the exploration of aa new research paradigm
TRANSCRIPT
A quantum interp
The exploration of aThe exploration of aPhD/Res
Walter Baets, PhD, HDRAssociate Dean for ResearchMBA DirectorProfessor of Complexity , Knowledge and InnovatEuromed Marseille – Ecole de Management
retation of business
a new research paradigma new research paradigmsearch seminar
tion
Flatland: Edwin Abbo
A. Square meetq
tt, 1884
ts the third dimension
Wanderer your fWanderer, your fthe path, and notWanderer, thereit is created as yyBy walking,you make the patyou make the patand when you look
th th you see the path will not be trod aWanderer, therebut the ripples onbut the ripples onAntonio Machado,Chant XXIX Proverbios y cantChant XXIX Proverbios y cantCampos de Castilla, 1917
footprints arefootprints arething more;e is no path,you walk.y
th before youth before you,k behind hi h ft which after youagain.e is no path,n the waters.n the waters.
tarestares,
Taylor’s view on th
The computer: attempt to
Manipulating symbols
Represent the world
Intelligence = problem solving
0-1 Logic and mathematics
Rationalist, reductionist
Became the way of buildiBecame the way of loBecame the way of lo
he brain
automate human thinking
Modeling the brain
Simulate interaction of neurons
g Intelligence = learning
Approximations, statistics
Idealized, holistic
ing computersooking at mindsooking at minds
DefinitionDefinitionE i lEpistemologyViews about the nature, tk l d ( h k knowledge (what makes t
OntologyPhilosophical investigation
h 1. What means ‘being’2. What exists l i h hilAn ontology is what philos
The ontology of a theory f th t b tfor a theory to be true
ns ns
the sources and the limits of b l f k l d )true beliefs into knowledge)
n of existence or being
h k isophers take to existis the things that have to existe
The essence
Pi t i ithi it Pictures science within its cthe absolute)
Provides a framework that apist m l ic l l ncepistemological relevance
(Philosophy of) science is of(Philosophy of) science is ofhistory (other than philoown logic)own logic)
of science
t f k ( t icontemporary framework (not in
allows judgement about the f th ( pplic ti n)e of a theory (or application)
ften embedded in sociology andften embedded in sociology andosophy that often develops its
My taxonomy of phil
Historical embeddingOrigin
PhilosotheoOrigin theo
Logical po(Wiene(Wiene
Critical ra(Pop
K h ’ dPhilosophy Kuhn’s paradLakatos
Symbolic int
Philosophy
yCritical t
ArchitectureArts
U f l it i
Feyerabend’sPostmoder
(Derida,Usefulness as a criteria (Derida, Foucault,
losophy of science
ophicalories
Designconsequencesories consequences
ositivism r Kreis)r Kreis)
ationalismpper)di th
DeductionInductionEmpiricismdigm theory
s theoryteractionism
EmpiricismHypotheses testingQualitative research
theories
Design paradigms chaostheoryrn theoriesApostel,
Design paradigm(van Aken)
Social construction oflitApostel,
Deleuze) realityDesign norms
My taxonomy of pMy taxonomy of pof science/2
Historical embeddingOrigin
PhilosotheoOrigin theo
R di l
Neurobiology
Radical con(MaturanaAutopoiesip
Self-referen
CognitiveCognitiveArtificial
Intelligence
Paradigm(Frankli
hilosophy hilosophy
ophicalories
Designconsequencesories consequences
t ti i Dynamic re-creationnstructivism, Mingers)is (Varela)
Dynamic re creationThe emergence ofobject and subjectLocal (contextual)( )
nce (Gödel) Local (contextual)validity
m of mindn, Kim)
Adaptive systemsImplicit learning
The pre-history ofp yof science
Pre-Cartesian/Pre-GalileChurch is the seat of scieS i i t t fi Science exists to confirm Science is the ‘common seIn fact it is holisticIn fact it is holistic
17th to the 19th centuryI think,therefor I amExperimentationThe role of the researcheThe role of the researche
(yet) questionedAbsolute Newtonian frame
concept)MeasurabilityTh d f h li ti thi kiThe end of holistic thinkin(Did science replace religi
philosophyp p y
an period (before 17th century)nce li i religion
ense’
y
er as involved subject was noter as involved subject was not
ework (absolute time and space( p
i ing in scienceon and became one ?)
