1

Emergence Much Coming From Little

Walid ElSayed MSc in Architecture: Computing & Design 10’11

School of Architecture and the Visual Arts/AVA

University of East London

A Critical Analysis of: Emergence From Chaos to Order, John H Holland.

January 2011

2

Abstract. In the absence of an established scientific Theory of emergence, John Holland in his

book employs science in exploring the emergence phenomena. This essay aims to follow

Holland’s analysis and chain of reasoning of his scientific proposal, in doing so the essay begins

by focusing on the general concepts which Holland has adapted as major themes of his study,

expanding by following his steps in exploring the concepts, and concludes by offering a

recapitulation of the core of his study of emergence and a brief illustration of the application of

emergence within architectural practice.

Keywords

Emergence, Complexity, Cas, model, Mechanisms, Cgp,

reductionism.

Word count for Abstract: 94

Total word count (including list of illustrations & references, excluding

Abstract): 4493

Word count excluding list of illustrations, references and Abstract: 3842

3

Introduction

………………………………………………………………………….

Understanding Novelty and the evolution of systems has forever

represented an enormous challenge given their ‘complex’

nature. Systems evolution and ‘the rise of new features which

cannot be reduced to existing ones’ has led to a need for a

greater understanding of ‘Emergence’ Phenomena. Following

Alan Turing’s earlier steps in employing science and

Mathematics to explain natural phenomena that lay behind the

realm of science as it was believed at the time, John Holland, In

his book, attempts to base a scientific foundation For

emergence and to ‘search for Theorems that puts the study of

emergence squarely in the scientific domain’ (Holland 1998,

p.235).

Holland presents a significant direction towards the awareness of

the phenomena of emergence. Through scientific investigation

and intense case studies, he explores the conditions of

emergence in order to offer a better understanding of this

complex concept. Ideas about rules, complex systems, model

buildings, mechanisms and constrained procedures, provide a

basis for his Chain of scientific reasoning behind emergence.

Much coming from little. A Flag of Emergence.

…………………………………………………………………………

John Holland advocates Emergence phenomena as ‘a

ubiquitous feature of the world around us’ (Holland 1998, p.2).

Emergent Phenomena can be detected across a diverse

spectrum of systems such as neuron networks, swarms, and

chromosomes. These ‘complex’ systems are amongst those

identified as exhibiting emergent behavior. Accordingly the law

of nature seems to be emergence, which characterises the

complex systems around us. Thus, ’we will not understand life

and living organisms until we understand emergence’ (Holland

1998, p.1). [Fig1-Fig2]-[Fig 3]

Holland’s discussion of emergence is formulated from a complex

adaptive systems (cas) framework. He defines these systems as

‘….system[s] composed of interacting agents described in terms

of rules’ (Holland 1995, p.10), which suggest emergence to be a

property of these complex adaptive systems. This idea is further

reflected by Steven Johnson who describes systems that exhibit

emergence as complex adaptive systems.

Commencing by questioning the magnitude and density of

complexity, Holland successfully employs the notion of ‘much’

and ‘little’ as a starting point for introducing complexity as

stemming from emergence. He identifies emergence as a sense

of ‘much coming from little’ (Holland 1998, p.1). In support of his

^ [Fig 1]

Neurons

Purkinje nerve cell, SEM

^ [Fig 2]

Neurons

Colored scanning electron

micrograph (SEM) of nerve

cells.

4

argument he effectively turns ones attention to two different

arenas, firstly that of board games and Chess, where

‘Agreement on a few rules gives rise to extraordinarily complex

games’ (Holland 1998, p.1).

Secondly that of mathematics, such as Newton’s laws of gravity

in which any ‘move’ in the input of its defined rules reveals new

equations and mathematical statements. This is reminiscent of

‘much coming from little’ as are the complex adaptive systems

we are confronted with.

Complex systems and their global behavior is not just a sum of

the local behavior of its entities. Holland’s hypothesis proposes

that interactions within these complex systems are non-linear,

resounding Paul Cilliers’s perspective of complex systems ’… not

constituted merely by the sum of its components, but also by the

intricate relationships between these components’ (Cilliers 1998,

p.2). Similar traits can also be seen in the study of complex

systems by Harrington.

