conference

3. WHAT is a feminine style technology conference?

a. Start with APOLOGY---we are FOOLS, we bragged but looked stupid,

b. Start with OPENNESS--we need to learn from YOU

c. NOT us presenting our greatness but us finding others great inside you GM who present

d. Not a printed summary of our great stuff but of your comments viewing vendor and

competitor firm demos of high tech applications

e. Not one focal technology for one focal part of GM but map of 40 parts of GM and 4

new technologies for each demo-ed

f. OFFER TO HELP not boast of greatness--if you judge us capable of helping, let us know

and we will do our best to assist you.

RESULT: The first sales--$17 million--by any part of EDS other than EDS’s CEO to GM. Done by a low ranked computer programmer, not a manager, not an executive. 3 meetings of 400 GM managers each, at hotels, one per month for 3 months.

THE CHALLENGE--A LAZY WAY TO BEAT COMPETITOR SOFTWARE APPS

1. After to apps we at Xerox PARC developed that were extremely high tech so US gov-

ernment secret agencies bought them before announced, my programmers were tired

and asked that our 3rd app be “successful in an effortless way”. So we brainstormed

what an “effortlessly superior app” would be like and how it could be designed.

2. Whole Brain not Focal--a Taguchi App that had tools to convince executives to do

Taguchi EARLY in product development processes not LATE.

3. Collaborate not Defeat--our app helped users set up each of our 6 competitor product

apps and helped users interpret results of each of our 6 competitor product apps

4. Care not Rank--our app minimized technical mathematic compexity in all areas,

appearing vastly easier than competitor apps, almost embarrassingly kid-ly simple.

5. Listen not Talk--our app used a flowchart of the steps of doing Taguchi analysis as its

interface, not menus--so users learned by re-use better the aims of Taguchi method.

RESULT--from start we sold 6 times all our six competitors--people who bought their all bought ours too.

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Culture Of Engineers1) math as hiding place for those uncomfort-able with emotion2) curriculum for handling ALL BUT power and money3) late career bitterness at own missing social power-skill 4) off the shelf sub-system shuffling rather than actual engineering or innovating

Culture Of MBAs1) self select psychopaths (use others without empathy)2) own progress- \wealth over all other values3) econ as hiding place for greeds & personal incapability4) innovation as product packaging only = fake innovation

Culture Of Design1) SPLIT communities: equations design vs. art design vs. social force design (laws/policies) vs. fashion design2) educated as wimps---low status, low salary, giving awards to each other till hired3) innovation eroded/thwarted by lack of power to inspire/influence clients (culture of bitching at conservativeness of clients)

Culture Of Users1) bubble male hormonal TOYS FOR BOYS markets2) no techs actually improve performance much3) few find WHAT new techs can actually be made to do4) substrate change substitutes for innovating

Culture OfTechnologies & Vendors

1) TOYS FOR BOYS hormonal bubble markets for useless innovations2) push more CONNECTEDNESS erodes difference & creativity (need pulsed systems)3) all use tiny fractions of functions “there”4) rhythm of delusions: mainframe now clouds, central cheap but loss of control (eternal return)5) tools for low level info, no tools for ordering, knowledge, contexting = CAN increases while DOES decreases = useless & un-used innovating

Culture OfCapitalism

1) fake variety2) sell dis-satisfaction = selling need where there is none3) innovation as excuse to sell un-needed products

Culture OfMonkey Hierarchy

1) internal tasks overshadow end-user service tasks2) copy what “works” till all are alike = org dies when environment shifts3) innovate for vertical not horizontal (client) impact (over time)

Culture Of Males

1) rank valued over service2) innovation draws top monkey attention3) innovate for promotion (mommy look at me I am important!)

Culture OfUSA

1) live in future so present very ugly2) aspergers Silicon Valley = home run change all innova-tions3) innovate without improvement of life quality the norm = useless innovating

Culture Of Tool Use1) invented as form for powerful aims, decays into grab-bag collections of irrelevant uses2) focus diffusion leads to weak impact leads to discard for newer tools 3) innovation as cover for failed use of prior inventions.

The Cultural Landscape of Innovating

Culture of Big Organizations1) too much too little audience at same time

2) becomes own world, loses client view3) fights to just do same or nothing

4) exaggerations of novelty as hassles erode trying5) force out all with great new = unfitting ideas

6) needed agreements prevent big changes7) flows & homes: nothing flows, nothing loved =

Hated mom of spin off ventures only

Home Run Innovations Handle THESE ELEVEN

YOUR DOOR TO CREATIVITY---2 DAY COURSE----AT BEIJING UNIVERSITY 19 & 20 OCTOBER 2013----Copyright 2013 by Richard Tabor Greene, All Rights Reserved, US Government Registered PAGE 6DEMOCRATIC RULES OF ORDER REPLACE EAST-WEST MEETINGS1. Each person lists one of more of the following on board:

2. Whole group reviews the list and puts similar items (ones that can be treated by applying same treatment to same topic) together.3. Whole group asks for and chooses WHAT treatment is best for each topic to get needed output. 4. Whole group appoints (or asks for volunteers) someone to lead applying the agreed on treatment to the topic to get the output.5. Whole group states TIME LIMIT for applying treatments to all topics. 6. Whole group puts item groups IN ORDER first to last, assigning last ones, if not gotten to in the meeting to pairs of people to do outside the meeting before the next meeting.7. Meeting beginsIN PRINCIPLE---all people in the meeting LEAD one or more item groups, that is, lead applying the group-designated treatment to the topic. IN PRINCIPLE--groups invent better and better treatments for han-dling certain topic types, as experience accumulates. Treatments include: secret voting, open debates, social design automata, everyone states one flaw in turn, everyone states one virtue in turn, pairs of people propose alternatives in turn then all discuss for X time and vote by open ballot, delphi process of circulating paper all add editings and critiques to in turn, etc.

out-come needed from this meeting

cus-tomer/user of that output

form/format of that output

topic that must be treated to pro-duce that output

treat-ment that must be applied to topic to pro-duce output

time needed to apply treat-ment to topic

sug-gested person to lead apply-ing that treat-ment to that topic

THE IDEA OF MONASTIC INNOVATION

THE JOB--as we know and use it was invented twice, at about the same time, 1000

years ago, in Spain by Benedictine monks and in Muromachi Japan at Rinzai Zen

monasteries. REPLACING THE JOB--most people cannot detect what is absolutely

harmful and stupid about the job as done by nearly all people alive today. Try it

yourself---list three utterly harmful and stupid aspects of the job as we all do

them.

Monastic Innovations are changes in interfaces and media so ancient and assumed

that no one at all changes them or improves them for thousands of years--like “the

job” was and is. The idea that one person for forty or so hours every week does

the same small set of tasks year after year (part of what “a job” has been and

remains) is utterly foolish to us all. There are hosts of jobs so dangerous, tiring,

using such narrow aspects of people doing them, that continuing them all week long

or worse all year long is foolish. People “burn out” due to such “jobs”. Further-

more, there are huge wonderful possibilities opened up when people combine “two

very diverse jobs” in each week, and even more when those “two” are in different

companies and industries. The Dutch are experimenting with this via a part-time

arrangement where people combine two part-time jobs each week for different

industries and firms.

Then there is the MISSING MICRO-ORGANIZATIONAL LEVEL phenomenon--depart-

ments, corporations, states, agencies are highly structured but all their work rests

on myriad mundane daily discussions, meetings, brainstorms, work processes, read-

ings and writings. Those six activities are largely free-form, unstructured, irregu-

lar, and unproductive. Most of society above them in scale is more structured than

they are. When we make each participant in a meeting, lead one treatment of

one topic in it, when we make discussions elicit noticings, feelings, recalled experi-

ences, interpretive frameworks, meanings in turn from participants so none inter-

pret before seeing inputs to interpretations of others, when we do work in intense

many-party events using expert protocols instead of a few desultory staff alone,

when we replace brainstorms with social design automata where people and ideas

do not merely watch each other but intensely build on each other and interact,

when we read not the points in text but the structuring of those points (layers of

context that give 90% of their meaning), when we write not bushy-irregular hard to

use paragraphs of text hiding number, ordering, and names of points, but instead

fractal multi-scale pages where names, ordering, and count of points is visible at a

glance--productivity of all our work and society leaps upward. Persons or small

groups switching to these new media, become “creators” via such productivity

leaps.

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10am

SOUTHERN CALIFORNIA CULTURE

1. laid back not intense

2. glorious weather always not rain/gloom

3. dream not reality industries

4. defense not private industries

5. face Asia not Europe

6. public coast not private

7. T shirt not blue suit

8. gold/frontier/fortune dreamers not immigrant boat land dreamers

DISNEY CULTURE1) escape 2) fantasies 3) system 4) fully 5) viewers 6) tightreality nicer than “experience” scripted not players controlnot face it realities = product roles no improv smiley lions

FRENCH CULTURE

1. strong hierarchy, elite do not mingle with workers = strong socialist unions, parties

2. top = abstract, mathematic, Descarte bottom =concrete,

3. family over work

4. design style over content= like Ugly movie stars

5. intellectual conversation over wealth

6. 2 hour lunches over wine

7. la gloire---yearning for specialness amid Europe, Concorde

8. glory through mind and style and life quality not monuments, buildings, wealth

2 Matrix Method Example

1. HELPS in 2. HINDERS in 3. HELPS in 4. HINDERS inOrigin Not in Origin Not in Target Not in Target Not inTarget Target Origin Origin

THE METHOD:Step One---for one culture which pole is it (example: hierarchy or equality)Step Two---state HOW and WHERE that pole shows up, is visible in culture oneStep Three---do the same as step one for second culture, select poleStep Four---do the same as step two for second culture, state how and where that pole chosen shows up, is visible in culture twoSTEP FIVE---state INTERACTIONRepeat 400 times.

THE EXAMPLE: hierarchy versus equality

HOW/WHERE DOES THIS SHOW UP IN CULTURE X STRONGLY/VISIBLY?

Example:

Southern California Culture

equality NOT hierarchy

---shows up in disobedience to and disrespect for bosses of all sorts---not take work too seriously = elan makes accomlishment not seriousness makes accomplishment

Disney Culture

hierarchy not equality

---shows up in micromanaging employees in Disney worlds—full scripting of moves, roles, jobs.

HOW DO THESE INTERACT---STRONGLY? WEAKLY?

Disney amazes with machine-like perfection of execution the rather unexact people of California.

Equals---Disney amazes egalitarians with display from hierarchy.

NOW REPEAT THIS 64 times 64 = 4000 TIMES

Categorize Strong Interaction Intersections Abovein One of the Four Categories Below

4000 Conversations about What Interacts with What to Result in What Overall.

by Richard Tabor Greene, Copyright 1993,All Rights Reserved, Registered with Library of Congress

YOUR DOOR TO CREATIVITY---2 DAY COURSE----AT BEIJING UNIVERSITY 19 & 20 OCTOBER 2013----Copyright 2013 by Richard Tabor Greene, All Rights Reserved, US Government Registered PAGE 4760 MODELS OF CREATIVITY

60 MODELS OF INNOVATION

60 MODELS OF DESIGN

FASHION

SOFTWARE

PRODUCT

GENRES

INTERFACE

EVENT

MEDIA

LAW &

GENRES

STORY &

SELF

CAREER

GENRES

GAME

CULTURE POLICY

CHARACTER

GENRES

Greene’sCreativity & Novelty

SciencesSOLUTIONS STUDIO

Greene’sCreativity & Novelty

SciencesSOLUTIONS STUDIO

120 Power & Quality TOOLS, 4 Creativity & Novelty SCIENCES, 12 Design Genre EXERCISESa TOOLS Course, Creativity & Novelty Sciences REPERTOIRE, 48 Weekly Design EXERCISES

60 MODELS OF

TECH VENTURES

MANAGINGQUALITY

120 TOOLS

MANAGINGQUALITY

120 TOOLS

Getting Into, Out of, Rid of: the World’s Top Ten UniversitiesNEW INTELLECTUAL, CAUSAL, SOCIAL, NEURO, SYSTEM, LANDSCAPES OF KNOWLEDGE & EDUCATIONStructural Cognition, Theory Power, Educating Mind Extensions, Monastic Innovations, Re-Do Plato/Confucius Empirically (RESULTS: 54 Excellence Sciences, 64 Capabilities of Educated People, 18 & 30 Creativity Sciences,

60 Models of Creativity, 25 Step Model of Insight, 54 Models of Innovation, 64 Natural Selection Dynamics), 25 Design Areas, LANDSCAPES OF DESIGN (Tipping Points, Natural Selection Operators,Biosense Levels, Replicating Silicon Valley, 54 Systems Genres, CULTURE [64 Cultures Dimensions, 192 Culture Dimensions, Culture Mix Creativity,Transplant X Across Cultures, The Femininity of Productivity, 11 Cultures of innovation], SOCIAL Landscapes [Social Processes, Org Types, Engagement Determinants, Creative Career Dynamics], How Universities Make Us Unsmart, Flaws in Thought,50 Mind Types, 5 Innovation Noises, Mass Event Delivery of Core Functions (replacing processes by small staffs).

MY CREATIVITY & NOVELTY SCIENCES SOLUTIONS STUDIO Email: [email protected] and [email protected]

Richard’s new bo

ok in CHINESE:

Your Door to Crea

tivity: 42 ModelsRichard’s 1 Year

1 Weekend Each Month COURSE

S

120 QUALITY SKILLS, 25 BRAINPOWE

RS, YOUR DOOR TO CREATIVITY,

30 CREATIVITY & NOVELTY SCIENCE

S

Brainstorm & Social Automata Ways

to design Social AutomataSTEPS INVOLVED EXAMPLE:

social web site parasite app designNOTES

A. LIST THE ESSENTIAL STEPS---CORE What do nearly all social web sites lack? Why?What unusual way to deliver that would puzzle the big guys now but please current users?What business model allows fast viral initial spread that evolve into serious money?What names, images, fun use aspects would excite media, users?Whose success/growth would you ride and how?How forestall/delay copying/substitution by biggies, established players?

B. LIST THE STEPS THAT ANY CREATIVE OUTCOME REQUIRES What UNITY, of function—that users immediately want and appreciate defines our product?What OBVIOUS parasite quality—makes users ALL WANT our product added to their facebook, linkedin, etc. app?What fundamental social function weak in all existing social apps can be boosted by a parasite app?

C. LIST THE STEPS THAT PEOPLE SHOULD BUT NEVER DO BECAUSE OF BLINDSPOTS, SELFISHNESSES, SOCIAL CROWD AND SIMILAR FLAWS

How are existing social web sites excessively male?How are existing social web sites fooling users and themselves (people share junk not worth, people connect to junk not worth, people input but read no one else's inputs)?How do criminal conspiracies like GroupOn demonstrate the monkey stupidity of financial markets and their so-called “bright” people?

