Complexity and Knowledge:

The paradigm of the ‘Now-economy’

Prof dr Walter R. J. Baets

Director Graduate Programs, Euromed Marseille – Ecole de Management

Director of Notion, the Nyenrode Institute for Knowledge Management and Virtual Education

2

3

Imagine…

You have planned one of these days

You appear for a non-existing breakfast talk

Your next meeting is cancelled, since yourvisitor is waiting for you at his office

It was all nicely planned

Bad luck, or normal ?

4

Imagine…

Your innovation management is well organized

You even have a well-researched methodology

Your people are encouraged to think out-of-the-box

But new products seldom come up

More of the same

Coincidence ?

5

Imagine…

You want to become a learning organization

But your people don’t want to share their knowledge

In fact, they don’t want to change and theydon’t want to learn

If that would not be the case, you could becomea learning organization

Or not ?

6

Imagine, even worse…

You are a partner of a well known consultancy

Your friends envy you for this

Suddenly, a snowball ruins your company...

… due to bad publicity

Could you have expected this ?

7

Imagine…

You are shareholder of Enron or Lernout &Hauspie

Promising companies in exciting sectors

Suddenly, your investment fades aways

Strange ?

8

Flatland: Edwin Abbott, 1884

A. Square meets the third dimension

9

Wanderer, your footprints arethe path, and nothing more;Wanderer, there is no path,it is created as you walk.By walking,you make the path before you,and when you look behindyou see the path which after youwill not be trod again.Wanderer, there is no path,but the ripples on the waters.

Antonio Machado,Chant XXIX Proverbios y cantares,Campos de Castilla, 1917

10

A very great musician came and stayed in our house,He made one big mistake …He was determined to teach me musicand consequently, no learning took place.Nevertheless, I did casually pick up from hima certain amount of stolen knowledge.

Rabindranath Tagore

Taylor’s view on the brain

The computer: attempt to automate human thinking

Manipulating symbols Modeling the brain

Represent the world Simulate interaction of neurons

Intelligence = problem solving Intelligence = learning

0-1 Logic and mathematics Approximations, statistics

Rationalist, reductionist Idealized, holistic

Became the way of building computersBecame the way of looking at minds

12

The role of the scientist /philosopher of science in business

Picture science within its contemporary framework (not in the absolute)

Provide a framework that allows judgement about the epistemological relevance of a theory (or application)

Philosophy of science is often embedded in sociology and history (other than philosophy that often develops its own logic)

13

My taxonomy of philosophy of science

Historical embeddingOrigin

Philosophicaltheories

Designconsequences

Logical positivism (Wiener Kreis)

Critical rationalism(Popper)

Kuhn’s paradigm theoryLakatos theory

Symbolic interactionismCritical theories

Philosophy

DeductionInduction

EmpiricismHypotheses testingQualitative research

ArchitectureArts

Usefulness as a criteria

Feyerabend’s chaostheoryPostmodern theories

(Derida, Apostel, Foucault, Deleuze)

Design paradigm(van Aken)

Social construction ofreality

Design norms

14

My taxonomy of philosophy of science/2

Historical embeddingOrigin

Philosophicaltheories

Designconsequences

Neurobiology

CognitiveArtificial

Intelligence

Radical constructivism(Maturana, Mingers)Autopoiesis (Varela)

Self-reference (Gödel)

Dynamic re-creationThe emergence ofobject and subjectLocal (contextual)

validity

Paradigm of mind(Franklin, Kim)

Adaptive systemsImplicit learning

15

The manager’s dilemma

Manager’s code of ethics

Manager’s philosophy concerning

humanbehavior

Manager’sunderstanding of

the politicalcontext

Manager’sepistemology

Manager’sresource

constraints

ManagerManagerial problem Management context

Management strategy Subsequent findings and its validity

Manager’s prior and ongoing exposures to, and socialization into, intellectual, socialand political traditions, mores, norms and values

The impact ofthe unforeseen(opportunity or

threat)