Further Comprehension of this concept is aided by drawing on

examples of Chess, ant colonies and the central nervous system

(CNS). It is proposed that the description of these emergent

systems entails the understanding of the relations and

interconnections between its constituents.

Holland formulated the theory of genetic algorithm and his

prominent publication in 1975 was a major achievement in this

field. His study aimed ‘to improve the understanding of natural

adaptation process, and to design artificial systems having

properties similar to natural systems’ (Goldberg D. 1988).

Holland equipped by his deep interest and expertise in processes

of natural evolution, successfully disputes the feasibility of

describing natural system such as an ant colony as only a group

of ants. An insightful exploration of their roles, interrelationships

and their collective behavior within their internal and external

changing environment would provide a more cohesive

description. He supports his argument by citing Hofstadter who

argued that the contemplation of an ant colony sheds lights on

the ample differentiation of the colony and its individual

inhabitants. The colonies reaction as a whole to the external

environment amazingly exceeds the capability of individual ants.

It reacts efficiently with all surrounding diverse conditions, lives for

years longer than its inhabitants and extends to considerably

larger areas.

The ant colony model aims to serve the notion of parts-to-whole

as campaigned by Hofstadter ‘a whole can be understood

completely if you understand its parts, and the nature of their

sum' (Hofstadter 1979, p.318) and this is further reiterated by

Holland.

^ [Fig 3.2]

Cracked Mud

Cracked mud in a dry desert

lake bed.

^ [Fig 3.3]

Branching

Branching Network emerge

from the differentiated pattern

of growth.

^ [Fig 3] Emergence Phenomena

^ [Fig 3.1]

Section through stem of a

geranium.

5

If we consider Smut’s prominent definition of ‘holism’ ‘The

tendency in nature to form wholes that are greater than the sum

of the parts’( Heylighen, F.; Cilliers, P.; and Gershenson, C. 2006), it could be said that this scheme of nature is at the core of

emergence.

In the case of non-living systems such as games, Chess cannot

be described only as a product of its components which

comprises of the Chess board and its pieces. The game is

defined by the rules, moves, the power structure, strategies and

the interactions between the different pieces to control the

board parts.

The interactions amongst a system’s entities ‘agents’ in

particular, describe the emergent behavior of the complex

adaptive system. These interactions are simply formulated by the

adaptive agents that continually adjust in response to other

similar adaptive agents in a changing environment.

The process of adaptation by the ‘agents’ is carried out by them

following a collection of simple rules. This process eventually

results in surprising outcomes. These outcomes can be seen as a

feature of complex temporal patterns which arise from the

bottom-up. [Fig 4]

The interactions have been described as ‘……coupled, context

dependent interactions. Technically these interactions, and the

resulting system, are nonlinear’ (Holland 1998, p.122). it appears

that This conception of dualistic non-linear interactions portrays

itself in terms of negative and positive feedbacks, and defines

the flow of interactions as feedback-loops. A similar exploration

of the parameters of Emergent systems was undertaken by

Johnson (Johnson 2002). Johnson has suggested emergence in

complex systems involves a mix of positive feedback loops that

pushes systems onward and negative feedback loops that

propel systems to reach an equilibrium point.

A phenomenon survives within boundaries.

…………………………………………………………………………

Holland emphasises on the significance of ‘rules’ in the

phenomenon and the non-chaotic nature of emergence. It is

pivotal to explore the interactions amongst a system’s

constituents in terms of the rules or laws. ‘When we observe

emergent phenomena, we ought therefore to try to discover the

rules that generate the phenomena’ (Holland 1998, p.188).

Holland’s proposal is supported by a similar argument by Steven

Johnson, in which Johnson claims that emergent systems

comprise of a multiple of agents who are dynamically

interacting in a number of different ways based on the local

rules they are exposed to. The ‘agents’ on their own are not

[Fig 4]

Three prints derived from the

MicroImage software, the

software is an exploration into

emergent form. Autonomous

software elements Interact

with their continually changing

environment to create a

kinetic field.