Th 20 h The 20th ce
Breakthrough of relativity(objective measurementand quantum mechanics
Comparing the validity of tEinstein) needs differe
1931 G d l’ h (1931: Gödel’s theorem (genreasoning can no longer
Box of PaBox of Pa
ntury
y theory (Einstein)t can no longer be claimed)(it is all interpretation)
theories (e.g. Lorentz versus ent methods
l l d f b l neral validity of symbolic r be claimed)
andoraandora
lf d Self-producing syradical constructiradical constructi
Maturana, Varela, Gödel, Min
Biological principle of self-pr= Autopoeisis
Has been interpreted a lot byHas been interpreted a lot by
In opposition to the focus onppVarela pick out the sian amoebae) as the ce
Individual autonomy, self-defwithin an organismwithin an organism
ystems, autopoiesisivismivism
gers
roducing systems
y different fields differentlyy different fields, differently
n species and genes, Maturana andp g ,ingle, biological individual (e.g.entral example of a living system
fined entities
Philosophical implPhilosophical implautopoiesisp
Epistemological and ontologEpistemological and ontolog
It constitutes a theory aboy
It implies there is no claim
Beliefs and theories are pu‘constitute’ rather tconstitute rather t
constructivi
‘B l f ’ ( )‘Biology of cognition’ (1970)the system in whichtakes placetakes place
ications ofications of
gical presuppositionsgical presuppositions
out the observer
to objectivity
urely human constructs whichthan reflect realitythan reflect reality
vism
) b ): observer is h description
IKen Wilber: A Brief HistoryThe concept of a holon (pI
Interior-IndividualIntentional
p (p
IntentionalWorld of: sensation, impulses,
emotion, concepts, vision
Truthfulness
J tJustness
Interior-collective EWorld of: magic, mythic, values
WE
Interior-collectiveCulturalWE
ITy of Everythingart/whole) IT
Exterior-IndividualBehavioral
/ )
BehavioralWorld of: atoms, molecules, neuronal
organisms, neocortex
Truth
Functional fit
World of: societies, division of labour, f ili t ib ti / t t
Functional fit
Exterior-Collective
groups, families, tribes, nation/state,agrarian, industrial and informational
Social
ITS
Euromedian Manage
Individ
PersonalPersonal
•Personal development•Emotional development•Leadership•Making a difference PersonalPersonal
DevelopmentDevelopment(Learner centered)(Learner centered)
•Self motivation•Joy•Involvement•Responsibility
•Historic legitimacy
•Respect
Interior
EuroEuro--Mediterranean Mediterranean b li f lb li f l
Historic legitimacy•Diversity•Sociology•Humanism•Relativism beliefs, valuesbeliefs, values
& culture& culture(identity)(identity)
Relativism•Complexity •Social responsibility•Euro-Mediterranean(long term perspective) ( y)( y)(long term perspective)
•Sustainable development CollecNetwo
ement Approach
dual
MechanisticMechanistic
•Quantitative approaches•Control/performance•Management byobjectives
managementmanagementapproachapproach
•Models•Financial orientation•Short term efficiency•Production managementg
•Dynamic system behavior•Management in complexity
Exterior
SystemicSystemicmanagement management
g p y•Management in diversity•Knowledge management•Community of practices•Ecological managementmanagement management
approachapproachg g
•Ethics in management
•Social corporate responsibility•Sustainable development•The networked economy•Emergence, innovation…
ctive/orked
Sometimes small differe
conditions generate very
in the final phenomena.
former could produce a
the latter.
Prediction becomes impo
accidental phenomena.