D. COMPARING THE 3 ABOVE LISTS, MAKE SOME STRATEGIC/AUTOMATA-TOPOLOGY CHOICES:1. competition is key or cooperation2. diversity is key of perfecting is key3. all steps can be made interesting or some steps will necessarily bore people doing them4. inputs are key or later editing of partial results is key5. support and further development is key or challenge and alternatives are key6. few well developed outputs are key or many diverse quick-and-dirty ones are key7. consulting outside resources in some steps is key or ideas already inside heads is enough

1. cooperation key2. diversity NOT key3. some steps require fleshing out details = boring4. inputs up front are key---right initial base key to morale5. further development is key not alternatives6. well developed few outputs are key7. no outside expertise or knowledge needed, everyone already has social web site involvement probably

E. CHOOSE AN AUTOMATON ARCHITECTURE FROM YOUR ANSWERS TO THE ABOVE:1. one strand or several in parallel strands of people2. one person per step or one pair/trio per step3. fast short steps or longer thoughtful steps4. keep all assigned roles roughly equal in difficulty, interest, importance, or make them differ greatly along these dimensions5. keep people in their roles or at times switch who does what role by reversing step flows, or moving people6. one set of steps for the entire time or switch to new protocols of steps after a certain number of iterations

1. one strand2 one person per step (or even two separated steps per person)3. medium---not short not long---force wise decisions4. reverse the role assignments from a front of vital initials to a back of quality details so all get the big picture experience AND the details working out experience5. reverse as above6. could induce an entirely different protocol of steps 2/3s of the way through our time to explore what main protocol seems to be slighting or omitting.

F. CONSIDER FEATURES OF THE PEOPLE AVAILABLE TO DO ROLES:1. good generators versus good editors2. marginal fringers versus main markets3. loose and free versus tight perfection4. careful versus sloppy5. needs motivation versus has it already6. obvious traits of each person—nationality differences, gender, profession, etc.

1. several brilliant generators available2. more fringers than main market persons available3. engineers do both---details and big pictures4. great care NOT needed with this final output---a social webparasite add on app design5. motivation in unlikely to be a problem given currency of the topic/product6. much diversity NOT needed

G. DEFINE YOUR SOCIAL AUTOMATON:1. TOPOLOGY topology of people in flows (the flows may or may not interact)2. ROLES (role sequence in flows) roles assigned to each person in each flow position3. INPUTS/OUTPUTS clear inputs to each role/person, clear outputs (= someone's inputs) from each4. MATCHES role needs matched by people traits5. INTERVALS right time for iterations—not too short that hard tasks cannot be done well, not so long that easy tasks bore people

1. simple tree array—2 sequence steps, 3 in parallel steps, 1 final wrap up/edit step 2. 3 people do a sequence of 3 steps, then same 3 people do different operations on the results of step 3, then 1 of them combines those 3 last results into one result3. Role One does C above, Role Two does B above, Role Three does A above; Role Four lists main function and four component functions of the app, Role Five describes the valueit adds exactly how to users of all main existing social web sites, Role Six creates a brief ad for the product capturing its unity and parasite value, Role Seven combine all that into a TABLE DEFINITION of the Nature and Benefits of the Product, Including its NAME

H. EVALUATE AND EDIT YOUR DESIGN (off line ahead of time, during execution)1. are the inputs TO each role good enough that the person can execute well their assigned task? Are the outputs FROM each role good enough that the next person can execute well?2. it the iteration time interval too short (some roles forced into poor quality work) ortoo long (some people idle and bored)?3. are the final outputs accumulating from your automaton different from each other in valuable ways?4. are people getting better at their roles as iterations increase and is that improving final outputs?5. are people good enough from lots of iterations they are ready for an assignment change? Are the outputs thus far accumulated, good enough and similar enough, that mid-process change might produce better more diverse final outputs?

The first 3 Roles produce---1. Our New Product does X statements by means of Y statements, where X meets the assigned criteria of each role.2. One minute will be too short at first but okay after 5 iterations or so.3. What is tough is ITERATING---you imagine your first product as if THAT is BEST and ONLY, then in a minute you have to imagine an entirely other parasite that works well4. The flaws of existing social web apps are huge and unmapped so the social automaton should gradually discover how MUCH is missing now and relate that to bias---young maletechies pretending to sociality

I. DESIGN AN AUTOMATON TO DESIGN YOUR SOCIAL AUTOMATON1. if you have experience, you have a list of default Automaton Definitions you have used already to choose one from2. if you are new, doing this automaton design first of a process for designing your automaton is practice for your real designing-by-automaton execution

Later.

Copyright 2011 by Richard Tabor Greene, All Rights Reserved, US registered

Day 37 to 10

Day 210 to 6

Day 17 to 10

WorkshopProcedures

EditPanels

Pre-workAnalysis

Phone Research

Experience Report

Questionnaire

Experiment

Interview Research

Model Building

Structural Reading

Fractal Model

Causal Star

Structural Equation

Data Mining

Data Request

Research

Research

Research

Building

Building

Modeling

SpecifyingField Research:Institutions

Field Research:Persons

Field Research:Events

Field Research:Regulators

Field Research:Academia

Field Research:Media

Creation Team:Variation

Creation Team:Combination

Creation Team:Selection

Creation Team:Reproduction

Creation Team:Question Finding

Creation Team:Insight Process

Liberation Fight

Freedom

Historic Dream

Novelty Conserving

Novelty

Institution Renewal

Emergence

Institutionalizing

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din

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ask

Fo

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Fu

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Cel

ebri

ty E

nd

ors

emen

t T

ask

Fo

rce

Co

ali

tio

n B

uil

din

g T

ask

Fo

rce

Ad

ver

tisi

ng

Des

ign

Task

Fo

rce

Punctuating Eventlet Teams: Wandering Minstrels Singing Emotions of Successful Change, Hidden Impracticality Exaggeration Joke Clowns

Find errorsFind error causes

Find possible problems Find root problems

Find possible causesFind root causes

Find possible solutions Find root solutions

Find implementariesFind root

Findimpacts Find root impacts

implementaries

Scan new technology

Scan new regulation

Scan new competition

Scan new customers

Scan new products

Scan new partners

Model trends

Model styles

Model suffering

Model policy change

Model errors

Model growth

Determinants ofcustomer sat

Determinants ofsupplier sat

Determinants ofpartner sat

Determinants ofleadership sat

Determinants ofgenba sat

Determinants ofdestiny set

Ou

r E

con

om

ic V

isio

n T

ask

Fo

rce

Ou

r P

oli

tica

l V

isio

n T

ask

Fo

rce

Ou

r C

ult

ura

l V

isio

n T

ask

Fo

rce

Ou

r S

oci

al

Ch

an

ge

Vis

ion

Ta

sk F

orc

e

Ou

r O

ver

all

Mis

sio

n V

isio

n T

ask

Forc

ePunctuating Eventlet Teams: Wandering Global Change News (Song delivery), Hidden Audience Routine Answer Mocking Clowns (joke delivery)

practicality

inspiring

affordability

impact power

unmatchability

understandability logicality

good sequence

powerful images

structural coherence

not boring

beyond expectations

customer appeal

partner appeal

genba appeal

leader appeal

regulator appeal

techno appeal

non-blocking

non-competing

synergies

non-parasitism

common focus

sequentiality

economic sense

political sense

cultural sense

change sense

vision sense

application sense

Sample Research Assembly 1 Day:30 Workshop Groups, 15 Taskforces, 6 Punctuating Eventlets, 155 Person Minimum, 8250 pages of printed results

Co

mp

etit

or

Th

rea

ts T

ask

Fo

rce

Gen

era

l E

con

om

y T

hre

ats

Ta

sk F

orc

e

Ow

n N

euro

ses

Th

rea

ts T

ask

Fo

rce

Na

tio

na

l C

ult

ure

Th

rea

ts T

ask

Fo

rce

Org

aniz

atio

nal M

isal

ignm

ent

Thr

eats

Tas

k F

orcePunctuating Eventlet Teams: Dancing Drink Delivery Girls, Random Audience Members Chosen for Spontaneous Song-lets on the Theme

Copyright August 2003 by Richard Tabor Greene, All Rights Reserved, US Government Registered Day 410 to 12Tuning

.

Globalizations of the Globalization Concept: Handling Complexity by Globalizing Bodies of Knowledge

THE RESULT: A MODEL APPLY-ABLE TO ANY BODY OF KNOWLEDGE TO HANDLE COMPLEXITY

Globalization Type Abstract Function,for Application to Any Body of Knowledge

Way It Handles Complexity

Est

ab

lish

Ma

na

gem

ent

Fu

nd

am

enta

ls

1. movement of move-ments

• actor scale expansion• volunteers elicited per topic• standardization of tools across hugeÅ@scope

expansion of scale of doer to match scale of problem/opportunity

2. stages of belief • time scale of commitment expansion expansion of time scale of doer to match scale of problem/opportunity

3. war on variation • management invented via cause focus recurrence of problems type complexity eliminated by addressing causes

4. customerization • opening to external environment• career caused narcissism weakened

exterior environment addressed rather than left unseen disrupting chaotically plans and career ambitions

Est

ab

lish

Co

st-B

enef

its 5. interiorize criteria • frontier for future exterior understanding created scale of ambition expanded

6. self fund • side-effect benefits financially counted & budgeted side-effect benefits quantified

7. human scale • re-centering around human not system scale ergonomic reduction of system complexity of operation

8. optimize • systems optimized not single roles, functions, persons inter-actions inter-relations optimized reducing chaos from non-combining individual excel-lences

Ma

ke

Ess

enti

als

Vis

ible

9. clear waste • self control boundaries and disciplines established self management to reduce chaos of own self established as basis for later system manage-ment

10. science method • data replaces opinion based decision• public space created for word and deed sharing of genba (work-

force)

complexity from impression-based deciding replaced by valid data basis;complexity of frustrated need to perform before others reduced by regular opportunity to perform

11. remove hiding places

• inventories removed as hiding places for process slop hidden complexity exposed by removal of traditional hiding places

12. engineer processes • find inter-process links, dependencies, enablements• distinguish work from enabling processes from enablement means

change processes• automate process deployment

complexity of actual inter-process architecture laid bare and reduced

Org

an

iza

tio

na

l

Incl

usi

on

13. social leveling • particularize tactics for different social levels complexity of inconsistency among social levels reduced by tailored approaches for each level

14. dissolve borders • quantify and remove relictual structure blocks for flows complex of structural excuses and traditions for poor process flows laid bare and removed

15. market introjection • bring market dynamics to interior structure components and their relations

use overt measurable complexity of market bidding systems to do things rather than endure hidden complexity from bureaucracies

16. horizontal manage-ment

• focus careers on impacting exterior customers not bosses adjust flow of human ambitions to match systems newly redesigned for horizontal impacts

Tra

nsp

are

ncy

17. diversity aligned • get all internal units pointing in common direction reduce variation of direction among org units

18. basic functions installed

• fractally implement most fundamental management functions establish uniformity (non-complexity) by level of implementation of fundamental manage-ment functions

19. invisibles made vis-ible

• measure latent, interior, emotive components of impact get systems to embrace emotive, subtle, nascent signals easily missed by formal, overt, ratio-nal systems--reduce complexity from missed non-rational dimensions operating unseen

20. transparency to voices

• omni-directional undistorted transmission of requirements reduce complexity of inconsistent transmission of messages in upward, downward, leftward, and rightward directions

Ca

pa

bil

ity

Inv

ent

21. diversity leveraging • pass diverse parties thru deployed processes, expand scope of doer using complexity there in diverse parts of organization as input thru uniformly distributed processes and events to produce creative outcomes

22. invent new compe-tencies

• systematic development of capabilities shared across the organiza-tion

match customer requirements laid bare with development of new capabilities to reduce imbalance between new demands and ability to supply

23. cognitive depth • mental productivity expanded to scale of problems faced using complexity there in diverse parts of human cognition via deploying uniform mental processes across varied workforce

24. new basic unit of competing

• scale of unit that competes expanded to fit scale of problems faced expand scale of doer to match scale of problem

Kn

ow

led

ge

Dep

loy

men

t

25. re-engineering • means by which functions are done updated in synch with capabil-ities the world develops

complexity of newly developed capabilities in the world input into company systems sys-tematically as new substrates

26. liquefaction • distributing functions to faster, newer, more temporary means of delivery

reconfigurability of workforce increased so complexity of doer configuration matches com-plexity of form change of problems faced

27. learning leverage • quality of acquisition and use of knowledge measured and improved continuously

scale of learning increased to match scale of problems faced

28. organization as the-ory

• everything set up and managed as experimental form not fixed right answer

commitments latent in form and function shortened and lightened so reconfigurability (com-plexity) of doer matches complexity of problems faced

Div

ersi

fica

tio

n

29. diversify diversity • mapping of types of diversity there• measure how well each type is seen and used

precise accounting for amounts and types and degrees of diversity there to be leveraged, to fully leverage complexity latent in own diversities

30. alternative delivery for management functions

• social class fixed inventory of “managers” replaced as default means of delivering managing functions

• exact spec of what are functions of managing• exact measures of when and where what amount of what such

function is needed

excess waste complexity of unneeded managing reduced and excess complexity from phe-nomena being unmanaged that need management reduced by precise accounting for amounts, types, and delivery means effectiveness for management functions

31. software framing • social and computer software linked as what is delivered• software specified via process fault root causes not “wanted” func-

tions

new technologies forced to contribute in entire existing context instead of adding to com-plexity by becoming parallel universe of “special” rules

32. movement global-ization

• all above tactics applied to other forms of quality--quality of earth, quality of conflict, etc.

entire tactical system reproduced to produce highly similar aims in very different contexts--vast expansion of tool set to match huge problem situation scope

New

Co

mm

on

sen

se

33. non-linear manage-ment

• linear statistics technique replaced by complexity theory technique expansion of tool set basis to match scope of problems faced

34. creativity competi-tiveness

• creativity dynamics studied and improved expansion of complexity you can invent and leverage to challenge complexity thrown at you by situations

35. biologic manage-ment

• new commonsense ventures established entire context and framework of operation at all functions and levels switched radically to express new tool set basis above--expansion of doer complexity in capability to match situa-tion complexity

36. social & technical function virtualiza-tion

• models evolved into games into simulations into distributed net groupware

• social forms of virtuality as well as internet forms and virtual workspace forms

• computational sociality and software socialities

learning aids gradually evolved into becoming major tools of doing actual work--reducing complexity introduced by spec-ing and implement-ing new systems by growing aids into principals

MANAGING QUALITY : 120 Quality Skills that Move You from Middle to Executive, from Regional to Global Management120 Quality Skills that Move You from Middle to Executive, from Regional to Global Management120 Quality Skills that Move You from Middle to Executive, from Regional to Global Management120 Quality Skills that Move You from Middle to Executive, from Regional to Global Management 120 Keys to CAREER POWER Copyright 2010 by Richard Tabor Greene, All Rights Reserved, Registered with US Government; sponsorted by De Tao Masters Academy, at SIVA1st of 12 Months-Weekends, First 10 tools

Detect and track customer requirements

Detect and track capability developments

Preserve thru operations what was detected

Build new process capability

Influence forces evolving customer requirements

Influence forces evolving process capability

SalesServiceSatis-faction

Manage-ment

DesignProduct

Deve-lop-

ment

Tech-nology

Deve-lop-

ment

Process&

EventEngi-

neer-ing

Customer Contact & LogisticsSupply Chain Capability Deployment

Alli

ance

asse

mbl

ypr

oces

s

Team

self

man

agem

ents

uppo

rtpr

oces

sV

isua

lsel

fman

agem

enti

nfo

syst

empr

oces

s

Wor

forc

eSW

AT

asse

mbl

ypr

oces

s

Org

aniz

atio

nfo

rmca

lcul

atio

npr

oces

s

Function

Deploy-ment

Process

The Shared Core Functional Architecture of Re-engineering

Series

Figure 4

Managing Complexity 391 Copyright 2006 by Richard Tabor Greene, All Rights Reserved, US Government Registered

Recent Syntheses of All Total Quality Development

Ray Gehani at Rochester Institute of Technology (Gehani, 1993) a few years ago published an article presenting 9 frontiers of quality. I have corrected that model by adding a key ele-ment and rewording an erroneous element. Figure 2.4 summarizes Dr. Gehani’s model with two modifications that I made.

