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

TOR

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

50

T

MEDIUM

MO

TOR

SPE

ED

4 0

30

20IFC O L D ,T H E N

IF C O OT H E NS L O W

SLOWST

AIR

M

1

10 S T O P

STOP

LCO

7º 13º10 º0

1

COOLCOLD

T E M P E

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

O L ,M E D IU M

OTRMST HT

16º 18 º 21º 24º 27 º 29 º 32º

HOTW

ARM

JUST

RIGH

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

yet Giuntiniulihogenetic fields and resonance)

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