PP
ences in the initial
y large differences
A slight error in the
tremendous error in
ossible; we have
i é i 1903oincaré in 1903
Mathematical complexity
Sensitivity to initial
X * XXn+1 = a * Xn
0.294 1.4 0.3
conditions (Lorenz)
* (1 X )n * (1 - Xn)
3 0.7
Cobweb Diagrams (AttCobweb Diagrams (Att
Xn+1 = μ * Xn *
dX / dt = μ X (1 -μ (
On the diagrams• Parabolic curveDi l li • Diagonal line
• Line connecting
tractors/Period Doubling)tractors/Period Doubling)
(1 - Xn) (stepfunction)
- X) (continuous function)) ( )
s one gets:eX XXn+1 = Xng iterations
Lorenz curve (But
L (1964) fi ll bl Lorenz (1964) was finally able
Lorenz weather forecasting mo
dX / dt = B ( Y - X )
dY / dt = - XZ + rX - Y
dZ / dt = XY - bZ
tterfly effect)
t t i li P i é’ l i to materialize Poincaré’s claim
odel
Fractals (MandFractals (MandSelf-similarity on different levf y ff
CoastlineC d FlCody FlowerBranches of a tree
Those forms cannot be reduce(Mandelbrot)
It is a set of attractors (gingeequations
Julia set: Z → Z 2 + C (Cequations
Dependence on starting va
Mandelbrot set is a fract
delbrot set)delbrot set)vels of detailf
ed to any geometrical figure
erbread-man) for a set of different
C is constant; Z is complex)
alues of z
tal (needs a computer)
h h Why can chaos n
• Social systems areSocial systems arenon-linear
• Measurement can Measurement can
M i l• Management is alwapproximation oapproximation ophenomenon
b d d not be avoided ?
e always dynamic and e always dynamic and
never be correctnever be correct
di i ways a discontinuous of a continuous of a continuous
Complexity iin physics
Il P i iIlya Prigogine
• Non-linear dynamic mperiod doublingperiod doubling,….
• Irreversibility of tim• Irreversibility of tim
• The constructive roleThe constructive role
• Behavior far away froBehavior far away fro
• A complex system = cA complex system = c
• Knowledge is built froKnowledge is built frobottom up
models (initial state, ).)
me principleme principle
e of timee of time
om equilibrium (entropy)om equilibrium (entropy)
chaos + orderchaos + order
om the om the
Entropy
M sur f r th m unt f dMeasure for the amount of d
When entropy is 0, no furtheWhen entropy is 0, no furthe(interpretation is that no info
h There is a maximum entropy diagram, this is 4)
Connection between statisticaentropy to a chaotic system py yassociated statistical system
dis rd rdisorder
er information is necessaryer information is necessaryormation is missing
h ( h b fin each system (in the bifurcation
al mechanics and chaos is applying in order to compare with anp
Biological complexity
Francesco VarelaFrancesco Varela
• Self-creation and selsystems and structuy
• Organization as a neu• The embodied mind• Enacted cognition• Subject-object divisj j• How do artificial netw• Morphic fields and mp
(Sheldrake)
lf-organization of ures (autopoièse)( p )ural network
ion is clearly artificialyworks operate (Holland)
morphic resonance p
Self-producing systp g yradical constructivi
Maturana, Varela, Gödel, M
Biological principle of self= Autopoeisis Autopoeisis
Has been interpreted a lo
In opposition to the focusand Varela pick outpindividual (e.g. an aof a living system.g y
Individual autonomy, self-y,entities within an o
tems, autopoiesispsm, self-reference
Mingers
f-producing systems
ot by different fields, differently
s on species and genes, Maturana t the single, biological g , gmoebae) as the central example
-defined organism.
Living systems operate in aLiving systems operate in aThe overall behaviorpurely by the componpurely by the compon
Observers are external to Observers are external to perceive both an entComponents within aComponents within ato other components
Any explanation of living syhaving no recourse thaving no recourse tfunctions.