The figure shows a series of American quality gurus who incrementally expanded quality from a function among many to a function performed by entire managements and workforces--Deming, Juran, Feigenbaum--primarily. Subsequent gurus expanded the approach, technique, and toolkits applied to this expanded role for quality, now called total quality. From thebeginning both managerial system prescriptions and quality technique prescriptions were suggested. So total quality has within it organizational design and process components,regardless of which guru you listen to. Schonberger (Schonberger, 1993) has presented a 19 item list of total quality principles; but I covered most of those items in my previous exam-ination of Dr. Deming’s 14 points. Several general trends can be seen in close examination of the list. First, total quality expanded quality from one function among many to a functionall perform; second total quality expanded quality from specs and standards to what the customer says quality is; third total quality expanded quality from techniques specialists appliedto techniques whole workforces applied; fourth total quality expanded quality from treatment techniques to prevention techniques; fifth total quality expanded quality from concentrat-ing on downstream production issues to upstream design and customer understanding issues; sixth total quality expanded quality from dealing with wants customers can articulate todealing with wants customers cannot articulate. This is a good summary of overall quality technique evolution, however, it is not a consensed on model, and that is what is needed, acommonly agreed on model showing the phases in total quality’s evolution and the stair steps to more sophisticated methods where and when they occurred.

Figure: 2.4

10 Frontiers in Total Quality

Frederick W. Taylor and

inspected quality

scientific study by engineers of people’s tasks

subdividing jobs into small simple tasks

decide on scientific grounds one best way to do each task

standardize how task in done thoughtout the workforce

Deming and process con-

trol quality

bring processes under statistical control

distinguish common (system) causes of variation from special ones

acknowledge that 85% of variation is not worker’s fault but managers’

redo management system to get manager to improve the system and its processes

Juran and company-wide

quality

quality planning, quality control, and quality improvment as trio

managing quality, getting management as a whole to continually improve quality

manage quality the way finance was managed company-wide

Feigenbaum and total

quality

originated concept of total quality control in 1951 at MIT grad student

got quality out of the hands of specialists and as every manager and worker’s role

integrate quality activity across the 8 stages of the value added chain of production

the customer determines what is quality and what is not, not some spec or standard

Ishikawa and prevention

quality

changed emphasis from process control to defect prevention in Japan

used fishbone and pareto diagrams with workforces to examine causes of quality prob-

lems

created concept of internal customer as well as external customer--my next process

Taguchi and design qual-

ity

optimize ideal flow of energy through the system not flaws the customer sees

optimize the ratio of intended performance over variations in the envionment of use

optimize along linear function range of values not around point values

adjust previous designs using tuning variables for use in later designs in short cycle time

optimiae multiple dimensions simultaneously using orthogonal arrays

Crosby and cost of quality show financial impact of poor quality by tallying the cost of quality

cost of conformance, cost of non-conformance, cost of lost opportunity

quality work is not net cost but produces greater profits from investment in quality

improvement due to side-effect learnings

Imai and improvement

quality

two forms of management--maintenance of improvement, generation of improvements

all jobs split into two jobs--do the work; study and continuously improve the work

evolve from modest improvement goals to stretch goals using policy management

Kearns and mandated

quality

several rounds of benchmarking plus top down training cascade to launch quality

employee involvement through training, recognition, solving teams

processes for defining customer needs and process improvements deployed

Kano and delight quality distinguis basic (inarticulate) expected (articulate) and delight (inarticulate) quality

challenged Japanese industry to develop techniques for delight quality

kansei engineering developed by Mazda and Honda to detect inarticulate customer wants

ask what 5 times in addition to asking why and how 5 times

CHANGE THE WHOLE ORGANIZATION

1. By vigorous training and self improvement

2. By getting everyone in the organization to work together to change the organization

3. By creating constancy of purpose to continuously improve quality

4. By getting executives to lead and stay involved.

CHANGE TO A NEW WAY OF WORK

5. By de-buffering work processes

6. By researching work processes using the scientific method

7. By deploying elite and executive functions to entire workforce

8. By fostering self-managing processes

9. By making the organization transparent to changes in customer wants

10. By fostering continuous improvement of organizational learning capabilities

11. By operating cross-functionally using teams

12. By listening to customers and being driven by them

13. By vertical alignment between organizational levels

14. By horizontal alignment across functions around particular customer wants

15. By continuously reducing variation in processes

16. By treating next processes as customers

17. By removing constraints instead of optimizing within them

18. By making suppliers and customer partners in improving cross-organization processes

19. By preventing defects, instead of inspecting for them

20. By stopping suboptimizing within functions and between organizations

(ex. choosing suppliers on price alone)

CHANGE TO A NEW WAY TO TREAT PEOPLE

21. Justify initiatives by their cognitive profits not just their immediate financial ones

22. Catchball initiatives with the workforce instead of imposing them

23. Create circles as space of appearance of excellence among employees

24. Intensify the rationality with which work is done

25. Ratchet up regularly cognitive skill platforms shared by the entire workforce instead of

depending on the mental powers of elites

26. De-buffer all acts of training from all acts of application

27. Stop managing by quotas, numbers, and objectives ( equals fear, “drive fear out of the workplace”),

instead improve processes scientifically

28. Manage by facts instead of by slogans, authority, opinion, experience, or guts.

CHANGE TO RECOGNIZE THE NEW NATURE OF WORK, QUALITY, AND ORGANIZATIONS

29. Work is statistical

30. Work is processual

31. Work is cross-function and cross-organization

32. Do quality and quantity will handle itself

33. Quality is free

34. Quality is what the customer says it is

35. Organizations are statistical

36. Product(service) lines are statistical experiments

37. Research markets by releasing products/services rapidly not by market analysis.

TRY TO ACTUALIZE THE LINE CENTERED ORGANIZATION IDEAL

38. Deploy functions from outside the line to the line

39. Deploy rewards from outside the line to the line

40. Deploy initiatives and plans from outside the line to the line

41. Deploy recognition and perks from outside the line to the line

42. Deploy specialized knowledge from outside the line to the line

43. Deploy information and reporting from outside the line to the line

44. Deploy authority from outside the line to the line.

Model of Total Quality ComponentsFigure 2.5

Detect, Influence,

CustomerRequirements

Detect, Influence,

ProcessCapabilities

Listen to customers

Preserve what was heard

Survey capabilities

Build new capabilities

THESIXCOREFUNCTIONSOFTOTALQUALITY

Influence customer

Influence forces changing

requirement determiners

process capabilities

& Implement

& Implement

The Essence of Business:

4 things?

6 things?

19 things? If so, it ignores VOICE of the PROCESS

THE QUALITY WITH WHICH YOUACHIEVE HIGH QUALITY

REPEATING THE QUALITY JOURNEYOF OTHERS BUT WITH HIGHER QUALITY

DOING OF QUALITY TOOLS & STEPS

Does your company tell you when andhow long to pee?


T

Page 1; Copyright 2004 by Richard Tabor Greene, All Rights Reserved, US Government Registered

.

Globalizations of the Globalization Concept: Handling Complexity by Globalizing Bodies of Knowledge

THE RESULT: A MODEL APPLY-ABLE TO ANY BODY OF KNOWLEDGE TO HANDLE COMPLEXITY

Globalization Type Abstract Function,

for Application to Any Body of Knowledge

Way It Handles Complexity

Est

ab

lish

Man

ag

emen

t

Fu

nd

am

enta

ls

1. movement of move-ments

• actor scale expansion• volunteers elicited per topic• standardization of tools across hugeÅ@scope

expansion of scale of doer to match scale of problem/opportunity

2. stages of belief • time scale of commitment expansion expansion of time scale of doer to match scale of problem/opportunity

3. war on variation • management invented via cause focus recurrence of problems type complexity eliminated by addressing causes

4. customerization • opening to external environment• career caused narcissism weakened

exterior environment addressed rather than left unseen disrupting chaotically plans and

career ambitions

Est

ab

lish

Co

st-B

enef

its 5. interiorize criteria • frontier for future exterior understanding created scale of ambition expanded

6. self fund • side-effect benefits financially counted & budgeted side-effect benefits quantified

7. human scale • re-centering around human not system scale ergonomic reduction of system complexity of operation

8. optimize • systems optimized not single roles, functions, persons inter-actions inter-relations optimized reducing chaos from non-combining individual excel-

lences

Ma

ke

Ess

enti

als

Vis

ible

9. clear waste • self control boundaries and disciplines established self management to reduce chaos of own self established as basis for later system manage-

ment

10. science method • data replaces opinion based decision• public space created for word and deed sharing of genba (work-

force)

complexity from impression-based deciding replaced by valid data basis;

complexity of frustrated need to perform before others reduced by regular opportunity to

perform

11. remove hiding places

• inventories removed as hiding places for process slop hidden complexity exposed by removal of traditional hiding places

12. engineer processes • find inter-process links, dependencies, enablements• distinguish work from enabling processes from enablement means

change processes• automate process deployment

complexity of actual inter-process architecture laid bare and reduced

Org

an

iza

tio

nal

Incl

usi

on

13. social leveling • particularize tactics for different social levels complexity of inconsistency among social levels reduced by tailored approaches for each

level

14. dissolve borders • quantify and remove relictual structure blocks for flows complex of structural excuses and traditions for poor process flows laid bare and removed

15. market introjection • bring market dynamics to interior structure components and their relations

use overt measurable complexity of market bidding systems to do things rather than endure

hidden complexity from bureaucracies

16. horizontal manage-ment

• focus careers on impacting exterior customers not bosses adjust flow of human ambitions to match systems newly redesigned for horizontal impacts

Tra

nsp

are

ncy

17. diversity aligned • get all internal units pointing in common direction reduce variation of direction among org units

18. basic functions installed

• fractally implement most fundamental management functions establish uniformity (non-complexity) by level of implementation of fundamental manage-

ment functions

19. invisibles made vis-ible

• measure latent, interior, emotive components of impact get systems to embrace emotive, subtle, nascent signals easily missed by formal, overt, ratio-

nal systems--reduce complexity from missed non-rational dimensions operating unseen

20. transparency to voices

• omni-directional undistorted transmission of requirements reduce complexity of inconsistent transmission of messages in upward, downward, leftward,

and rightward directions

Ca

pa

bil

ity

Inven

t

21. diversity leveraging • pass diverse parties thru deployed processes, expand scope of doer using complexity there in diverse parts of organization as input thru uniformly distributed

processes and events to produce creative outcomes

22. invent new compe-tencies

• systematic development of capabilities shared across the organiza-tion

match customer requirements laid bare with development of new capabilities to reduce

imbalance between new demands and ability to supply

23. cognitive depth • mental productivity expanded to scale of problems faced using complexity there in diverse parts of human cognition via deploying uniform mental

processes across varied workforce

24. new basic unit of competing

• scale of unit that competes expanded to fit scale of problems faced expand scale of doer to match scale of problem

Kn

ow

led

ge

Dep

loy

men

t

25. re-engineering • means by which functions are done updated in synch with capabil-ities the world develops

complexity of newly developed capabilities in the world input into company systems sys-

tematically as new substrates

26. liquefaction • distributing functions to faster, newer, more temporary means of delivery

reconfigurability of workforce increased so complexity of doer configuration matches com-

plexity of form change of problems faced

27. learning leverage • quality of acquisition and use of knowledge measured and improved continuously

scale of learning increased to match scale of problems faced

28. organization as the-ory

• everything set up and managed as experimental form not fixed right answer

commitments latent in form and function shortened and lightened so reconfigurability (com-

plexity) of doer matches complexity of problems faced

Div

ersi

fica

tion

29. diversify diversity • mapping of types of diversity there• measure how well each type is seen and used

precise accounting for amounts and types and degrees of diversity there to be leveraged, to

fully leverage complexity latent in own diversities

30. alternative delivery for management functions

• social class fixed inventory of “managers” replaced as default means of delivering managing functions

• exact spec of what are functions of managing• exact measures of when and where what amount of what such

function is needed

excess waste complexity of unneeded managing reduced and excess complexity from phe-

nomena being unmanaged that need management reduced by precise accounting for

amounts, types, and delivery means effectiveness for management functions

31. software framing • social and computer software linked as what is delivered• software specified via process fault root causes not “wanted” func-

tions

new technologies forced to contribute in entire existing context instead of adding to com-

plexity by becoming parallel universe of “special” rules

32. movement global-ization

• all above tactics applied to other forms of quality--quality of earth, quality of conflict, etc.

entire tactical system reproduced to produce highly similar aims in very different contexts--

vast expansion of tool set to match huge problem situation scope

New

Co

mm

on

sen

se

33. non-linear manage-ment

• linear statistics technique replaced by complexity theory technique expansion of tool set basis to match scope of problems faced

34. creativity competi-tiveness

• creativity dynamics studied and improved expansion of complexity you can invent and leverage to challenge complexity thrown at you

by situations

35. biologic manage-ment

• new commonsense ventures established entire context and framework of operation at all functions and levels switched radically to

express new tool set basis above--expansion of doer complexity in capability to match situa-

tion complexity

36. social & technical function virtualiza-tion

• models evolved into games into simulations into distributed net groupware

• social forms of virtuality as well as internet forms and virtual workspace forms

• computational sociality and software socialities

learning aids gradually evolved into becoming major tools of doing actual work--reducing

complexity introduced by spec-ing and implement-ing new systems by growing aids into

principals

Managing Complexity 422 Copyright 2006 by Richard Tabor Greene, All Rights Reserved, US Government Registered

Tool 27:Quality Genres

The goal was totalizing quality, getting entire managements and workforces to do it, to take responsibililty for it, instead of leaving it to one small profession, the quality assurance pro-fessionals. It has taken decades to totalize quality somewhat, more in Japan, less in the US, even less in Europe. In government, zero in Japan and a lot more in the US, with little inEurope is found. As one effort to thusly totalize quality was attempted or somewhat completed, new vistas opened, often because lackings in that kind of totalization just attempted hadbecome visible. The quality genres in the model below are a sequence of corrections as organizations worldwide tried to get entire managements and workforces responsible for quality.