16

I

WE

IT

ITS

Interior-IndividualIntentional

Interior-collectiveCultural

Exterior-IndividualBehavioral

Exterior-CollectiveSocial

World of: sensation, impulses, emotion, concepts, vision

World of: magic, mythic, values

World of: atoms, molecules, neuronal organisms, neocortex

World of: societies, division of labour, groups, families, tribes, nation/state,agrarian, industrial and informational

Truthfulness

Justness Functional fit

Truth

Ken Wilber: A Brief History of Everything

17

Research methodology

Traditional approach

Problem statementExisting literatureResearch hypothesisData gatheringAnalysisAcceptance/rejection of hypothesisGeneral conclusionsFurther research

18

Research methodology

Practical research

Loop of :emergent problem statementexploration of dataemergent research hypotheses

Measurability (of perceptions) ?

19

Design paradigm for managementapplications

Business research: between academia and professionalsScholarly quality and managerial relevance.

Types of science:Formal sciences: philosophy, mathematicsExplanatory sciences: natural sciences, social sciencesDesign sciences: engineering, medical, psychotherapy,

management.

Mission: develop knowledge to be used in design and realization of artifacts:

construction problems;improvement problems.

20

Tested and grounded technological rules is a typicalresearch product of design science.

Typical research design is ‘clinical research’ = researchon the effect of interventions.

Typical research cycle will be multiple cases (solved) witha reflective cycle.

21

Sometimes small differences in the initial

conditions generate very large differences

in the final phenomena. A slight error in the

former could produce a tremendous error in

the latter.

Prediction becomes impossible; we have

accidental phenomena.

Poincaré in 1903

22

Sensitivity to initial conditions (Lorenz)

Xn+1 = a * Xn * (1 - Xn)

0.294 1.4 0.3 0.7

23

Cobweb Diagrams (Attractors/Period Doubling)

Xn+1 = * Xn * (1 - Xn) (stepfunction)

dX / dt = X (1 - X) (continuous function)

On the diagrams one gets:• Parabolic curve• Diagonal line Xn+1 = Xn

• Line connecting iterations

24

Lorenz curve (Butterfly effect)

Lorenz (1964) was finally able to materialize Poincaré’s claim

Lorenz weather forecasting model

dX / dt = B ( Y - X )

dY / dt = - XZ + rX - Y

dZ / dt = XY - bZ

25

Hénon Attractor

X n+1 = 1 - a * X 2 n + Y n

Y n+1 = b * X n

Again, different attractors are shown

Other examples: Pendulum of Poincaré, Horse Shoe

26

Why can chaos not be avoided ?

• Social systems are always dynamic and non-linear

• Measurement can never be correct

• Management is always a discontinuous approximation of a continuous phenomenon

27

Fractals (Mandelbrot set)

Julia set: Z Z 2 + C (C is constant; Z is complex)

Self-similarity on different levels of detail

CoastlineCody FlowerBranches of a tree

Those forms cannot be reduced to any geometrical figure (Mandelbrot)

It is a set of attractors (gingerbread-man) for a set of differentequations

Dependence on starting values of z

Mandelbrot set is a fractal (needs a computer)

Ilya Prigogine

• Non-linear dynamic models (initial state, period doubling,….)

• Irreversibility of time principle

• The constructive role of time

• Behavior far away from equilibrium (entropy)

• A complex system = chaos + order

• Knowledge is built from the bottom up

29

Entropy

Measure for the amount of disorder

When entropy is 0, no further information is necessary(interpretation is that no information is missing

There is a maximum entropy in each system (in the bifurcationdiagram, this is 4)

Connection between statistical mechanics and chaos is applying entropy to a chaotic system in order to compare with anassociated statistical system

Francesco Varela

• Self-creation and self-organization of systems and structures (autopoièse)

• Organization as a neural network• The embodied mind• Enacted cognition• Subject-object division is clearly artificial• How do artificial networks operate (Holland)• Morphic fields and morphic resonance (Sheldrake)