Living systems are autopoie(self-producing) circ(self producing) circself-referring organ
an essentially mechanistic way an essentially mechanistic way. r of the whole is generated nents and its interactionsnents and its interactions.
the system Observers the system. Observers tity and its environment. n entity act purely in response n entity act purely in response s.
ystems should be nonteleological,o idea of purpose goal ends and o idea of purpose, goal, ends and
etic cular cular, ization
I li ti f Implications of au
Pl h l ’ l Plus ça change, plus c’est la Organizational closure (imm
i l t )social system).Structural determinism.D i t i t t Dynamic systems interact w
their structure.I t ( t b ti ) d Inputs (perturbations) and Structural coupling = adapta
d s n t sp if th does not specify the Self-production was not onl
biological systems (cobiological systems (cogenerated models; huorganizations law)organizations, law)Law as an autopoietic
t i itopoiesis
ê h même chose.mune system, nervous system,
ith th i t th hwith the environment through
t t ( ti )outputs (compensations).ation where the environment d pti h n s th t ill adaptive changes that will occur.ly specified for omputeromputeruman
c system (Teubner)
Philosophical implicaPhilosophical implica
E i l i l d lEpistemological and ontolog
It tit t th bIt constitutes a theory abo
It i li th i l iIt implies there is no claim
B li f d th i Beliefs and theories are pu‘constitute’ rather th
nst ti ismconstructivism
‘Biology of cognition’ (1970)Biology of cognition (1970)the system in which takes placetakes place.
ations of autopoiesisations of autopoiesis
i l i igical presuppositions.
t th bout the observer.
t bj ti it to objectivity.
l h t t hi hurely human constructs whichhan reflect realitymm.
): observer is ): observer is description
Ontology of autopoOntology of autopo
Perceptions and experiencesby our bodies and nervby our bodies and nerv
Therefor it is impossible forTherefor it is impossible forthat is a pure descriptof ourselves.of ourselves.
Experience always reflects tp y f
There is no object of our knjby the observer.
oiesisoiesis
s occur through and are mediatedvous systemsvous systems.
r us to generate a descriptionr us to generate a descriptiontion of reality, independent
the observer.
owledge, it is distinguishedg , g
Rupert Sheldrake (Rupert Sheldrake (
They are self-organised “cy g
They have a time and space/ h f btime/space schemas of vib
from interaction);
They attract the systems characteristic forms or mocharacteristic forms or morealisation of these activitthese activities. The goals where these activities are are called he attractors;
(morphogenetic fields)(morphogenetic fields)
collections” or “collectivities”;;
e aspect and they organise from b ( ) ( d h f brations (energy) (and therefore
under their influence towards odels. They organise the odels. They organise the ties and preserve the integrity of or the places attracted
Rupert Sheldrake (mThe morphic fields are putwhich are themselves entiinclude other morphic fielhierarchy) or holarchy. Th
t f shi ; emergent fashion;
They are structures of prThey are structures of practivity is probabilistic;
They include a so-called clresonance with its own pas
ith blresonance with comparablesystems. This memory is cAs more models repeat thAs more models repeat thbecome more normal.
morphogenetic fields) 2t in relationship with holons (units re). The morphic fields therefore ds in a climbing hierarchy (nested
hese holarchies are created in an
robability and also their organising robability and also their organising
losed memory, formed by self-st and morphic
t i e anterior cumulative. emselves they emselves they
Paradigm oaradigm oWhat are t
Based on cognitive ar
The mind and the sou
Behaviorism: mind asexperimentalisexperimentalisbehavior is wh
Mind as the brain: th
Mind as a computer: functionalism (Turing machin
of mind :of mind the stakes
rtificial intelligence.
ul question.
s behaviorsm (one can observe);sm (one can observe);
hat counts.
he mind-brain identity.
machine
ne idea).