6

aware of any higher level centeralised rules or of the larger scale

impact, effect or consequences of their interactions.

Contrary to the widely accepted notion of emergence as a

phenomena associated with biological evolution, Holland in his

book goes further by considering rule-governed domains such as

games, systems with well-defined constituents and systems

defined by scientific theory (Newton’s theory of gravity). This

proposal reflects that emergence is not only limited to living

forms but extends further to these non-biological domains.

These systems have specifically been selected as the rules that

govern them can be identified easily. A profound scrutiny of

these systems draws on how very few rules or laws can generate

‘surprising complexity’ which have recognizable features and

are not just the product of random patterns.

Holland suggests ‘that the study of emergence is closely tied to

[the] ability to specify a large, complicated domain via a small

set of laws’(Holland 1998, p.123). If we take chess for example,

this ‘animated’ complex system can be said to be governed by

distinguishable simple rules. The dynamic change in this system

over time, which is the fluctuating arrangements of the pieces in

a game of chess means that these invariant rules practically

govern the variables. The dynamic variation in their

arrangements leads to unpredictable sophistication which yields

complexity and different patterns, nevertheless and more

importantly consistencies do appear within these patterns. The

same could be said in respect of Newton’s gravity laws, if we

consider the variation in the orbit of planets along their path in

galaxy systems.

On this ground it follows that in a way the rules or laws are the

generating force behind ‘complexity’, but it is the continual

change and possibilities of fluctuation in the patterns that result

in the creation of endless emergence. Due to the endless

possibilities of pattern formation, for example the possible

positions of pawns on a chess board means this emergence is

effectively perpetual in nature.

Accordingly an important aspect of the thesis Holland put

forwards in his book then is that a crucial key in resolving the

mystery of emergence comes from the process of extracting the

underlying governor rules that can be achieved by recognizing

the mutual features and patterns within the ever changing

complexity outcomes that he calls ‘regularities’. It follows

therefore that understanding emergence thoroughly involves

the process of extracting procedures which result in regularities

apart from any other attendant details, referred to as

‘modeling’.

Computer based models are used consistently as a tool in

Holland’s investigation of emergence as he suggests that ‘the

[Fig 5]

IF [Stimulus] Then [Response]

Clause Example.

7

computer is like an automated stove: once the recipe is inserted,

the delicacy described emerges’ (Holland 1998, p.17).

By exploring complexity in these models and eliminating the un-

salient details, we can realise a ‘repetition’ or ‘regularities’. this

set of observable regularities almost certainly necessitates an

observer.

These regularities enable observer to build a set of common

instructions ’subroutines’ in the form of ‘IF [ stimulus ] THEN [

response ]’. These are called ‘learning actions ‘which are at the

heart of the model building process. A ‘computer-based

realization’ of the rules and those ‘allowable interactions’

supports the recognition of the emergent patterns. [Fig 5]

Models, Mechanisms and Constrained generating

procedures (cgp). Understanding chaos through order

…………………………………………………………………………

Throughout the book Holland highlights the importance of

‘modeling’ and its subsequent use in the study of emergence.

‘The critical steps in Constructing a model are selection of salient

features and laws (generators and transition functions)

governing the model’s behavior’ (Holland 1998, p.224).

Arguably, although the ‘inability to anticipate’ is widely

accepted as an aspect of emergence phenomena, Holland

suggests that science is based on model construction and that

well-comprehended scientific models are crucial in the study of

emergence as they play a part in facilitating our predictions. In

support of his argument he has cited a definition taken from the

American heritage dictionary where it states that ‘A [model is] a

tentative ideational structure used as a testing device’. It has

been suggested that ‘models, above all, make anticipation and

prediction possible’ (Holland 1998, p.11).

In the case of games, Holland suggests an ‘observer who has an

omniscient view’ is still unable to predict the outcome of the

game and will still be in the same position as the individual

players due to the endless possibilities that the course of the

game may take. It follows that where the players are adapting

to each other emergence will always be present.