31

Implications of autopoiesis

Plus ça change, plus c’est la même chose.Organizational closure (immune system, nervous system,

social system).Structural determinism.Dynamic systems interact with the environment through

their structure.Inputs (perturbations) and outputs (compensations).Structural coupling = adaptation where the environment does

not specify the adaptive changes that will occur.Self-production was not only specified for biological systems

(computer generated models; human organizations, law)In Artificial Intelligence:

Emergence-connectionism (ANNs, complexity,…)Emergence-enaction (communication platforms)

32

Ontology of autopoiesis

Perceptions and experiences occur through and are mediatedby our bodies and nervous systems.

Therefor it is impossible for us to generate a descriptionthat is a pure description of reality, independentof ourselves.

Experience always reflects the observer.

There is no object of our knowledge, it is distinguishedby the observer.

33

Self - Reference

Gödel theorem (1931)

‘All consistent axiomatic formulations of the number theorycontains propositions on which one cannot decide.’

It all boils down to a ‘loop’ problem (being self-referential)(Esher drawings)

Language is self-referential.Can we make numbers self-referential ?

Number theory

34

Constant GödelSign Number Meaning

~ 1 notv 2 or 3 If ….. Then 4 There is an …..= 5 equal0 6 zeros 7 The immediate

successor of( 8 punctuation mark) 9 punctuation mark‘ 10 punctuation mark

35

Numerical Gödel A PossibleVariable Number Substitution Instance

x 11 0y 13 s0z 17 y

Sentential Gödel A PossibleVariable Number Substitution Instance

p 112 0 = 0q 132 (x)(x=sy)r 172 p q

Predicate Gödel A PossibleVariable Number Substitution Instance

P 113 PrimeQ 133 CompositeR 173 Greater than

36

( x) (x =sy )

( x ) ( x = s y ) 8 4 11 9 8 11 5 7 13 9

28 * 34 * 511 * 79 * 118 *1311 * 175 * 197 * 2313 * 299

37

Gödel number is a number that substitutes an expression(about numbers)

Gödel’s world contains numbers:Expressions in number theory;Or, expressions about expressions in number theory.

No existing system of numbers, no reference system (of anykind) can be found in which everything can be corrector complete.

Societal consequences of self-reference.

Chris Langton

Artificial life research

Genetic programming/algorithms

Self-organization (the bee colony)

Interacting (negotiating) agents

39

Conway’s game of life

One of the earlier artificial life simulations

Simulates behaviour of single cells

Rules:

•Any live cell with fewer than two neighbours dies of loneliness•Any live cell with ore than three neighbours dies of crowding•Any dead cell with exactly three neighbours come to life•Any cell with two or three neighbours lives, unchanged to the

next generation

Plife.exe (windows)

John Holland

Father of genetic programming

Agent-based systems (network)

Individuals have limited characteristics

Individuals optimize their goals

Limited interaction (communication) rules

41

Law of increasing returns (Brian Arthur)

• Characteristics of the information economy (a non-linear dynamic system)

• Phenomenon of increasing returns

• Positive feed-back

• No equilibrium

• Quantum structure of innovation (WB)

42

Emerging new paradigm of mind (Franklin)

• Overriding task of mind is to produce the next action• Minds are control structures of autonomous agents• Mind is better viewed as continuous as opposed to Boolean fuziness• Mind operates on ‘sensation’ to create information• Varela: it is structured coupling which creates information, not sensory input• Sensing, acting and cognition go together (enacted cognition)• Mind re-creates prior information in order to help produce actions• Mind tends to be embodied as collections of relatively independent modules, with little communication between them

Hence: mind (as the action selection mechanism of autonomous agents), to some degree, is implementable on machines

43

Summary (until now)

• Non - linearity• Dynamic behavior• Dependence on initial conditions• Period doubling• Existence of attractors• Determinism• Emergence at the edge of chaos