Mind as a causal structure: There exist a compleThere exist a compleevents are nodes.Input-output relationInput output relation
Mental causation :Mental causation physical to mental: bumental to physical: tym p y ymental to mental: our
Mental content: interpretat
causal-theoretical functionalismex causal network in which mentalex causal network in which mental
ns play an important role.ns play an important role.
urning one’s fingers;ypewriting;yp g;r thinking.
tion.
Emerging new pag g p(Fran
Overriding task of mind is gMinds are the control struStructure is determined by
coupling; Varela).Mind is better viewed as co
fuziness.Mind operates on ‘sensationVarela: it is structured cou
not sensory input. d Sensing, acting and cognitio
(enacted cognition).
aradigm of mindgnklin)
to produce the next action.pctures of autonomous agents.y evolution or design (structural
ontinuous as opposed to Boolean
n’ to create information.upling which creates information,
h on go together
Mind re-creates prior infMind re creates prior infproduce actions.
Mind tends to be embodieindependent modulndependent modulbetween them (con
Mind is enabled by a mult
Mind, as the action selectagents, to some deg ,implementable on m
What is Intelligence (Kha
formation (memories) to helpformation (memories) to help
ed as collections of relatively es, with little communication es, w th l ttle commun cat on
nnectionism).
titude of disparate mechanisms.
tion mechanism of autonomous egree, is g ,machines.
alfa)
Complexity in computing and AI
Chris LangtonChris Langton
Artificial life researchArtificial life research
Genetic programming/a
Self-organization (the
Interacting (negotiatin
algorithms
bee colony)
ng) agents
Conway’s game of
One of the earlier artific
Simulates behavior of sin
Rules:
•Any live cell with fewer than •Any live cell with more than t•Any dead cell with exactly th•Any cell with two or three ne
next generationnext generation
Plife.exe (windows)( )
f life
cial life simulations
gle cells
two neighbors dies of lonelinessthree neighbors dies of crowdinghree neighbors comes to lifeeighbors lives, unchanged to the
John HollandJ H
Father of genetic progr
Agent-based systems (n
I di id ls h li it d Individuals have limited
Individuals optimize theIndividuals optimize the
Limited interaction (com
ramming
network)
h t isti s characteristics
eir goalseir goals
mmunication) rules
Complex Adaptimp p
Artificial Neural Netwo
Agent-based systems (n
G ti Al ith sGenetic Algorithms
Fuzzy logicFuzzy logic
Fuzzy neural networksy
ve Systemsy m
rks
network)
ARTIFICIAL NEURALARTIFICIAL NEURAL
How does the brain opeHow does the brain ope
L NETWORKS (ANN) (1)L NETWORKS (ANN) (1)
rate?rate?
ARTIFICIAL NEURALARTIFICIAL NEURAL
What does an artificial nW f
Input Layer Hidden
X1
X2
X3X3
X4X4
X5
Xn
L NETWORKS (ANN) (2)L NETWORKS (ANN) (2)
neural network look like?