Instructions for Use. Quality is not the only body of knowledge that might be better applied when “totalized” or when “de-professionalized”. What other such bodies of knowledgemight better perform when subjected to the same or a similar sequence of genre moves as those in the model of quality genres below? I ask clients and students to pick some other bodyof knowledge and explain exactly what performance improvement would result from applying each genre treatment in the model below to it instead of to quality.

Genre Function Icon Appro-

achesGuru Philosophy Methods Vehicle

Components of Total Quality By Chronological Genre

QualityasProfession

Down-streamQuality

Top Down

SolvingQuality

Bottom UpQuality

Horizontal

UpstreamQuality

Inspect QualityAssur-ance

F. W.Taylor

Specialize jobsSimplify jobsScientifically..study jobsStandardize jobsControl to execu-...tue standards

Outsider studies jobTime motion studyJob specificationsSupervisors inspect...complianceReward compliance

Industrial engineer...sectionSupervisors trained...as inspectorsHumans hired/...trained in as...replaceable parts

De-buffer(Visualselfmanage-ment)

JITPokeyokeTPM

ShingoExpose process...flaws by remo-...ving inventoryFoolproof toolingMake system/...equipment state...visible at a...glance

People clean their...own workspaceFast set up times...for equipmentShip only what cur-...rent customers...wantDaily preventative...maintenance

Mandated inven-...tory reducationsWorkforce solving...of exposed flawsOperators become...own maintenanceNo effort toolingPlace for everything

Control SPCCost of...QualityQuality...audits

DemingJuranFeigen-...baumCrosbyKearns

Quality as top...down mandateMassive trainingSwitch supervi-...sors to enablersMake managers...responsible for...quality not just...workers

Control chartsDesigned experi-...ments7 Statistical toolsMake processes:...measured, in con-...trol, centered, then...reduce variation

Top down deployed...trainingChanged manager...incentivesDe-professionalize...quality role

Customers define

Manage Q across...the value chain

ScientificMethodas BasicWorkProcess

QC storyKaizen

Imai

Two forms of...management:...maintenance &...kaizenTwo aspects of...jobs: perform &...improveEvolve goals from...easy to stretch

Ishikawa diagramPareto of customer...dissatisfiersQC story boardDaily and monthly...management of...kaizen targets5 whats, 5 whys,

QC circle activityCoaches for mana-...gerial solvingEmphasis on root...causes not solving...symptomsNational hierarchy...of circle confer-...ences

Deployfunctions

QC...circlesLine cen-...tered...org.nPolicy...mngmt.

MizunoKawaseAkao

AlignmentQuality

Let workers know...and improve...their own pro-...cess capabilityDeploy functions...to line from...staff

Defined solving...processProcess definition...process deployedQC story standard

Case conferences as...in law, medicineNational circle...conference hierar-...chy

Data based finding...of problems, cause,...and solutions

Catch ball between...org.l levels

Process

Process...mngmt.Cross-...function...mngmt.TQC

Suppliers = you

Ishikawa

Defect prevention...not detectionNext process is...my customer

Bottom up innovatn.

Cross-functional...careers neededAccount for non-...local effects

Horizontal catchballPut manager hier-...archy sidewaysPut new managers...as process mngrs.TQ monthly staff...meeting of own &...other fn. targets

Flowcharting7 Management ToolsStaff meeting cas-...cades by processVP of timeliness,...of at my price, etc.Inter-organizational...staff meetings

Transpa-rency

QFDPolicy...mngmt.

AkaoMizuno

Link across func-...tions and levelsKnow process...capability of the...organization as...a wholeBig deeds in one...year, yearly

MatricesBusiness as transla-...tionYearly post mortems...per targetMonthly daily...mngmt. gap diag-...noses

CatchballDaily monthly...management

CognitiveCompeti-tiveness

TaguchiKansei...engi-...neeringDelight...quality

TaguchiKano

Surface inarticu-...late customer...wantsSubstitute experi-...ment design for...dataless policy...makingEngage determi-...nants of wants

Principal components...analysisOrthogonal arraysProbe productsNeural net facial...muscle emotion...detectors

Statistically educa-...ted managers be-...coming executivesProduct develop-...ment teams that...do their own...market research

Quality

Quality

...5 hows

Kano

The Tools of the Total Quality Movement

The Tools of the Global Quality Movement

Debuffering Managing by Fact

Workforce Deployment

Process Engineering

Just in Time SPC

TQC

CirclesQCStory 7 Tools Sets

Taguchi

Kansei

Quality Functn Policy

Research

Kaizen

Re-engineering

Supplier/

ChainsCustomer

Agile Organizatn.

Statistical Management

Software

KnowledgeReduce

Physical

Inventory

ReduceToleranceInventory

ReduceBoundaryInventory

ReduceAuthorityInventory

OptimizeIdeal Flow

OptimizeSignal/Noise

Ratio

OptimizeLinear Function

Not Point Values

MeasureManagerTampering

ReduceVariation:Common

CustomerPull

System

FoolproofedJobs &

De-Profession-

alize

WeeklyLeadership

Commitments

Distribution ofProblem, Cause,

Solution, Implementatn

Deepening ofProblem, Cause

Solution, Implementatn

Measurement ofProblem, Cause,

Solution, Implementn.

Wholeworkforcetrainingcascade

ManageBy

Signal

ControlPointFlags

StandardMeetingProcess

HorizontalVertical Diagonal

Cross-orgTeams

Bottom UpAwards

Competitions

Benchmarking ofProblem, Cause,

Solution, Implementatn.

t

High techCircles

AssumptionBreaking

New materialsfor workfunctions

Outsourcing

ProcessImprove

Next processas

Customer

Modelprocess

Optimizecross-orgprocess

Deploycommonprocesses

Deploycommon

datasystems

Deploycustomer

Requirements

MeasureCustomerEmotions

Definemurmurs

Make orgemotionallytransparent

Firm asstatistical

experiment

StandardizeOrganizatnComponents

Internet

Assembly ofFirm Components

Market

Use tuningfactors to mesh

subsystems

Obtaincustomerrequire-ments

TPM

& Special

Customercomputed

firms

RelationshipSelling

Venture

Horizontalalignment

Verticalalignment

Inter-organizational

alignment

Market speedalignment

Tracingexecution of

customerrequirements

Tracingexecution of

new capabilities

Tracingexecution of

new technologies

Tracingexecution ofbusiness

opportunitiesHorizontalcatch-ball

Verticalcatch-ball

Inter-orgcatch-ball

Customer-Suppliercatch-ball

customer/operationtrade-offs

goal/capabilitytrade-offs

advantage/uncertainty

trade-offs

profitrisk

trade-offs

A Possible Tactical System for

The Tools of the Global Quality Movement

Liquefy Management Social Automata Leadership

Virtualization

Movement Meshing

10 QualityMovements

Value-MeshingPractices

Manage ByBldg. Movts.

Manage ByEvents

Just-in-TimeManaging

Social AutomataProcess

InversionOutsourcing

Computer

SocialVirtuality

Micro InstitutnDevelopment

ParameterAdjustment Emergent

SocialComputations

MovementElicitation

ArendtFreedom

GEStringentMeasures

GreenpeaceChallengeChapterize

GreeneInstitutnalizeEmergence

Social AutomataMovements

Economic Political

Cultural

Foundational

ManagementFunction

Explicitization

Select/CreateBasic Units

CreateStates/Behaviors

CreateNeighborhoods

CreateInteractions

ConnectednessDiversity

Patchings

Social

Socially ViableProduction

Unit

Self EmergentSurplus

BiologicGrowthProcess

RitualCelebrative

FundamentalsDiscipline

FitnessRecognition

FitnessReplication

EmergentPattern

RecognitionSocial

Automata

Challenge

LocalPractices

Invite LocalVolunteers

Organize/EquipVolunteers

inChapters

Global Goals &Combinechapters

Extract Expert

Protocols

Structure HumanArrayProcessor

Invite OnlyContributors

Workshops

DesignSelf Sustaining

Follow UpProcess

Identify Type,Amount, and

Timing ofManagingFunctionNeeded

Identify ExcessManagingFunctions &

ManagersSupplied

ArrangeAlternateDeliveryMeans

ManageFunctionNeeded

Recognition

BestManagementProtocols

TeamingLayer

Elicitation

Bureaucracyas

Capability Library

Policy Result

Research DetermineFundamental

Values of EachMovement

InventPractices

CombiningThose Values

AllocateRoles Among

All Stakeholders

ConfirmEfficacy

Set Up SocialAutomata

AdjustParameters

RecognizeEmergentPatterns

Adjust

Automaton Units

IdentifyNon-workLocales &

Times

Equip forProduction

Play-cate WorkLocales &

Times

ShrinkJob, Expand

Lifework

Identify FunctionsWithout Need

forCompetitive-

ness

Outsource allNon-competitive

Functions

ManageOutsourcingContracts

ImproveSupplier

Performance

Micro-PricingInternetAnalysis

Virtual WorldInterface

Design

Virtual WorldVisibilityTactics

VirtualReal WorldInterfacing

BasicTeam Units

TeamCombinations

Team &Combination

Events

WorkspacePluralizatn

Global Community-Based Conservation of Coastal Zone Resources

Simulations

The WHOLE Organization and WHOLE Person as Doers

What software& devicesare learningto support

TotalizedQuality asthe ESSENCEof business.

TQM totalized the treatment of 1 body of knowledge --quality.

What otherbodies of

knowledgewould thrive

if totalizedsimilarly? the web?

TABLE 1. Measuring Customer Requirements: 10 Measurements for Each of 22 Feature Types per Product or Service; Copyright 2000 by Richard Tabor

Greene, All Rights Reserved, Government Registered

MEASURES PER FEATURE TYPE:

degree of satisfaction with the feature

relative impor-tance of the feature compared to others

what feature aspects determine degree of satisfactionwith this feature

satisfactionwith info provided on the feature

changes in future customer situation that’llprobably changecustomer satisfaction

distinguish basic, normal, and delight quality of the feature

kansei map of custome experssionsontoo feature aspects

find trade-offs among feture aspects and features acceptable to customers

customer compar-ison of competitor offerings on this feature

fill ins on how customer evaluates, can help, plus other comments

basic features (shared with similar products/services)

advanced features (unique to this product/service)

packaging and interfaces

guarantees

tangible sensory impacts

semiotic features (fad/fash-ion/status)

acquisition process

use process

repair process

upgrade process

replacement process

get rid of process

service reliability

service uniformity/variety

service responsiveness

service assurance

service empathy

service timeliness

service expressiveness

service uniqueness/novelty

systematicity (fit/expense with related products/ser-vices)

adequacy of information on above features


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BIGNESS is a DISEASE = You must counteract its cultureThe KEY to big-ness is QUALITY smallness

Fractal of Contents

Are You Creative?60 Models of Creativity and the 960 Ways to Improve Your Creativity that They Suggest

By Richard Tabor Greene

2526

272829

30

SO

CIA

L

KNOWLEDGE

EVOLUTION

Compilation

FractalRecurrence

Cycle

SystemModel

60Models of

CreativityCopyright 2002 by

Richard Tabor GreeneAll Rights Reserved

US Government Registered

11

23

4

56

CATALOGRecommen-dations

Traits

Question Finding

Darwinian Systems

CombinedThought

TypesGarbage Can

27

8

9

10

11

12

BLEND

Culture

Mixing

Disci-pline

Com-bines

Tuning

Paradox

Door-way

ScaleBlend

IdeaMarket-

ing

3 13

14

15

16

17

18

Com-munity

ofIdeas

SocialCompu-tation

Social

Move-ment

SpaceShare

Parti-cipatoryArt & Design

419

20

21

22

23

24

GR

OU

P

MassSolving

ProcessDeploy-

ment

OptimizeIdealFlow

Meta-

SocialConnec-tion-ism

Demyst-

ification 525

26

272829

30

KNOWLEDGE

EVOLUTIONRelocating

Idea

ProgramsSimple

Dialectics

FractalRecurrence Idea

Waves

631

3233

3435

36

EXPERIMENT

Solution

Culture


Creation Events

FractalModelExpan-

sion

SocialAutomata

Create by Balancing

7373839

40

41

42

SYST

EM

Non-

Linear Systems

Darwin-ian

System

Effects

Surprise

AdjacentBeyond

PopulationAutomaton

843

44

45

46

47

48

PU

RIT

Y

Sub-creations

Produc-tivity

Perfor-mance

Influence

Investing

Info

Design

SEL

F

949

50

51

52

5354

Courage

AnxietyChannel

ExtendedSelfDeve-

lop-ment

InterestEcstasy

CareerInvent

Perfor-manceCrea-tivity

MIND

1055

56

57 58

59

60Insight

CognitiveOperator

Extremes

MakingSense

PerceptInvent

ExperienceRealizatn.

SubstrateUpdate

Cognition

Ecosystems

These models aregiant collectionsbased on single themesor matrix frameworks.

These modelssee creativity as social relations & dynamics of sorts.

Thesemodels see

group-nessmaking

procedurescreative

Thesemodels see

dynamics amongideas themselves that

become creativity

These models seesolids disolved into hypotheses making creativity.

These modelssee non-linearaspects of realitythat make creativityinevitable inthe universe

Thesemodels see

some singlefunction or

aim that donepurely resultsin creativity.

Thedynamics

of creatinga self if

extendedresult increativity.

Within the mind dynamics or constructs that “create”.