Holland’s attempts to structure the phenomena through

modeling methodology comes close to Peter Cariani’s outlook

of ‘The emergence relative to a model’ which ‘sees emergence

as the deviation of the behavior of a physical system from an

observer’s model of it’( Cariani 1989, p.779).

Adapting Modeling methodology raises the need to explore

emergence by describing these emergent systems in terms of

their elementary mechanisms. By using mechanisms as building

blocks an observer can construct a model that exhibits

emergence. The term ‘mechanism’ has been defined by

8

Holland as a set of agents, initial ‘states’ and rules that

determine and constrain the agents’ behavior.

His discussion takes account of the rules of neural systems,

checkers and other models and in doing so he lays out a

general formula that covers these amongst other models.

Interestingly, it is suggested that the state of a system at one time

together with its interaction with the external environment

enables the mechanisms and subsequently the system, to

determine the state of itself in subsequent stages. A process

known as ‘system evolving’.

Holland offers the term ‘Tree of moves’ to portray the notion of

system states. The root is the initial state of a system and the

branches lead to the states that can be attained from the lower

levels in the tree. The leaves are the ending states that

determine the outcome of the emergent process. Holland

illustrates how the ending state (leaves) grow so rapidly even

when the states at the lower level are simple.

Tracing paths in the tree of moves can define ‘strategies’ which

guide the emergent system behavior in any possible situation.

Repeated processes enable the system to define a feasible

strategy. [Fig 6]

The mechanisms as building blocks of models and their

interconnections, constrain the behavior and the possible

outcomes within the system. This constrained dynamic behavior

has been described by (Bonta, M. and protevi, J.2008) later as

‘focused systematic’ behavior which emergent structure

enables through constraining the action of its component parts.

The process of building the models from a collection of

mechanisms and the procedures defining their local

connections are referred to as ‘constrained generating

procedure ‘cgp’ - a hallmark of emergent systems.

drawing on the path of these ‘cgp’ which involve an

understanding of the functions and rules in term of the system’s

mechanism, Holland discusses how these mechanisms ‘respond

to actions (or information), processing that input to produce

resultant actions (or information) as output’ (Holland 1998,

p.126).

The simple lever is used as a working example of a simple ‘cgp’

in support of the notion. Under simple rules and functions which

form a simple mechanism the action of pulling down one end

(input) results in a generating force at the other end (output)

that is multiplied by the ratio of the lever arms. [Fig 7]

[Fig 7]

The Lever

Example of Simple mechanism

[Fig 6]

Tree of Moves

Part of a game tree for tic-tac-

toe.

9

It can be said that most models involve more than one

mechanism. Therefore it is important to understand the networks

that link mechanisms which Holland calls ‘networks’ or ‘cgp’s

that define the diverse emergent models and systems.

The Cgp’s framework as proposed by Holland reflects a fixed

connection within and between mechanisms which raise

questions on whether this framework is suitable to describe

emergence in mobile agents’ environments such as an ant

colony.

However, in response to this, Holland suggests that the cpg’s

framework allows a system to accommodate changes in its

geometry. A cgp can alter its connections and its collection of

mechanisms as long as they include a type of mechanism called

‘processors’. Thus, the cgp’s can alter their structure to reflect

the ever changing pattern of interaction in a process called self-

reproduction. Based on this notion, Holland introduces ‘genetic

algorithm’ and employs it into the cgp’s framework.

Significantly, Holland has provided a dynamic framework that

particularly describes the mechanisms as the building blocks of a

subassembly, which if combined with other similar subassemblies

form a level of complexity. This level governs what emerges at

the higher levels in nested hierarchies, where successively higher

levels of complexity require sequentially lower levels to emerge.

The emerged Macro-complexity increases proportionally with

the increase in Micro-mechanisms and networks.

An observer has to conclude the cgp’s that generate

emergence then discover the rules that govern the system

entities. This means that an observer practically needs to reduce

his complex observation. Holland thus puts forward reductionism

as an approach for a deeper understanding of the multitude of

emergence.

Reductionism.