n Layer Output Layer
Out1
O t2
Out1
Out2
ARTIFICIAL NEURAL ARTIFICIAL NEURAL
How does an artificial neurHow does an artificial neur
X1 W1
X2W2
NETX3 W3
X4
Inputs
W4
Inputs
KNO
NETWORKS (ANN) (3)NETWORKS (ANN) (3)
ral network works (gets trained)ral network works (gets trained)
O t F ( t)T
TRESHOLDVALUE
Out-F (net)
Output
OT
Output
ARTIFICIAL NEURAL ARTIFICIAL NEURAL
Comparison to other DSS
Able to simulate no
H l i b hHas learning behav
Non parametric (noNon-parametric (no
Fault tolerant (can(
Seeking diversity (
Pattern recognition
NETWORKS (ANN) (4)NETWORKS (ANN) (4)
S techniques (advantages)
on-linear behaviour
iviour
o equations)o equations)
n easily deal with NAs)y )
(instead of averages)
n
FUZZY LOGIC (1)( )
F t d l iFuzzy sets and overlapping
1of ship
.7
gree
oem
bers
Deg
me
0 150
b hi f tig membership-functions
Tall
185 200Height in cmHeight in cm
FUZZY LOGIC (2)FUZZY LOGIC (2)
Representation of the co
averaheigshort heig
1hip
short
.7
1
ree
ofm
bers
h
0 49
Deg
mem 0.49
0 150
oncept size using fuzzy sets
ageht Tallht
very talle y a
Height in cm 185 200
g
FUZZY LOGIC (3)( )
Fuzzy rules (1)y ( )
1000
90807060S
PE
ED
60504030R
MO
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3020100
AIR
0
45 50
TEMPTEMP
IF WARMTHEN FAST
55 60 65 70 75 80 85 90
PERATURE IN DEGREES FAHRENHEITPERATURE IN DEGREES FAHRENHEIT
FUZZY LOGIC (4)F Y LOG ( )Fuzzy rules (2)
0 1
F u zzy ru les
100
90
80
BLAST
FAST
70
60
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MEDIUM
MO
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SPE
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COOLCOLD
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IF W A R M
IF H O T ,T H E NB L A S T
IF JU S TR IG H T ,T H E N
IF W A R M ,T H E N F A S T
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OTRMST HT
16º 18 º 21º 24º 27 º 29 º 32º
HOTW
ARM
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R A T U R E IN D E G R E E S C E L S IU S
FUZZY LOGIC (5)
ADVANTAGES:• Smooth behav• Smooth behav• “Human-like” b• Natural langua• Natural langua
EXAMPLES:• Sendai Subwa• Trading syste• Washing machg
micro-waves
iouriourbehaviourage approachage approach
ayyemshines, CAM-corders, s
FUZZY NEURAL NETW
Combination of the learning befuzziness and the (though fuz
Overlapping and vague memberOverlapping and vague memberproblems
Fuzzy rules is a reality in mana
Fuzzy and learning behaviour i
P tt h t b di d Pretty much to be discovered management sciences
WORKS IN MANAGEMENT
ehaviour of neural networks with the zy) rules
rships is a reality in managerial rships is a reality in managerial
agement
s very human
i in s
GENETIC ALGENETIC ALLGORITHMS (1)LGORITHMS (1)
GENETIC ALGGORITHMS (2)
GENETIC ALGOORITHMS (3)
GENETIC ALGGENETIC ALGGORITHMS (4)GORITHMS (4)
GENETIC ALGGORITHMS (5)( )
GENETIC ALGGENETIC ALGGORITHMS (6)GORITHMS (6)
GENETIC ALGGENETIC ALGGORITHMS (7)GORITHMS (7)
GENETIC ALGGENETIC ALGGORITHMS (8)GORITHMS (8)
A beginning of g gSome research pr
Complexity and emergent learnComplexity and emergent learnAgents, Sara Lee/DE
Innovation in SME’s: a networkANNs, brainstorm sess
Telemedecin: a systemic reseadi l k tmedical care market:
AgentsKnowledge management at AkzKnowledge management at Akz
creation ability: ANNs, Akzo Nobel
Information ecology: For the moment a concAgentsAgents
Conflict managementAgentsg
Knowledge management at BisoAgents
evidenceojects
ning in innovation projects:ning in innovation projects:
k structure:sionsarch into the ICT innovations in the
zo Nobel: improving the knowledgezo Nobel: improving the knowledge
ceptual model
on: contribution to innovation
Complexity iin economics
Law of increasiLaw of increasi(Brian Arthur)
• Characteristics of th( li d(a non-linear dynam
• Phenomenon of incre
• Positive feed-back
• No equilibrium
• Quantum structure oQ m(WB)
ing returns ing returns
he information economyi )ic system)
asing returns
of business f
Summary (un
• Non - linearity• Dynamic behavio• Dynamic behavio• Dependence on iP i d d bli• Period doubling
• Existence of att• Determinism• Emergence at thEmergence at th
ntil now)
ororinitial conditions
tractors
he edge of chaoshe edge of chaos
A quantum innterpretation
Gödel’s theorem: 1931No absolute axiomatic syste
Relativity theory (Einstein):Relativity theory (Einstein):No absolute measurement is
Quantum mechanics: first paObservation is interpretatio
Complexity theory (PrigogineEmergence bifurcations stEmergence, bifurcations, st
em is possible
first part of the 20st century first part of the 20st centurys possible
art of the 20st centuryon
e): second part of 20st centuryrange attractorsrange attractors
Once holism and complm mpwe cannot avoid a fund
PAULI comple
Syy(=occurring
From causal coherence (from cause to effect)
A-cau
exity acceptedy pdamental question
ementary physics
ynchronicityy yg–together-in-time)
Coincidence (occurring together)
usal linkshence….