Thesemodels are

mixtures, blends,and combinations

of things.

p23

p33p44

p56

p75

p89

p125

p134

p142

p162

p183

p192

p206p216 p225

p239

p250

p262

p285

p300

p314

p323

p349

p358

p377

p388

p401

p411

p426

p439

p462

p473

p489

p499

p511

p520

p534

p544p562

p587

p599

p617

p645

p656

p664

p674

p686

p700

p717

p734p749

p763p775

p787p810p823

p836

p846 p854

p866

Page 8 Overview of the 60 ModelsPage 22 Catalog Models HexagonPage 32 128 box Recommendations ModelPage 70 to 74 Scientific Creativity Quad-HexagonsPage 93 48 Culture Dimensions TrianglePage 100 Career 64 QuadPage 114 64 Social Processes QuadPage 121 Catalog Models CombinedPage 124 Blend Models HexagonPage 173 Power Creation Quad 64Page 168 Social Automaton TablePage 202 Blend Models CombinedPage 205 Social Models HexagonPage 260 Cluster Creation SpacePage 281 Social Models Combined

Page 533 System Models HexagonPage 559 Simonton HexagonPage 573 System Effects 128 TrianglePage 579 326 System Effects HexagonsPage 604 Adjacent Beyond 64 QuadsPage 641 Systems Models CombinedPage 644 Purity Models Hexagon

Page 711 Creation Engineering 128 TrianglePage 712 Purity Models CombinedPage 716 Self Models HexagonPage 805 Composing/Performing 128 TrianglePage 806 Self Models Combined

Page 809 Mind Models HexagonPage 876 Mind Models CombinedPage 879 Flow Diagrams of All 60 ModelsPage 982 Grounding Example Tables for All 60 ModelsPage 1056 ReferencesPage 284 Group Models Hexagon

Page 329 General Computation Page 331 Social Process Event Types

Page 332 Ways Orgs Learn 64 QuadPage 336 Organizing for Meta-CognitionPage 344 64 Ways Orgs Learn TablePage 367 Rise of Science TablePage 373 Group Models Combined

Page 376 Knowledge Evolution HexagonPage 444 96 Simple Program IntuitionsPage 457 Knowledge Evolution CombinedPage 461 Experiment HexagonPage 530 Experiment Models Combined

Introducing:

Fractal Concept

Models

Covers:Kaufmann’s InvestigationsWolfram’s Simple ProgramsAbbott’s Chaos of DisciplinesSimonton’s Origins of Genius

and many othersRoot-Bernstein’s Discovering

A Self-Study Text

on Creativity that Any

Person & Any Course

in Any Discipline

in Any College or

Company Can Use

Today!

The MostComprehensiveBookon Creativitythat hasEver beenPublished!

The Next StageAfter R&DArts & Sciences

Individuals & Groups

NGOs & Companies

Ventures & Styles

Fashion & Tradition

P66

THERE ARE AT LEAST 60 WAYS TO CREATE NOT ONE = YOU MUST MASTER THEM ALL

If you and your firmdo not know what agood person and good manager areyour business willlose its bestemployees andleaders to firmsthat DO know,that have adetailed mapof what“great”peoplelearn andcan do.

Being a parent, being a spouse, being a manager, being a friend,

being a leader ALL require spotting great people & making people great SO what is a great person?

QUALITY OF PERSON

DESIGNS THAT LEAD USERS TO SEE AND PLEASE THEIR CUSTOMERS,

LEADINGS THAT DESIGN PROCESSES TO NOT NEED LEADERS

TAGUCHI APP:1. Persuade bossto do Taguchi early= app had financecharts for persuad-ing bosses on whento us the app.2. Process of usenot menu interface.3. WRAPPED around,helped input/outputof competitor apps.

QSOFT APP:1. Sold as simpledatabase for TQteams, easing hated reports toexecutives2. INSIDE hiddenwas general process automaterusers graduallydiscovered/used

INVENT EVENTS:1. Acquaintancegroup sizing = 1252. 25 in parallelworkshop teamsusing expert proto-cols3. Competition forextension teamservice pairs.

TQ VENTUREECOSYSTEM INVENTS1. 800 people 8days 16 tech venturesdesigned, funded,legalized, staffed.2. PLUS 16 “Valley”events,processes,orgs BETWEEN theventures.3. No “participants”invited, all lead

FINITE ELEMENT-IZED EVENTS:

1. Police forbid400 person masspolicy design eventSO circulated among25 sake bars nightlyasking 1 questionof 400 workersper night + ASHIYA

FRACTALINTERFACE

1. the problem:access 1 of 7000choices in 1.2seconds or die

2. Solution--regularfractal ordering of 7000 choicesto memorizein 4 days

3. Interface--4digit phonenumber =9x9x9x9

FACE TOSCREEN RATIO

Xerox OEM lostsale arguing across

FIX = 4events all: budget,

sales plan, product strategy,

4 sitesfor

each in differentcity yearly

VISUAL DOMINANCE FASHIONS1. all designs data tested against couture2. catch eyes 1st hold longest metric3. discovered: true random shapesbetter than pseudo random; fractal 3 layers--design inside design inside design works best, etc.4. kimono shirts: collar covers,zip fronts, sleeve vents5. sleeveless suits, 2 centimeterfront gaps, double off angle zipsremove-able sleeves too6. data confirmation of Taoistdesign principles

FIXSTRAINS

OFCUSTOMERS

dirty collars,hot sleeves,

ugly GAP cloths,Calvinist norms,

institutional ruttingss

BICYCLE1. rotate from racer

position for viewing

2. seat back for faster speeds3. electronic assist for safe returns

4. comfort seat since non-racing5. raised handlebars for upright view

6. close bars for shoulder ease7. 180 degree mirror

8. metallic tape coverfor no accidents

9. foldable rain cover10. GPS chips theft-recovery

WHICH T.Q. WAYS DOES EACH Richard Tabor Greene DESIGN BELOW EMBODY OR USE?

JET


META-WORKUse entire workforce as computer.15% of all worktime for improving

how we work.Upgrade improvement toolkit every

two years without endTill 10,000 Phd.s a year of process

research get done

TOTALIZE TREATMENT OFONE BODY OF KNOWLEDGE

--first we totalized treatmentof finance knowledge:

--then quality knowledge--what next? web? big data?

QUALITY THUS-LY TOTALIZED

defeated, globally, the EastCoast USA MBA religion ofbusiness; 11 industriesstill today, dominated byJapan.

TOTAL QUALITY IS NOTJAPANESE MANAGEMENTCULTURE--it was designed byJapanese to UNDO usual suchculture: from vertical to hori-zontal, from opinion of rank todata basis of decision, from functional fiefdoms to cross-function teams

A NEW PUBLIC SPACE--CIRCLESRule by Process ExcellenceNot Top Manager OpinionFrom library of bureau compe-tencies draw process layer,

from process draw teaming layer, from teams draw events,from events draw new bureaus

A TQ THEORY OF THINKING--ROOT THINKING

A TQ THEORY OF LEADERSHIP--BUDDHIST CLEAR MIND PERCEPTION OF CAUSES:

Mindscape recognition: 1) fix leap to solution withoutcauses 2) fix seeking causes near whereproblems appear 3) fix systems sized forideas not for people’s use 4) etc.

1) clear wastes 2) inventory is waste3) management is waste 4) meetings/reports are waste 5) expose causes

“muge” transparency= buddhist priority of percept6) make causes-fixes-effects VISIBLE to all

THE T. Q. THEORY OF BUSINESS1) all processes multi-firm 2) pull from customer times entire process 3) each step has customer-supper relationship 4) customer satisfaction must

always be measured not assumed

COMPU-TATIONAL

SOCIALITYsocial connections

preceeding webconnections;

use entire workforceas computer;

DEPLOYMENTS--policy via up-downcatchball; quality by left right catchball;from thinkingby events tomanaging byevents

TOTALQUALITY

UN-DOESTO QUALITY

WHATUNIVERSITYPROFESSIONSDO TO IT

USING TOTAL QUALITYFOR DESIGN & LEADING:1) Focus designs onthings that helppeople satisfy theircustomers

2) Focus leading by reducing leading toexpose natural in-process dynamicsthat counter customer needed process outcomes, the fix processesto no need future leadership

3) TOTALIZE DESIGN TOTALIZE LEADING as quality totalized them.




MOVEMENT OFMOVEMENTS

within firms,across chains,within industries,across nations

COMPUTA-TIONALSOCIALITYusing entireworkforces as computers

META-WORK

15% of worktime used toimprove HOWto work.

DEMOCRATIZEDSCIENTIFIC MANAGEMENT by fact no rank’s

opinion = stats

SOCIAL WEB LEDCOMPUTER WEBJIT pull thru processesguided web application

EVOLUTIONARY MASSSOLVING till entireworkforce does PHDeach year.

BUSINESSESSENCE

4-6-9 functions: listen, preserve, Both customers

techs

DEFINED

CHANGESEQUENCE:totalize, remove, customerize, processize, evolve solving, expand system scope, line centered orgn.

GURU SERIESTaylor, Shingo,Dem-Fei-Juran,Imai-Kano, Mizuno-Kawase,Ishikawa, Akao,Taguchi, Kano

APPROACH SERIESdebuffer, process, scientific solving, scale expansion, workforce deployment

FAT SERIESdata, idea,procedure,career, opinion,training, supervision,coordination,learning,launchescompetencesCYCLE SERIES

involve, debuffer,

solving, process, daily management, visualization, combinatorics, collaboration

TOTAL-IZATIONSSERIES:

war on variation,human scale,make visible,break bounds,align voices,tool generations,knowledge deploy,diversify,new commonsense

PHASE SERIES mobilize,

produce, align, requirementsVOICE SERIES customer, supplier, process, CEO, tech

GENRE SERIESProfessionQ,

DownstreamQ, TopDownQ, SolvingQ,

BottomUpQ, HorizontalQ, AlignmentQ, UpsteamQ

NEW UNIT OFCOMPETITIONcross-teams layer,outer ally layer,firm population layer, business core layer, knowledge core.

24 TOOLS: debuffer,manage by fact, work-force deploy, processengineering, social auto-mata leading, liquefy management, virtualize,movement meshing

ANTI-CULTURESERIES:anti-male, anti-Japanese, anti-monkey hierarchy,anti-money-grub,anti-tech, anti-USA,anti-MBA, anti-profession

EXPOSE WITH DATA:process flaw, system flaw,executive flaw, systeminhumanities, rule bymonkeydynamics

DEMOCRATIZE:science, data access,

data generation, solving, system design, business design, whole workforce invention

HOW A DEMOCRATIC

BUDDHIST DEFINES WORK,BUSINESS, LEADING, QUALITY

Line Centered Orgn. Ideal--all non-line work is WASTE, COST.only work that adds value custo-mers receive, is allowed.

MANAGEMENT is evil, sheer waste

FORTY YEARS OF ENTIRE WORKFORCES SOLVING

SCIENTIFICALLY FOUND WHAT?1. That the root cause of all work problems is top manager incompetence, statistical, complexity, cultural tampering

2. That all management/leading comes from lack of processs design

LEADING IS WASTE FROM BADLY DESIGNED WORK PROCESSES = GOOD DESIGNS LEAD

THE NATURE OF THIS COURSE IS:

NOT VISUAL EXAMPLES GENERATORS OFCONCEPTS

EXPAND YOUR MIND’SMENTAL OPERANDS

TOOLS FOR ESCAPINGALL CURRENT CONTEXTS

FROM CRAFT TO HISTORY CHANGING DESIGNS, LEADINGS,

QUALITIES

INTERACTIONS

designleadership

quality

total quality

structural cognitionjobs’ Silicon Valley

at mid-career STYMIED due

to visual example TRAP;VISUAL vsVERBALdesigners

examples trap you in whatalready WAS done; conceptsexpose abstract dimensionsunpopulated in history so far

TRAP ONE--applying mentaloperators to same number4 to 8 ideas at a time as allelse do, HERE to 64 or 128!

The POWERS OF NEGATION:Being nice to you and all is--

TOTAL QUALITY give usthe POWER TO LEAD & DESIGN

STRUCTURAL COGNITION gives usthe POWER TO THINK

JOBS’ SILICON VALLEY WAY give usthe POWER TO make DESIGNS THAT LEAD

by distinguishing what is WASTEfrom what is CORE

by revealing STRUCTURES of ideas,their LAYERS of CONTEXT, and gettingus to design IDEA STRUCTURES

and the POWER TO lead by DESIGN GENERATION

TOTAL QUALITY defeated US East Coast MBA Religion of Business;SILICON VALLEY defeated Japanese Total Quality Religion of Business;JOBS defeated Silicon Valley Religion of Business;STRUCTURAL COGNITION is defeating JOBS “devices that save dead industries” ways

VICTORYa Course About

TRAP 2: PROFESSIONALNEUTRAL OBJECTIVE THOUGHTCoke Cola is EVIL not “product”Chair designs are killers not OK

RECOVERIN

G

EMOTIO

N



9 TOOLS + A MAIN TOOL1. to find what makes a market

unique--Extreme Product Extrapolation;

to scope tipping points-- Strain Analysis

2. to satisfy customers-- 22 Dimensions

3. to measure satisfaction--Questionless Questionnaires 4. to focus designs & leading--TQ Spec-ing

5. to escape flaws

in thought--Root Thinking

6. to design beyond all pastdesigners--Dimensions of

Difference Analysis7. to amaze--

of creativity8. to remove vagueness--

Research Events 9. to amaze-- anthro/theo liberations

Femininity

10. to fix/replacebrainstorms--

SocialAutomata

find absurdbut successfulproducts, whatmakes themsucceed? Inventa product for all such found dimensions

what all version share,what’s unique to this one,processes of get/use/fix/etc., status & systems fits, info on all

choice lists, fill ins, trade-offs you’d accept, what might change your evaluatn later,

make every feature fix root cause of why

something in customer’s

process causes customer’s customer dis-satisfaction

look for causesnot where problems appear but throughout system; do not leap from problem to solution instead address causes

line up past designs then

identify for eachpair dimensions

by which theydiffer, then interpolate,extrapolate new dimensions, then

make new designusing them

measure how male your presentdesigns are, then via 32 ways,make them more feminine and test for greater creativity and/or

productivity

gather library-web literature, find3 layers of whom to call and what toask about, after each round of phoning,upgrade who to call & what to ask

review what tribal

ways theclients

want to beliberated from;

and life parametersthat chafe them,

then make designfeatures thatliberate!

arrange peoplein groups of 6;

assign mental operationfor each of those 6 to

repeatedly apply to partialresults handed to them from

others of those 6; at time intervalsof 2, 5 etc. minutes, all

apply their operationon their partial

results, accumulatingnew design per groupof 6 every 2/5 min.s

TEN TOTAL QUALITY TOOLS FOR DESIGNS THAT LEAD, LEADING BY DESIGNING

5 POWERS: Quality, Structural Cognition, Jobs-Design, Theory, & Creativity Sciences

Your TQual Opportunity:1. Total Quality is generaltheory of all business/design2. TQ is done poorly by all3. TQ guies web-tech use

Your SCogn Opportunity:1. There is published theorybasis of SAT, GRE, Toefl tests2. How top professors think is researched and published3. Six methods and you excel!