It’s all in the eyes of the beholder

…………………………………………………………………………

Reductionism is based on the idea of reducing complex

phenomenon to their simplest constituents to gain a greater

understanding of the complexity. Holland In his thesis

acknowledges that to explore the multitude of Emergent

phenomena, reductionism should be considered as a

methodology. He points out that ‘emergence in rule-governed

systems comes close to being the obverse of reduction’(Holland

1998, p.8) which is in sync with the view of Goodenough and

Deacon that ‘By starting from wholes and moving down into

parts, one is moving in the opposite direction from which things

arise’ (Goodenough and Deacon 2006, p.854).

10

Holland’s ‘reductionistic’ approach which aims for a deeper

understanding of the phenomena by decomposing the

emergent system into mechanisms, networks and interactions

also has a mutual element with Kauffman (1971, cited in Wimsatt 2002) ‘A reductive explanation of a behavior or a property of a

system is one which shows it to be mechanistically explicable in

terms of the properties of and interactions among the parts of

the system’. The notion of reduction is based on the ground of

considering emergence as resultant of mechanisms and

interactions within boundaries and constraints, thus, any

emerged complexity can be reduced to these interactions.

Holland suggests that a complex phenomenon can be reduced

to simple laws. In demonstration of his notion he puts forward

that if we are to consider ‘reductionism’, on its inverse we will

need to add new levels (laws) to the simple description that

define the system. These new laws in the higher levels are

considered to be consequences of the fundamental laws in the

lower levels. An Understanding of this hierarchy of ‘levels of

definitions’ plays a key role in the understanding of emergence.

His hypothesis seeks to define the basic laws that describe the

lower-levels of a system which he calls ‘Microlaws’, in order to

understand the complex outcome in the higher levels that

generate the multitude of phenomena. An extension of this

methodology has been proposed by Steven Johnson in his study

of emergence which he describes as ‘The movement from low-

level rules to higher-level sophistication’ (Johnson 2002, p.18).

In search of a theory.

Recapitulation

…………………………………………………………………………

In the absence of an overarching theory, Holland attempts to

define criteria for emergence in which one can understand and

identify when encountered with this phenomenon.

By employing the concept of the ‘whole’ being more than the

‘sum’ of its part as a starting point for understanding emergence

it is implied that phenomena are to be understood in terms of

the interplay between the parts of the whole and not as a sum

of these parts. The generated complex system is formed through

the relations and interconnections between the system’s

constituents in non-linear interactions. In other words, the

emergent whole always goes beyond its parts qualitatively. For

example the coherence of a swarm of ants can never be traced

back to the behavior of a single ant.

Even though emergence is associated with chaos, Holland in his

studies has attempted to shed light on the order underpinning

this phenomenon. Emergence arises as a consequence of

11

relationships amongst the systems agents that interact under

simple un-centralised rules over time.

It follows that for a deep understanding of emergence,

observers should start by establishing system models as a

problem-solving and measuring device in their exploration of the

phenomenon.

By distinguishing the communal interaction between agents and

their mutual temporal consequences ‘regularities’ a model can

be constructed. An observer to the phenomenon seeks to

recognise the mechanisms, rules, interactions and procedures

that underpin these models.

The outcome possibilities of any interactions between the

mechanisms of these models are restricted by non-linear

interaction rules.

When considered within the context of constrained generating

procedures, changes in a systems environment result in the

mechanisms composing subassemblies within the micro level,

which if combined with other subassemblies form a level of

complexity. This level governs complexity arising at the higher

macro levels in a hierarchical manner which evolves and

provide systems with an ability to alter their geometry over time.

Emergence within Architecture practice

Architecture is learning.

…………………………………………………………………………..

Holland’s theory of emergence introduces a revolutionary

approach to exploring processes and systems involved in

creating forms and complexity through emergence. It focuses

on the processes behind the creation of a form rather than

seeing the form as determined. The form in light of the

emergence perspective is seen as a product of a

morphogenetic process.

In this context, parts-to-whole notion, feedbacks, genetic

algorithms and the form-finding processes formulates key marks

that seem to be crucial in understanding the application of

emergence in architecture.