A quantum i
non-lonon-losynchroyentang
nterpretation
ocality; ocality; onicity; yglement
Mechanistic verThe evolution i
Product oriented Unique distribution channelsqControlStabilityM t b bj tiManagement by objectiveProcesses are the assetsHierarchical organization Hierarchical organization Machine thinking (symbolic)Industrial era
rsus organic:gn business
The client co-createsMultiple channelspEmergent processesChange (learning) is the goalM t i h d l itManagement in change and complexityLearning is the assetHuman networksHuman networksHuman thinking (fuzzy)Knowledge era
S tSome quantumMaxwell, Planck and Bohr: intro
beauty and coherenceEinstein de Br lie and SchrödEinstein, de Broglie and Schröd
continuous wave as a bacausal descriptioncausal description
Heisenberg, Pauli, Jordan and Devent-by-event causalit
ll d f d well-defined trajectorieIn 1935, Schrödinger formulatePauli: Background physics has aPauli: Background physics has a
to a natural science whias with consciousness
Pauli accepted that physical valarchetypes, change in tbs Obs ti n iobserver. Observation i
of human consciousness
t im storiesoduced criteria such as fertility,
din er: shared a c mmitment t a dinger: shared a commitment to a asic physical entity subject to a
Dirac: we no longer have ty and particles do not follow
b k des in a space-time backgrounded his famous ‘cat paradox’n archetypal origin and that leads n archetypal origin and that leads ich will work just as well with matter
lues, as much as he eyes of the is th s lt is the result s
Some quantum sq
Polkinghorne: The implication of phenomenon of “entanglephenomenon of entangleremote activity, not simpontological in nature
Polkinghorne (1990): The greatethe more the consciousnresonate with the hologrresonate with the hologr"quantum zero point" (thin an almost resting, but g,energy field
stories (2)( )
these observations is that the ement” (non-locality) includes a real ement (non locality) includes a real ply epistemological, but in fact
r the experience of satisfaction, ess of each cell in the body will raphic information engraved in the raphic information engraved in the he lowest possible state of energy, not quite, situation) of the q , )
So, on the Copenhagen interpphysical processes are, at theinherently indeterministic aninherently indeterministic anclassical physics is dead. Theentanglement (or non-separabentanglement (or non separagives rise to the measuremenmakes it impossible to assign
bi i l d h i l arbitrary isolated physical sywith another system in the pasystems are no longer interacsystems are no longer interaccharacteristic of quantum sysindication of the ‘holistic’ cha
pretation of quantum mechanics, e most fundamental level, both d non local The ontology of d non-local. The ontology of
e heart of the problem is the bility) of quantum states that l ty) of quantum states that nt problem. This entanglement independent properties to an
i h i dystem once it has interactedast – even though these two cting The non-separability cting. The non-separability stems can be seen as an aracter of such systems.y
A quantum in
In the arts: Cara et MurphyIn linguistics: Dalla Chiarra egIn the physical sciences: PauIn biology: Sheldrake (morphI m di i : Ch th AIn medicine: Chopra, the Ay
regular medici
nterpretation
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