Your Jobs-SV Opportunity:1. Design as liberations, leadership as liberations2. 11 cultures to liberate from3. Overcoming your own Initial Fctory Settings as kid4. Extend from 1 CreativityScience to a dozen+ at once

BUSINESS RELIGIONS:British Empire--killing-tradeUS East Coast--Robber Baron MBAsUS West Coast--democratized techJobs--designs saving strainedindustriesBig Data USA--structural cognition= orientation in info floods

THREE GREENE STUDIO COURSES:1. This intro 3-day 3-way course2. Two one-weekend per monthfor a year courses (each = 8 fulluniversity 1 semester courses):a. Quality Skills for Global Executives120 Methods in 8 Genres of qualityb. Creativity & Novelty Sciences SCHOOL30 Creativity Sciences in 1 Year

THEORYPOWER

The more theoriesyou have,

the more diversethey are,

the more abstractthey are,

the more youNOTICE,

the more youIMAGINE

the more you can do

in situationsThe BiggerYour World

IDEA

POWERapply ordinary

mental operators to 50 ideas whileothers apply to only 4 or 6,VAST NEWMENTALPRODUC-

STRUCTURE

TIVITY

30 CREATIVITY &NOVELTY SCIENCES

POWERUse plural diverse models of each while others are stuck using a favored one or 2; Use plural creativity

sciences while others repeat 1 or 2; Use interactions of 30 creativity sciences that others miss.

FOCUS for design, leading from Total QualityLIBERATIONS for design, leading from Silivon Valley Jobs’ WaySCALE for design, leading from Structural CognitionAim better, Follow less, Do more

The power of no waste, the power of beyond cultures & self, the power ofoperating on patterns of many diverse ideas, the power of noticingmore, the power of using 30 ways the new gets into human affairs.

POWERS

THE NATURE OF DESIGN-LEADERSHIP PROJECTS & TEAMS IS:

BUILD X TOGETHERTeams of 5 (or 4)

SLEEP W

ORK

Decid

e 1

issue to

COMBINE on EACH TEAM:

More than one experienced people

= use viewpoint differences;

Experienced with novices

= fresh questions force experience

out of ruts

Famle-led teams & Male-led ones

= liberate from eons of male-ness

CONCEPT DESIGN BUT?

Not “design anything” but

“what needs designing?”---

JOBS--what industry is dead

and needs saving NOW?

SV--22,000 new techs, all die

that fail to find customer uses

PROJECT EMERGENTS:

Concepts are launchpads, they

must be kill-able mid-stream;

Insights emerge, NOTHING good

gets designed without MOTIVE

POWER, INTEREST, GROUP ELAN

TEAM LEADERSHIP:

a function not assigned role =

whoever spots what is ruining

progress and gets it handled,

examples: Big mouth males =

shut them up; Loyalty to original

team idea = restart, etc.

TEAMS & PROJECTS OF

THIS COURSE:

Goal is learn & apply 3 new

methods per day, in team of

strangers = choose something

all can test new methods out

on, INTEREST + NOT TOO HARD

1. Tools do NOTHING bythemselves, People USEtools for strong PURPOSES

2. Teams need notstay TOGETHER--at time 5 separate people-aims, attimes 2 competingidea-subteamsat times all together

3. Using newtools = error,slow, but also fresh ideas,

BE GOOFYPRODUCE LOTS

requires looseness.

REVERSE TEAM SOCIAL PATTERNSAt Intervals of Every 2 Hours:1. Old guys shut up, youth talk and vice versa2. Males shut up and females talkand vice versa3. Visual artists shut up and engineers talkand vice versa

BUILD TEAMS AROUND INTEREST & MOTOVE

AFTER 3 HOURS TOGETHERWrite “Our Team’s Rules for Succeeding”Considering who you are,what you aim for, what WILL insure success(What good in each personshould we use, what bad should be block)

NOT CAPABILITY--People who WANT to work togetherWhat will excite ALL of us to immense effort levels?What will benefit from blending what we all are?What do we ALL need to stretch ourselves towards?

sleep o

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EACH T

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AS

50 M

EMBER

S:

5 fa

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mem

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+ w

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SOCIALLY INDEXING GROUPS

1. Stand in 3 experience\age groups--under 5,under 10, more than 102. Stand in 2 genres--devices/software & arts3. Split Male Femalein each of the 6 groups4. Each person holds upcard showing their:2 biggest interests 2 things they want to design in next 3 days

generally poorly done

OF DEVICES PRODUCTS

1) measure impact on

social indexing in

groups of features2) measure how high

social indexing

improves performance

3) redesign for higher

social index impact4) predict sales/versions

by culture of device

interactions with

culture of users

OF GAMES1) subtract some reality from work systems you get simulations, subtract more you get games2) Gaming Theory (not Game Theory) is a Theory of Fun—what emotional, social, intellect moves please us most---how can we turn boring work into games of fun & intensity, reducing system errors

OF DECISION SYSTEMS

1) the unit capable of intelligent thought is NOT any lone individual, TRIOS of 2 women & 1 man out-think any 3 lone individuals

OF PRODUCTIVITY1) The Femininity of Productivity, systems that feminize systems make them more productive2) even technologies that are more productive feminize systems!

OF ARTS1) 64 functions of all arts, got from 150 famous artists2) use it to predict auction prices and later fame levels by which and how many functions covered by a work3) use it to guide composing great works4) use it to add aesthetics to engineered systems

OF WEBSITES & INTERFACES

1) fractalizations

=

embedding spaces within

themselves, with analogous

functions on all levels at

the same position 2) uniting visual, language,

and muscle memory via

regularizing choice spaces

3) emotional drill up and

down—the human

journey/story of site/inter-

face exploration

OF EVENTS1) events mix with web & social net systems2) work evolves from event form to process to years later departments3) new KINDS of event:mass workshops, 20 teams in 3 days, whose result combine into POWERFUL result.4) events empowered by new social automata forms of meeting and structural cognition tools.

OF MEDIA1) inventing video that informs not decorates2) establishing a publishing-of-readings industry3) inventing a replacement for ancient bad interfaces: a) prose—replaced by fractal concept pages,b) meetings replaced by social automata c) discussion replaced by scientific rules of order

OF REPLACEMENTS for PROSE, MEETINGS,

DISCUSSIONS1) prose hides count, names, order of points---fractal pages show instantly all 32) meetings allow feigned interactings, social automata make more ideas faster, each using exact ideas of others3) discussions leave each convinced he/she is righter than others: stratified responding shows how each notices & feeling different things in each situation, only combinations are accurage

OF LAWS & POLICIES

1) our processes for design of laws & polities are centuries old and no design firm would use them2) apply best IDEO etc. design tools and engineering system design tools to law design3) create self adapting laws using built-in social media surveys

OF CULTURES1)selves, high performances, & cultures are ALL the same---results of repeated practice2) intense shared effort that requires inter-dependency, crises, shared enemies, & professional practice ALL create new cultures = how create the culture we need, & fix those we now have

OF STORIES:CAREER, MOVIE, SELF1) there are 8 types of story by mood aimed for, but 1 cosmic story they all subset from2) all stories are about leaving self, that is the educatedness journey3) go to another world, come back with magic power and unafraid

DESIGN

DESIGNOF

FASHIONS1) experiments on what patterns get attention first, and hold it longest2) example: true random intersections attract better than human random-ish lines3) fractal design attract—deifferent but related patterns from 5 meters, 2 meters 9.5 meters 4) tactics to work-a-room using only fashion

DESIGN

DESIGN

DE

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GN

DESIGN

DE

SI

GND

ES

IG

NDESIGN

DESIGN

DESIGN

DE

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34 Copyright 2014 by Richard Tabor Greene, Rights Reserved, for hire De Tao Masters Academy

LANDSCAPES OF LEADERSHIP & CREATIVITYHandled 2 Ways: Woven thru Many Courses + Special Event CurriculumLANDSCAPE TYPE AS CONSTRAINT AS POWER

For LEADERSHIP For CREATIVITY For LEADERSHIP For CREATIVITY

BrainscapeBrains incapable ofrationality, distortive ofreality in known ways.180 known flaws inthought

Distorted Thoughtbecomes Failed PoliciesPlans, policies, laws,rules, not designed withthese in mind, donothing, do harm, or dothe opposite of theirintent.

Designs get Mis-used byDistorted User ThoughtsDesigns and inventionsthat assume users arecapable of rationalityand not flawed inthought, do nothing, doharm, or do theopposite of their intent.

Leading that Uses theFlaws in Minds GetsReal ResultsPolicies, structures,processes, goals thatappeal to us torecognize the flaws in usand go beyond “whatcomes naturally” withthem, have stellar,historic scale results.

Designs that CounterUser Distortions ThriveDesigns, performances,creations, etc. that takethese flaws in our brainsinto account, magicallyachieve far more thanamateur folk solutionsand fixes.

SocialscapeThere are at least 16ways to be social, allunmeasured today andanything not takingthese, oftenunconscious, socialforces into account,bounces off of realityrather than helping,penetrating, orimproving it.

Unseen Social ForcesUsually OverpowerLeader IntentsCareer systems generateimmense creativitynoise—creations forvisibility to top execsand careeradvancement, not forcustomers and generallyjust started notcompleted. Leaders nottinkering with suchsocial forces, getnothing butappearances done.

Creations Fit into anInvisible Social Life orFunction DistortedlyThere is a “social life ofinformation” (JSBrownand Duguid), a “sociallife of devices”, “a sociallife of policies androutines of work”--it isinvisible, powerful,cross-department, cross-firm, cross-rank. If youdesign without knowingit, its forces distort, orundermine, orcounteract all you do.

If you Lead in All 16Ways of Being Social—Huge Power & ImpactEACH of these ways ofbeing social drifts ordecays without leader-ship—collective agree-ment emergent orcatalyzed by “a leader”.Most “leadershipdelivery mechanisms”omit 15 of the 16 orthem all. When leaderslead in several of themnot one or a few, impactand changes of directionare immense.

When a Creation Workson Several Ways ofBeing Social—HugeImpactThis invisible social lifehas power—invisibleissues and aspirations.Is a design, innovationprogram, performancerecognizes these anduses, redirects, fuses, ordistinguishes them—theimpact of what iscreated multiplies andexpands.

BioscapeWhen commonsensechanges, what isexcellent, goal, goodeffort changes—thatmeans the olderversions do harm, hurtmissions, are notenough. Bone is our newimage of strength—notsteel—because it growsstronger where stressedand repairs its cracksand breaks, unlike rigidmechanics of steel.

Leading Mechanically isWay Too Slow for TodayPlans and orders fail.The commander, whoplans strategy andimposes it onworkforces andcustomers is a mechano-sense hold-over fromearlier centuries—massmur-derer generals as“great” men of history.Today's bio-sense styleleader tunes inter-actions in populations ofagents till neededresults emerge from theefforts of all.

Static, Solitary DesignsFail Today—LackDynamicsStatic isolate designsfail. The idea of “adesign” as a static thingthat sits there and getsused for a staticfunction, is a mechano-sense holdover fromcenturies of tooling.Today's bio-sensedesigns are thingsdesigned to interact,learn, and evolve newfunctions with users ofthemselves, inecosystems ofinteracting otherproducts.

Leadership that WeavesFlows, Tune InteractionsThrivesWhen you, from thebeginning, approachleading in a bio-senseway, you shape, tweak,tune, lubricate, connect,diversity, spreadinitiative, help teamsevolve unlimited newpower from promisesmade and kept. Yougrow interactivepopulations of ideas,persons, funds,ventures, rather thanplan alone then work toimpose plans on personsnot involved.

Creations that Interact,Connect, Learn,Socialize ThriveAudiences of MIT'sdigital symphony inventnew sounds usinginvented instruments asthey file into the hall—which players weaveinto real-time-inventedworks as anorchestrating orchestra.This is the new creativity—live, digital, enabled,audience-invented,swarm-crowd,interactive.

SystemscapeNon-linear systemsare everywhere,though for centurieswe made linearmodel simplificationsof them and omitteddynamics. Personalcomputing enabledus to admit dynamics

Manage Reactions toActions not PlansThe first action of a plan,when done, changes thecontext of the plannedfor 2nd, 3rd, and otherlater actions, so theytake place inenvironments,unanticipated,unplanned on, havingless or worse effects

Ride Turbulent Changesof Context Instead ofFighting FluctuatingValueDesigns andperformances, etc.change the expectationsof audiences/consu-mers, so mere magazinearticles or newssegments make millionssuddenly dis-satisfied

Seeing What is Invisibleto Most Others – GreatAdvantage and PowerSystems have pent upforces, blocked orredirected, joining ordiffusing, invisible tohumans becausedelayed in time, spreadout in space, oroperating at differentsize scales. Leaders who

Aiming Features atWhat is Growing,Evolving = Easy SuccessApple has releasedproducts that saveddying industries byUSING forces thoseindustries resisted,opposed, or took tocourt. Silicon Valley hasa 100,000 firms makingprofits by riding trends,


into models, policies,leading, and designs.

than planned. Planshave to have step 2 takeplace in the reactions ofstakeholders to step 1 orthey fail.

with what they wereenthusiasts for an hourearlier. Expectations area seething turbulentocean washing over allwe make, changingvalues dynamically inun-anticipated ways.

see them, can redirectthem and use theirforces for humanbenefits and goals.

successes of others'products, and marketenthusiasms—designedto ride, invented toparasite on.

CulturescapeInvisible, every-where, centuries old,culture is every-where in and aroundeverything. To go outfrom home turf wemust go inward,undoing how wewere raised, what wenotice, fear, trust,etc. Devices, users,markets, bosses,firms have cultures,often un-intended,countering goals.

Culture Makes Fools ofElites, ThroughoutHistoryEuro-Disney in 2014,took billions of bail outmoney, again—to stayalive. 15+ years ofculture-caused failure,unfixed by generationsof CEOs, boardmembers, consultants.All the US elitemanagement tools andpersons, failed, failed,failed, year after year,decade after decade.Cultures constantlymake fools of our bestand brightest leadermen. The culture ofmales itself may be acause of this.

Creations, Designs,Genres Get Trapped inCreativity CulturesIf you look at the chairsall around you today, atwork, in hotels, athome, in cars—you findthat a culture, for10,000 years, has madeONE SIZE of chair eventhough the world ismostly, everywhere, twosizes of adults—a malesize and a female size.So women, worldwidesit in male sized chairsthat cut off blood to thelower legs of women.2000b.c. 10b.c., 100a.d.500a.d. 1800a.d.--unfixed for centuries—cultures of stupid designprevail all around us.Large car seats forpassenger women, kill24,000 women worldwide a year who slip outof seat belts.