Architectural practices have adopted differing attitudes and

approaches over the centuries in terms of the parts to whole

notion. If we consider the architects of the renaissance era we

see how they were predominantly influenced by the renaissance

itself and so the design process focused less on the building and

^[Fig 8]

The Micromultiple House

Design is based on a mass-

producible system

implemented as an

interconnected network of

small, flexible bands.

12

engineering side of things but rather on the decorative and

proportions element of design.

It follows that even if a ‘part’ is more specialized than the whole,

it is the whole that will lead to evolution of a system, not the

individual part in isolation. This leads us once again to

emergence which is based on the notion that the emergent

whole will always be qualitatively superior to its individual parts.

currently the architectural application of the Parts-to-whole

concept requires the recognition of building without a single

fixed form. Greg Lynn 2006, has suggested that ‘Architecture has

a responsibility to express parts-to-whole relationships and

hierarchy and not to propose buildings as seamless monolithic

hulking masses’. Currently the Parametric modeling approach

has been introduced in architecture to make accessible the

idea of parts-to- whole and several architectural designs are

now parametric designs that reflect parts-to-whole relations.

An extension to parts to whole concept which has been

adopted by Architectural firms is that of ‘system thinking’. A

‘system’ as defined by Austrian Biologist Ludwig von Bertalanffy is

‘an entity which maintains its existence through the mutual

interaction of its parts.’

Emergence is associated with ‘systems thinking’ in that the

interactions of individual parts or constituents of a system give

rise to properties which are not properties of the individual parts

if looking at them individually. Russ Ackoff, a prominent

campaigner of ‘systemic thinking’ believed that the

Architectural profession embraced this concept through the

design process, where the desired building is identified and then

working backwards the individual parts are identified.

The employment of genetic algorithms and mathematics as a

generator of emergent systems that have been discussed in

Holland’s book led to an establishment of solid ground for an

intense application of algorithms to Architecture that employ

mathematics to generate pattern based organization. This

digital revolution permits more control over a form’s geometry in

the morphogenetic design process.

Form-finding processes have been introduced within

architecture with early experiments by Feri Otto.

‘Bioconstructivism’- is a contemporary architectural tendency

which introduces a new paradigm that is infused with biological

aspirations and aims to engage the concept of emergence

within architectural practice. Tom Wiscombe, Greg Lynn, Karl

Chu and others, are currently combining biology into their

designs where a bias toward the emergence concept has led to

developing Architectural and structural experiments that adapt

to the demands of emergence.

[Fig 9.1]

^[Fig 9.2]

^[Fig 9]

Urban Beach (Roof Canopy)

Design aims to use a non-

hierarchical patterning of

small, interlaced units, or cells.

The position and geometry of

each cell was determined by

shading requirements, required

shear and moment reactions,

and also by the position and

behavior of neighbor cells.

13

List of illustrations

………………………………………………………………………………………………………….

Figure 1: Neurons:

Purkinje nerve cell, SEM

(Source: Mccarthy, D. SCIENCE PHOTO LIBRARY)

Figure 2: Neurons:

Coloured scanning electron micrograph (SEM) of nerve cells.

(Source: Gschmeissner, S., SCIENCE PHOTO LIBRARY)

Figure 3.1: Section through stem of a geranium which reveal geometrical

Arrangement of the bundles of differentiated vessels and cells

Which produces complex structure capable of differential

movement. All the cells have a structural Role and structure

capacity emerges from their interactions.

(Source : Emergence. Morphogenetic design strategies.

Architectural design Magazine)

Figure 3.2: Cracked Mud:

cracked mud in a dry desert lake bed,Rosamund Lake,

California, USA

(Source: Ravenswaay, D.V., SCIENCE PHOTO LIBRARY)

Figure 3.3: Branching Phenomena:

Branching Network emerge from the differentiated pattern of

growth.

(Source: Pedrazzini, C. SCIENCE PHOTO LIBRARY)

Figure 4: Rules-governed emergence:

Three prints derived from the MicroImage software, the software is

an exploration into emergent form. Autonomous software

elements Interact with their continually changing environment to

create a kinetic field.

