Transcript No Slide Title - UCT GSB Blogs

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
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Imagine…
You have planned one of these days
You appear for a non-existing breakfast talk
Your next meeting is cancelled, since your
visitor is waiting for you at his office
It was all nicely planned
Bad luck, or normal ?
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Imagine…
Your innovation management is well organized
You even have a well-researched methodology
Your people are encouraged to think out-ofthe-box
But new products seldom come up
More of the same
Coincidence ?
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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 they
don’t want to learn
If that would not be the case, you could become
a learning organization
Or not ?
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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 ?
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Imagine…
You are shareholder of Enron or Lernout &
Hauspie
Promising companies in exciting sectors
Suddenly, your investment fades aways
Strange ?
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Flatland: Edwin Abbott, 1884
A. Square meets the third dimension
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Wanderer, your footprints are
the 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 behind
you see the path which after you
will 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
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A very great musician came and stayed in our house,
He made one big mistake …
He was determined to teach me music
and consequently, no learning took place.
Nevertheless, I did casually pick up from him
a certain amount of stolen knowledge.
Rabindranath Tagore
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Taylor’s view on the brain
The computer: attempt to automate human thinking
Manipulating symbols
Represent the world
Modeling the brain
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 computers
Became the way of looking at minds
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)
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My taxonomy of philosophy of science
Historical embedding
Origin
Philosophical
theories
Design
consequences
Philosophy
Logical positivism
(Wiener Kreis)
Critical rationalism
(Popper)
Kuhn’s paradigm theory
Lakatos theory
Symbolic interactionism
Critical theories
Deduction
Induction
Empiricism
Hypotheses testing
Qualitative research
Architecture
Arts
Usefulness as a criteria
Feyerabend’s chaostheory
Postmodern theories
(Derida, Apostel,
Foucault, Deleuze)
Design paradigm
(van Aken)
Social construction of
reality
Design norms 13
My taxonomy of philosophy of science/2
Historical embedding
Origin
Philosophical
theories
Design
consequences
Neurobiology
Radical constructivism
(Maturana, Mingers)
Autopoiesis (Varela)
Self-reference (Gödel)
Dynamic re-creation
The emergence of
object and subject
Local (contextual)
validity
Cognitive
Artificial
Intelligence
Paradigm of mind
(Franklin, Kim)
Adaptive systems
Implicit learning
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The manager’s dilemma
Manager’s prior and ongoing exposures to, and socialization into, intellectual, social
and political traditions, mores, norms and values
Manager’s code
of ethics
Manager’s
philosophy concerning
human
behavior
Managerial problem
Management strategy
Manager’s
understanding of
the political
context
Manager’s
epistemology
Manager
Management context
Subsequent findings and its validity
The impact of
the unforeseen
(opportunity or
threat)
Manager’s
resource
constraints
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I
IT
Interior-Individual
Intentional
World of: sensation, impulses,
emotion, concepts, vision
Exterior-Individual
Behavioral
World of: atoms, molecules, neuronal
organisms, neocortex
Truthfulness
Truth
Justness
Functional fit
World of: magic, mythic, values
World of: societies, division of labour,
groups, families, tribes, nation/state,
agrarian, industrial and informational
Interior-collective
Cultural
WE
Ken Wilber: A Brief History of Everything
Exterior-Collective
Social
ITS
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Research methodology
Traditional approach
Problem statement
Existing literature
Research hypothesis
Data gathering
Analysis
Acceptance/rejection of hypothesis
General conclusions
Further research
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Research methodology
Practical research
Loop of :
emergent problem statement
exploration of data
emergent research hypotheses
Measurability (of perceptions) ?
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Design paradigm for management
applications
Business research: between academia and professionals
Scholarly quality and managerial relevance.
Types of science:
Formal sciences: philosophy, mathematics
Explanatory sciences: natural sciences, social sciences
Design sciences: engineering, medical, psychotherapy,
management.
Mission: develop knowledge to be used in design and
realization of artifacts:
construction problems;
improvement problems.
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Tested and grounded technological rules is a typical
research product of design science.
Typical research design is ‘clinical research’ = research
on the effect of interventions.
Typical research cycle will be multiple cases (solved) with
a reflective cycle.
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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
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Sensitivity to initial conditions (Lorenz)
Xn+1 = a * Xn * (1 - Xn)
0.294
1.4
0.3
0.7
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Cobweb Diagrams (Attractors/Period Doubling)
Xn+1 =  * Xn * (1 - Xn)
dX / dt =  X (1 - X)
(stepfunction)
(continuous function)
On the diagrams one gets:
• Parabolic curve
• Diagonal line Xn+1 = Xn
• Line connecting iterations
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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
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Hénon Attractor
X
n+1
= 1 - a * X
Y
n+1
= b * X
2
n
+ Y
n
n
Again, different attractors are shown
Other examples: Pendulum of Poincaré,
Horse Shoe
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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
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Fractals (Mandelbrot set)
Self-similarity on different levels of detail
Coastline
Cody Flower
Branches 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 different
equations
Julia set: Z  Z
2
+ C (C is constant; Z is complex)
Dependence on starting values of z
Mandelbrot set is a fractal (needs a computer)
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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
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 bifurcation
diagram, this is 4)
Connection between statistical mechanics and chaos is applying
entropy to a chaotic system in order to compare with an
associated statistical system
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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)
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) 31
Ontology of autopoiesis
Perceptions and experiences occur through and are mediated
by our bodies and nervous systems.
Therefor it is impossible for us to generate a description
that is a pure description of reality, independent
of ourselves.
Experience always reflects the observer.
There is no object of our knowledge, it is distinguished
by the observer.
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Self - Reference
Gödel theorem (1931)
‘All consistent axiomatic formulations of the number theory
contains 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
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Constant
Sign
~
v


=
0
s
(
)
‘
Gödel
Number
1
2
3
4
5
6
7
8
9
10
Meaning
not
or
If ….. Then
There is an …..
equal
zero
The immediate
successor of
punctuation mark
punctuation mark
punctuation mark
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Numerical
Variable
Gödel
Number
A Possible
Substitution Instance
x
y
z
11
13
17
0
s0
y
Sentential
Variable
Gödel
Number
A Possible
Substitution Instance
p
q
112
132
r
172
0=0
(x)(x=sy)
Predicate
Variable
Gödel
Number
A Possible
Substitution Instance
P
Q
R
113
133
173
Prime
Composite
Greater than
pq
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( x) (x =sy )
(

8


4
x

11
)

9
(

8
x

11
=

5
s

7
y

13
)

9
28 * 34 * 511 * 79 * 118 *1311 * 175 * 197 * 2313 * 299
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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 any
kind) can be found in which everything can be correct
or complete.
Societal consequences of self-reference.
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Chris Langton
Artificial life research
Genetic programming/algorithms
Self-organization (the bee colony)
Interacting (negotiating) agents
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)
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John Holland
Father of genetic programming
Agent-based systems (network)
Individuals have limited characteristics
Individuals optimize their goals
Limited interaction (communication) rules
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)
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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
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Summary (until now)
• Non - linearity
•
•
•
•
•
•
Dynamic behavior
Dependence on initial conditions
Period doubling
Existence of attractors
Determinism
Emergence at the edge of chaos
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