Leading Cultures andCulture ChangesOverpowers ResistantInstitutions & InterestsThe leader arrangementor person that takescultures into account, orcreates needed cultures,magically solves whatgenerations of othersfailed to handle. Thinkof the Challengerdisaster—caused byengineers educated tobe subservient to MBAmanagers—so whenlives at stake versusschedule and manager-bonuses at stake wasthe situation, theengineers wimped outand it rained deadastronauts, costing thespace program over$200 billion dollars inreduced future publicfunding support. MBAswon and the programshrank to 1/10th itsformer size.

Escaping Cultures ofDesign, Performance,Etc. to Create NewCulturesCreators of all sorts,operate outside ordinaryculture to a certainextent—going beyondnorms and traditions tosurprise us with thenew. However, they stilloperate inside lots oflayers of other cultures,they use their creativesuccess to stay blind to.Frank Lloyd Wright is therare counter example—aman who was reborntwice, lives 3 differentdesign cultures,becoming a baby at age45 then again at age 75—re-doing basicmaterials, stresses,shapes, tools, to rise in15 years, each time backon top—a man who re-cultured himself.

TechnoscapeIt used to betechnology was cul-de-sac-able, given tonerds, off in themargin of work. Nowit is central to allwork processes anddeterminingoutcomes. Insightsare being activelyreplaced by morepowerful “big data”patterns found. Allwe try to lead andcreate rides on everupgrading substratesof device, connec-tion, information.

Switching Skill, Career,Person from Sinking toRising Techs is Hard,Rare, Often Too LatePitiful is the largecorporation, likeMicrosoft, embracingthe new, after 10,000small firms have longgone beyond it. Foreverracing to catch up withthe past, they are filledwith engineers andcareers steeped in oldertechnologies andconsumer needs. Sonyin 2014 with 20,000audio-video engineerswith nothing to do butlose money for theirfirm. Technology has itsown pace and you haveto keep up or die.

The Painful ContinualLeap to New SubstratesCauses Missed Futuresand Delayed SalvationsAll over the world designcolleges are in fights asboards impose digitalCEOs on artsy facultyand students. The newsoftware, digital, device“materials” of designand performance arehere to stay and growingexponentially changingconsumers intocomposers andproducers. Makinginstant crowd-sourcedconcerts and events. Itis a race that mostdesigners and designcolleges are notwinning.

Aiming not at Now SeenFutures but the FutureAfter That, Works WellOnce in a while a giantcorporation who ignoreda major technology shift,gets it right andmanages to re-inventitself not on the catchup platform, but on atwo-steps beyond thepresent platform, futureplatform the worldevolves toward. Intel in2014 did this, notplaying smartphonecatch up but makingLyktos style new insecteye types of cameraenabling entirely newsmartphone uses andcustomers. Leaderswhat to GUESS thefuture beyond ourpresently seen oneRIGHT to survive.

Designs, Performances,Creations that Ride theSeething New Flows ofthe Ever New in our EraWe all ride on animmense flood ofcreativity embedded inconstantly changingdevices and softwarefunctions in the thingson and around us. It isa lot of work just to keepup, much less to seebeyond the presentlyseen future and guessthe future no one hasimagined yet. Butdesign, composing,fashion, performinghave to do this—imagine the as-yetunimagined future “twofutures from now” aseveryone already workson the “one future fromnow” we all can seefrom the present.

EmotionscapeFor centuries emotion

Leaders Get Upendedby ProfessionallyIgnoring Feelings of Self

Designs Have to HandleSeething TurbulentFlows of Context and

Leading asPerformances thatOrchestrate Emotions

Creations as Dual—aCore and a Package, theLatter Designed to Cut


was low status femalestuff and shunned—notmanly, not leaderly, notcreative. TheEnlightenment tried tofree us from “thepassions”, of our“lower” bodily nature.But brain research foundthat rich emotions madefor better and moredecision in leader anddesigners. The emotiveones lead and createdbest.

and Other that Re-appear as Forces andReactions. Emotions travel fartherand faster than conceptsand determine what wenotice. So leaders, givento fascination withthemselves and theirown views and ideas,constantly have theirplans and aims undoneby emotions thatalready traveledthroughout workforcesor markets. Leadingrequires a rational ideadance that fascinatesand addresses needsand an emotional dancethat addresses fears.

Value ChangesThe creator all too oftenuses his or her ownemotion to create andblinds him/herself to theemotions in users orviewers of those works.Furthermore, theemotions in audiencesand users, buyers andviewers, is a dynamicecosystem from pluralmedia and personalevents hourly—so workshave to burrow throughlayers of seething “youshould feel this” and“better fear that”messages. Creationslose audiences this way.

Leader arrangements ofpersons (however youchoose to deliver leaderfunctions to a group)can act dually—on theconcept plane and onthe emotion plane,taking into account thatthe emotion planecontent will travelfarther and faster anddetermine reactions tothe slower conceptplane contents. Suchpeople are Steve Jobsand Tim Cook—turningproduct releases intoperformances so “buy”emotions rise weeksbefore available sales.

Through SeethingEmotions and ContextsCreators who see thelayers of seethingemotion messages frommedia, relations, and lifein consumers andaudiences, create a coreand a package aroundthat core. The packageis images and contentsto put away, subdue thecompeting emotioncontents inconsumer/audience/user environments. Thecore, then, gets through,with emotions set by thepackage, and competingnoise emotions alreadyat bay.

MindscapeThe like module of thebrain competes with theinterest module of thebrain—what we likebores us, what interestsus we at first fear.There are twenty otherpairs of brain modules insimilar conflicts.Leadership arrange-ments and creationseither take these intoaccount or getundermined badly bythem: analyze,synthesize; personalize,systematize; and others.Furthermore, thesemind type pairs meanthings decay—whatinterests us a week ago,bores today asfamiliarity increasesdecreasing interest.

People Often Lead OneMind Type of a Pair,Inviting Revolts by theOmitted Mind TypeMost of the time aparticular leadershipdelivery regime orperson as leader (one ofthose regimes)unconsciously favorsone mind type not theother of these 20 pairsat left. Thatautomatically rules outhalf the total mentalpower of everyonearound the leader. Italso creates abackground noise offrom the omitted mindtype of each pair, thatirritates but getsignored. We often seegenerational changes inleader arrangementsand results as aconsequence—theomitted mind types re-appear 10 to 15 yearslater via revolt.

Creations that Engage OneMind Type out of a Pair,Leave Users/ClientsUnsatisfiedDesigns andperformances that agerapidly and lose power,often are ones thatdepended on one mindtype not the other ofvarious pairs. Theirlimited engagement ofthe whole mind meansthe left out mind typeswork to undermine thedesigns, orperformances, causingsatisfaction to rapidlydecay. For designs thatlast, both mind types ofnumerous pairs have tobe engaged by featuresof the design in acollusive, cooperativemanner.

Leadership that EngagesAll the Mind Pair Typesat Once, Amazes Us,Gets Total EngagementYou can walk into anygroup or organizationand in a few days ofhanging around spotmind types, of 20 ormore pairs, that arenever present orrespected and used.This allows instantpowerful fixing ofenduring problems thecurrent regimes of thegroup fail to handle. Itlooks like magic—theinscrutable strangercomes into town andlike Clint Eastwood, witha few words, fixeseverything.

Products and Creationsthat Engage All MindType Pairs, Strick Us Allas Epoch-Making,Balanced in AmazingWaysApple was not first withanything. It did not havethe first iPod, the firstiPhone, the first iPad. Itcame later and made alater version of eachthat wildly succeededwhere the others hadbeen sad unprofitableniches. What madeApple's later versions sospectacular? If youmeasure you find theApple versions balancedmind types that wereunbalanced in the priordevices. The Appleversions were built forthe mental decay ofbrain module pairs—with user-invented appsthat “made the devicenew just as it wasbeginning to bore me”for example.

Computation-scapeFive types ofcomputational system arein dialog, generatingendless others—brains,societies, biology, mindextensions, and machine(computers). Physics,design, leadership,composing, performingare all becoming invarious unexpected ways“computational”.

Computation Moves Usto Dynamic, Real-TimeActions, Destroys PlansWhat elite and toppeople used to plan andcommand is nowridiculously out of date.Real time inventivesynthesis of variousstreams of liveinformation has to takeplace and turn intoproduct and service.Leader regimes thatslow things down till

Computation MakesThings More Social andHuman, Emotive andDifferent (Evolving)Devices, displays,furniture, andarchitecture arebecoming morecognitive, moreresponsive, morenetworked, moreinstantaneous, moremorphy (changing intoother things). As suchthey are all in dialog, co-

Leadership ThatComputes Hourly, Bythe Minute, Has Thingsto Lead and InfluenceLeader arrangementsthat work out thecomputation-ality of allthat is internal andexternal to a firm, aproduct, a person, auser, a client—adapt tothe actual real-time info,self-re-configuringnature of our times.They “fit” in a word,

The Creation ThatComputes Continually,Evolves Continually, is itOne Thing?The computationalinsides and thecomputational surfaceinterfaces and thecomputationalecosystem relations andthe computationalusers/consumers ofthings—all have to beconsidered and“designed” for.


after plans andcommands are issued,fall behind markets andcustomers, and out ofbusiness.

evolving, influencingeach other afterpurchase and forpurchase. Thecomputational realitiesof any “thing” are nowessential to what the“thing” is and does.

while those notcomputationally ima-gined, may sell for awhile but fundamentallyare not “fitting”, andhence, soon to besupplanted.

Everything that wasstatic becomesdynamics, everythingalone becomenetworked, everythingdefined becomesevolving.

GenderscapeIt surprises no one thatnearly everything in ourentire world, is overlymale today and hasbeen for centuries. Thelatest acid on face inAfganistan, disfiguring ayoung woman, tells usthe horrors we allemerged from when wecivilized our way out oftribes. Leadership andcreation need thisescape from thecenturies of malenesswe still suffer in. Thewhole human mindneeds to be engaged notjust monkey tactics.

Eons of Male Rule HaveMade Everything TooMale—We ThrowMalenesses at ProblemsCaused by ItTerritory fights,dominance, beatingothers, competitions,studying one'sopponents, markingterritory, bluster andthreat—all thesemonkey behaviors are alltoo central to what weall judge “leader-ly”. Aleader is a big monkey isthe individual piecesofwhat we all assess asleader-ly are to bebelieved. But wars,depressions, hugeswaths of poverty,emergent diseases frompoor to rich, and crimefrom poor to rich—makeus seek less hormonalleadership forms.

Creativity Sciences EachGet Stuck inMalenesses, UnawaresLike chairs designed forcenturies for one sizewhen two sizes are inreality there—like carseats of one size that kill24,000 women, but notmen, a year who slip outof seat belts sized formen—the world isdrenched in maledesigned things thatended up designed “formales” thoughnominally for all. Themale version of “all”turns out to be smalland partial and biasedand murderous. We allneed help escaping thehidden malenessesinside our imaginations.

We Solve by FeminizingLeader regimes thatfeminize workforces andproducts outperform byimmense margins onesthat perpetuate normalhormonal male actions,commands, territoryfights, and the rest ofthe banana-landbehaviors of monkey-dom corporations. It ismore than puttingwomen up at top, butputting femininitiesthere inside both menand women steeped inmale ways and views,without knowing it.Stanford feminizedknowing and beatHarvard and MIT solidly.

We Change History withFeminizations of ThingsIn the 1990s softwarepeople (men mostly)went around and re-engineered processes(uniting separatedcomputer systems in ahorizontal Japan-sylesupport of processes).In the end, all this malecomputer workmanaged to vastlyfeminize work processes—what was analytic wasmade synthetic, whatwas hierarchic wasmade egalitarian, whatwas central controlbecame locallynegotiated. Technologythat work “feminized”processes.

YOUR DOOR TO CREATIVITY---2 DAY COURSE----AT BEIJING UNIVERSITY 19 & 20 OCTOBER 2013----Copyright 2013 by Richard Tabor Greene, All Rights Reserved, US Government Registered PAGE 14LANDSCAPES OF DESIGN: BRAINSCAPES, SOCIALSCAPES, SYSTEMSCAPES, CULTURESCAPES

BRAINSCAPE--POLAR PAIRS OF MIND TYPES (MODULE SETS) 1. The Fast Emotive Mind versus the Slower (Editing) Conscious Mind--emotional signals set our minds in a frame that determines what our conscious minds notice and react to---

we seldom freely choose what we notice2. The Calculative Symbolic Mind versus the Associative Mind--we build formal models, but actually operate unconsciously using vague plural deep similarities, metaphors, asso-

ciations3. The Raw Input Mind versus the Indexed Input Mind--autistic savants see without indexing so see non-objects, pure percepts, so they can draw exactly--most of us never directly

see our seeings, instead we see indexed version, edited version. 4. The Male Mind versus the Female Mind--most of what is around us is overly male, solution moves things toward femininity5. The Engaged Mind versus the Detached Mind--when you attain satori in zazen, you sense THAT over there is exactly me here, no obstruction (mu ge), and instantly a flood of

compassion for beings around the world now torturing themselves with ego-drives to be separate, distinct. Engagement is bought by leaving compassion behind,engagement is sheer psychopathologic focus (Jobs, Bill Gates, Zuckerberg)

6. The “Be” Mind versus the “Have” Mind--Kegan stages of psychologic growth, learning to have what we used to be, wielding our contents instead of our contents wielding us7. The Explore Mind versus the Exploit Mind--conflicting desire to stay open and experience more versus desire to use what we know via focus8. The Conform Mind versus the Rebel Mind--we do not want or respect clubs that accept us as members, we want to belong while maintaining our own uniquenesses9. The Pour Soi Mind versus the En Sor Mind--Sartre, we are conscious beings (pour soi) wanting the permance and security of inanimate things (en soi)10. The Faith Mind versus the Evidence Mind--moms give us faith in what reality is that fails till we learn to supplant faith with experiment and evidence11. The Object Mind versus the Frame Mind--what we objects we identify and notice utterly depend on what frameworks are mobilized inside us, and the objects promote subse-

quent later mobilization of certain frameworks not others12. The Topic Mind versus the Comment Mind--thought always is something noticed and reactions to it13. The Distributed Rep Mind versus the Gramma Neuron Mind--most of what a concept is, in our minds, is layer after layer of association and context but there is also very specific

neurons for particular concepts like “grandma”14. The Propositional Mind versus the Analogy Mind--we maintain statements of what is true in experience and situations and we notice patterns and relations that, though not

strictly true, by analogy have meaning and implications for us15. The Declarative Mind versus the Procedural Mind--we remember facts about things and we remember how and why we did things certain ways and convert one to the other16. The Domain General Mind versus the Domain Specific Mind--we think amateurishly about nearly everything except for tiny domains where our thinking is disciplined, made

modest by data, that is, professional17. Left Brain (defenses; forcing unity) versus Right Brain (realities: discrepancy detector)--we build models and collect flaws and exceptions to them both18. The Conscious Mind versus the Unconscious Mind = Meta-Representation Mind versus Primary Representation Mind; language as needing/enabling reporting internal states to

others/self = consciousness; modeling others’ minds led to modeling own mind--we notice things in experience streams and we notice that and what we are noticing,both