(Source : Programming Cultures. Art and Architecture in the Age

of Software. Architectural design , Vol 76 No 4)

Figure 5: If[ ] Then[ ] Clause

Illustration of subroutines in ant colony that function as a learning

action.

(Source : Holland, J. 1998, Emergence: From Chaos to Order)

Figure 6: Tree of Moves

Part of the tree game for tic-tac-toe.

(Source : Holland, J. 1998, Emergence: From Chaos to Order)

Figure 7: The Lever

used by Holland, J. as an example of simple mechanism

(Source : Holland, J. 1998, Emergence: From Chaos to Order)

Figure 8: The Micromultiple House

Design is based on a mass-producible system implemented as an

interconnected network of small, flexible bands.

(Source : Emergent – Tom Wiscombe)

Figure 9: Urban Beach

Design aims to use a non-hierarchical patterning of small,

interlaced units, or cells. The position and geometry of each cell

was determined by shading requirements, required shear and

moment reactions, and also by the position and behavior of

neighbor cells.

(Source : Emergent – Tom Wiscombe)

14

References

………………………………………………………………………………………………………….

Holland, J. 1998, Emergence: From Chaos to Order. Oxford University Press, Oxford.

Johnson, S. 2002, Emergence: The connected Lives of Ants, Brains, Cities and Software. Scribner, NY.

Holland, J. 1995, Hidden order: How adaptation builds complexity. Helix Books.

Cariani, P. 1989, Emergence and Artificial Life. Artificial Life II: vol. X, eds. Langton, et al, Addison-Wesley,

Redwood City, CA, pp. 775-797.

Hofstadter, D. R. 1979, Gödel, Escher, Bach: AN ETERNAL GOLDEN BRAID. Vintage Books, NY.

Bonta, M.,; Protevi, J. 2006, Deleuze and Geophilosophy. Edinburgh University Press, Edinburgh.

Heylighen, F.; Cilliers, P.; and Gershenson, C. 2006, Complexity and Philosophy (online)

http://uk.arxiv.org/abs/cs.CC/0604072 [Accessed 5 Jan. 2011]

Goodenough, U.,;Deacon T. W. 2006, the Sacred Emergence of Nature. In The Oxford Handbook of

Religion and Science. Oxford University Press, Oxford.

Goldstein, J. 1999, Emergence as a Construct: History and Issues. Emergence: Complexity and Organization

1 (1), pp. 49–72.(online).

http://emergentpublications.com/eco/ECO_papers/Issue1_1_3.pdf [Accessed 21 Dec. 2010]

Cilliers, P. 1998, Complexity and Postmodernism. Understanding complex systems.

Routledge, London.

Kauffman, S. A. 1972, Articulation of Parts Explanation in Biology and the Rational Search for Them, In

PSA1970, eds. R. C. Buck and R. S. Cohen, Boston Studies in the Philosophy of Science, 8, pp. 257-272.

Bonta, M.,; Protevi, J. 2006, Deleuze and Geophilosophy. Edinburgh University Press, Edinburgh.

Wienstock, M. 2004 Morphogenesis and the mathematics of emergence. In Architectural design

Emergence: Morphogenetic design and strategies. 74(3) , pp. 11-17

Chu, K. 2006 Metaphysics of Genetic Architecture and Computation. In Architectural design Programming

Cultures: Art and Architecture in the Age of Software. 76(4) , pp. 38-45

Chu, K (2006) Metaphysics of Genetic Architecture and Computation. In AD Programming Cultures: Art and

Architecture in the Age of Software. 76(4) : 38-45

Wiscombe T. , Emergent Models of Architectural Practice, Yale Perspecta (online)

http://www.emergenttomwiscombellc.com/pdfs/YalePerspecta.pdf[Accessed 02 Jan. 2011]

Wiscombe T. , Emergent processes , OZ Journal (online)

http://www.emergenttomwiscombellc.com/pdfs/OZJournal.pdf [Accessed 02 Jan. 2011]

Emergence (online).

http://en.wikipedia.org/wiki/Emergence [Accessed 21 Dec. 2010]

Reductionism (online).

http://en.wikipedia.org/wiki/Reductionism [Accessed 23 Dec. 2010]