19. The Prototype Mind versus the Exemplar Mind--we have models that define what is named one way and another, and we have examples that suggest quickly what should beexamined for such naming

20. The Inference Mind versus the Ecology Mind--we have various kinds of noticing and implications of them in our minds and we have various dialog-ing brain modules in a kindof mental module ecosystem of constant conversation

21. The Low Level Implicit versus the High Level Explicit Mind; Controlled Attention vs. Immediate Representation Mind--we have percept and percept processing hardware thatbring noticings to an indexing/editing layer that interprets those noticings as objects in worlds we know and can operate on

22. The Rational versus Irrational Mind (Kahneman etc)--there are rational operations we can consistently perform punctuated by errors we always make that prevent us beingentirely rational

23. The Rule Mind versus Model Mind--we can enact any of various rules to predict and handle futures or we can apply coherent models and work out implications from them.24. The Cognition Mind versus the Meta-Cognition Mind--we think and we can think about how we think and adjust how we think25. The Gene Mind versus the Environment Mind--we are born with percept and concept hardware processes, mostly factor analysis--what changes when we change X--we observe

kids doing such experiments--AND we modify what those co-occurrence noticings build (grammar for one thing) with models of who and where and what we areenabling manipulation of worlds we find or build

26. The Culture Mind versus the Non-Culture Mind--part of us is beyond culture capabilities of percept and thought, and part of us is Initial Factory Setting taught us unconsciouslywhile growing up somewhere and sometime--few of us become fully conscious of those Initial Factory Setting and change them--those few who do are called “adults”(generally 55 years old or older)

27. The Like Module versus the Interest Module--what interests us we dislike and fear, what we like bores us.

EXAMPLE: DESIGN FOR BRAINSCAPES--MIND TYPESFAST EMOTIVE VERSUS SLOW CONCEPT BRAIN

WHAT INTERESTS US WE DISLIKE (Like vs. Interest Module)

LEFT BRAIN (FORCING UNITY) VS. RIGHT BRAIN (DETECT DESCREPANCIES

MEN’S FASHIONS---VISUAL DOMINANCE Kimono SportFormals1. Dominate any room visually in instants---requires impact on fast emotive

brain = from visual departures from norms to strong initial emotions that travel

fast through crowds

2. Do not approach others, let them, after an hour and drinks approach me.

What interests us we fear and dislike and need to investigate from safe dis-

tances. Once familiar after approaching me, they dare LIKE what interested

them at first sight.

3. Designs that violate norms, and are instantly seen as highly descrepant, chal-

lenge and frustate the part of the brain wanting unity--creator/designer ID

allows unusual designs to be assimilated to professions and professional norms.

RESULT--fashions plus waiting for approach plus designer/creator ID = LIKED

DOMINANT INTERESTING designs.

RARE and REBELLION--Game Design--Beat the Boss (Group play match boss icon-monster with appropos gross noises)Optimize design for play fun or for instant comment/reaction to boss actions--

from fast emotive versus slow concept mind.

Optimize design for turning familiars sufferred into disallowed but real com-

ments and reactions to those familiars--from what interests us we dislike and

what we like bores us)

Optimize design for rewarding specific capture of boss pomposities and arro-

gances rather than for general abuse and personal anger--accuracy over effu-

sion--from forcing unity versus descrepancy detecting mind.

THE FAIL-URE OFTHEWORLD’STOP TENCOL-LEGES--FIXINGFLAWS INTHOUGHTS OFELITES

THE TOOL ILLUSIONGradual Supplanting of Motive and Love with Analysis & Cal-

culation= Never Finding Your Own

Loves and Creativities

1) top colleges are too male = monkey hierarchy, status sup-plants thought too often = they call maths ìhardî but maths are kid stuff, not hard, peo-ple & minds are ìhardî2) every year beyond 2 at college makes you and me LESS creative = we learn excuses & fitting in; 3) unbearable FOCUS builds the futureóGates, Jobs, Zuckerberg =Asperger's = no prep at univ. for venture intensity4) lack of anything you LOVE enough to sacrifice ALL for it NOW = you will fail to build = to be NOT harmful (= great) a college has to teach LOVE (of ideas & persons) and FRIENDSHIPS;

ANCIENT WRONG ORGANIZATIONS & FORMATS OF KNOWLEDGE

Generating people and professions too narrow for our cross-national, cross-cultural, cross-

technology problems = Success by Solving Pet-tier & Pettier Pieces Till Disaster Overwhelms

AllNot “divide and conquer” But “divide and fal-

ter”

5) BECAUSE colleges have the wrong department structure (not the 15 SCIENCES on this page) = they and the way they divide up & use knowl-edge are too OLD, too slow to CHANGE6) THEREFORE your mind has the wrong knowledge structure = so you miss the NEW

7) schools & COLLEGE educated your brain not what makes you intelligent = your extended mind, tools outside that make you smart: your personal library, files, network are un-educated = you are NOT smart though you are old-criteria ìsmartî8) you have trained your brain badly by using ancient interfaces: = too bushy from prose, = too unstruc-

tured from usual meetings, = too altercative from usual discussions, = too nar-

row-biased-uncomprehensive-unimagina -tive from bushy web and prose pages, that is, a

LACK of diverse comprehensive models9) (from the MIT Caltech Stanford Harvard people I

hired at Xerox): your cognitive list limit is too smallóyou apply mental operators to 6

to 8 ideas not 50 to 150 at one time;10) you are EXPERT at what schools teach: = sit-ting; obeying, remembering; not using: not challenging; not creating, making trash(=homework); = YOU MUST CHANGE

DESCARTIAN “MERE IDEA” MINDS “Professional Objectivity

Stance” Allows Evil to Look Elite and Okay--Unable to Judge or Design or Create for Lack of

Love, Motive, Emotion= Key Brain Modules Undevel-

oped at by Colleges

11) your intelligence is your surface area (exposure to flows) not your stored volume12) people with more theories, more abstract ones, NOTICE MORE,act diversely, = live in a bigger world13) Engineers & MBAs both lack social skill and emotion management = they design/make unsocial social web products that rapidly decay (facebook now) = NEED social sciences + arts = FULL DESIGN 14) Procedural literacy not mere ver-bal literacy or media literacy =han-dling power, money, love, ambitions, loss, appetites, failure, emotion, rela-tionshi

Copyright 2015 by Richard Tabor Greene [email protected] PAGE 2

THINKING DESIGN--The 1st 100 Approaches (vol. 1 of 4) by Richard Tabor Greene

1. muge “no obstruction” design PAGE 35

2. plural creativity sciences embodiment design PAGE 43

3. transparency totalization design (TQM) PAGE 48

4. social computation social connectionism (Japan) PAGE 54

5. Japanese aesthetics, Taoist aesthetics PAGE 62

6. stewing new techs design PAGE 70

7. knowledge re-organizations design PAGE 74

8. reality & imagination sciences design PAGE 85

9. yin negation of yang design (Taoism) PAGE 91

10. computation dialog design (social computational designs, versiontwo)

PAGE 113

11. totalization of bodies of knowledge design 121 totalize bodies of knowledge 147 computational sociality 179 globalizations of bodies of knowledge

PAGE 119

12. being in a world designs PAGE 192

13. designs fixing dysfunctions of belief PAGE 223

14. designs fixing us-them exaggerated into dangerous devaluing ofdiffering others

PAGE 233

15. designs opening of new routes to current awe and ecstasies by glo-bal new forms, rites, symbols

PAGE 248

16. selves are designs are cultures are high performances PAGE 258

17. dynamics of making and managing a self PAGE 295

18. everyone a designer: of self, life, career, family relations 359 designing evolving entities 401 challenges to being a self 443 career design

PAGE 358

19. designing for selves PAGE 480

20. designs as expressions of self PAGE 535

21. selves as expressions of design PAGE 555

22. “design thinking” a la Cross, Martin, Expert-ology PAGE 587

23. organizational learning dynamics & knowledge tipping pointsdesign

PAGE 605

24. geometries of thought design--thinking structurally PAGE 624

25. knowledge and idea dynamics design--waves, dialectics, evolution,natural selection, fractal structure, recursive loser inclusion in victorideas, recompilings across fields and knowledge formats, flow andhomes

PAGE 664

26. social & idea indexings for mind extending and mind extensionsdesign 749 kinds of space 767 kinds of reflection 780 theory power 839 persuasion 856 leadership design 891 intelligence unit 907 thought flaws 987 Western causality 1000 Asian causality 1006 story design 1031 storys = insight processes 1066 exercises

PAGE 746

27. REFERENCES PAGE 1068

Copyright 2015 by Richard Tabor Greene [email protected] PAGE 1

JOBS-APPLE

MANAGE SELF

EMERGINGCROSS ET AL

AI EXPERT DESIGN SYSTEMS

DESIGNLANDSCAPES

AVENUES

DESIGNED

INSIGHT PURE

INSIGHT IN 10 AREAS

INTERFACE NATURAL

SILICON VALLEY

PURPOSESBODIES OF

NEW SCIENCES

THEORIESSAVING SAVIORS

21 THINGS 21 WAYS

25 KINDS

ANTI-UN-DE-

NEW

SILICON

SYSTEMS

SECULARRELIGIONSUMMARY

PROBLEMATICS

DESIGNAVENUES

MAKE & USEAS INSIGHT

SELECTION

OF ALL ARTSKNOWLEDGE

OF DESIGN

DESIGN

AS ART

FIGHT

SCIENCE

PLURAL-

OF ITIES

INGSDESIGN-

DESIGN’S

INSIGHT

PROCESS

THREE

SCALESDESIGN-ING

KNOW

PERCEIVEDO

BASES

EVERY-WHEREDESIGN-INGS

DESIGN

TIME NEW SPACE

DESIGN OF

SYSTEMS

VALLEY ECSTASY

QUALITY

SOCIALITYTOTALI-

ZA-

&

TIONS

THERELIGIOSITIES

OF DESIGN

REDESIGN DESIGN (LKK)

DESIGNING

AS

UNDESIGNING

DESIGNAS

CULTURE WORK

SURVIVEIMPOSESUSTAIN

NEW KINDS OF SPACE

THEBUSHY-

DISEASESYSTEMS

GENRES OFSYSTEMS

CULTURE ROLE IN

SYSTEMS DESIGN

EVOLUTIONARYENGINEERING

SATISFYINGCUSTOMERS

CUSTOMER DESIGNED

POLICIES & DESIGNS

ASSESSMENT

DESIGNDRIVEN

ECSTATICGAME PLAY DESIGN

DESIGNING

OF SILICON VALLEY

80DIMENSIONS

OF SILICON VALLEY

COSMIC

GEOGRAPHIES

OF DESIGN

DESIGN

BUDDHISMS

DESIGN

SOCIAL

PHYSICS

INDUCTIVEDEDUCTIVEABDUCTIVE

DESIGN ASFIGHTS:

THINKINGDESIGN

REPLICATIONS

THINKING

Designs that Lead

by

DESIGNRichard Tabor Greene

Leading by Designing

Leading Design

Leading Designers

Japanese AestheticsSocial ComputationConnectionist SocialityMass Design AlgorithmsCrowd DesigningsBureau-Process-EventEvent-Process-BureauZen & Taoist DesignCongregational ScienceBIosense replacing

MechanosenseDialog Among Computation

System Types

160 Appoaches, Tools,Exercises for Design

PLURAL MODELS FOUNDATIONS--a design grammarNEW WHO for DESIGN--social design automataPLURAL CREATIVITY SCIENCES & MODELS --at singularities in design processesCULTURES DESIGNING & DESIGNED--the unconscious designerEMERGENT DESIGN CONSTRAINTS--types of engagement, sociality, spaces, art aims, computation types, culturesEVENTS DESIGNING & DESIGNED--bureau to process to event and event to process to bureauLEADERS DESIGNING & DESIGNING LEADINGS--replacing fixed social classes as way to deliver directionality REPLACING ANCIENT MEDIA/INTERFACES--escaping harms/habits from prose, sitter-creating old onesQUALITY DESIGNS, DESIGNING QUALITIES--via totalizations of bodies of knowledgeDESIGNING TECH CLUSTERS--capturing the biologics and natural selection dynamics of Silicon ValleySYSTEMS DESIGNING AND DESIGNED--getting out of linear static educations, professions, toolsINDIVIDUAL MODELS OF ALL OF DESIGN--remapping it all to run on emergent new landscapes of design, thought, creativity, career, technology

CONTENTS:

54 Excellence Sciences, 30 Creativity Sciences,120 Creativity Models, 60 Innovation Models,64 Natural Selection Dynamics, Automata Designand Designing, Creativity Models as Design Grammar,Design Cultures and Design Of Cultures, Kinds ofSpace, Purposes of All Arts, Type of ComputationalSystem in Dialog, The Cultural Work of Innovating,Event Design and Design Events, Invent Events,Design of Alternatives to Leaders for Leading,Replacing Prose-Brainstorms-Meetings-Discussions,Totalizations of Bodies of Knowledge beyond Quality,Replicating Silicon Valley in Silicon Valley & Elsewhere,Non-Linear Systems Designing and Designed.

8 NEW LANDSCAPES OF

DESIGN-CREATING-LEADING

East Asian, Nordic, Santa Fe,Non-Western, Non-linear,Biologics, Evolutionary DESIGNGenres, Means, New Aims





DESIGNS THAT LEAD

120 Creativity Models30 Creativity Sciences60 Innovation Models

Who Designs--AutomataHow Design--Creation Models

Design Limits--kinds of:engagement, sociality,

space, art impacts, com-putation types, cultures

Event Design--mass eventsLeadership Designs--various

function delivery meansSystem Design--non-linear

designings/designsMedia/Interface Design--via monastic level inno-

vationsQuality Designing--viatotalizing bodies of

knowledgeTech Cluster Design--via complete maps of

Silicon Valley dynamics

DESIGNS THAT LEAD

By Richard Tabor Greeneemail: [email protected]

amazon.com/author/richard-tabor-greeneCopyright 2015 by RTGreene, Rights Reserved

designs that lead buy at amazon excerpt--200 new landscapes changing who designs and what.pdf

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