About complexity and knowledge

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About complexity and knowledge How order leads to
chaos !
Prof dr Walter Baets Euromed Marseille, Ecole de
Management The Nyenrode Institute for Knowledge
Management and Virtual Education
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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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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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Taylors 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
computers Became the way of looking at minds
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Ken Wilber A Brief History of Everything
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Complexity Theory
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Sensitivity to initial conditions (Lorenz)
Xn1 a Xn (1 - Xn)
0.294 1.4 0.3 0.7
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Cobweb Diagrams (Attractors/Period Doubling)
Xn1 ? Xn (1 - Xn) (stepfunction) dX
/ dt ? X (1 - X) (continuous function)

• On the diagrams one gets
• Parabolic curve
• Diagonal line Xn1 Xn
• Line connecting iterations

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Lorenz curve (Butterfly effect)
Lorenz (1964) was finally able to materialize
Poincarés claim Lorenz weather forecasting
model dX / dt B ( Y - X ) dY / dt -
XZ rX - Y dZ / dt XY - bZ
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Ilya Prigogine

• Non-linear dynamic models (initial state,
• period doubling,.)
• Irriversibility of time principle
• Behaviour far away from equilibrium (entropy)
• A complex system chaos order
• Knowledge is built from the bottom up

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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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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)

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Knowledge and learning
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Innovation as learning
Emotions
Facts
Internal
External
EXPERIENCES
CONTEXTUAL KNOWLEDGE

INDIVIDUAL MENTAL MODEL
Emotions
Individuals with characteristics
(agents)
Individual
Collective
SHARED MENTAL MODELS
Emotions
Interaction
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Your knowledge infrastructure
Your knowledge infrastructure
Your knowledge infrastructure
Ownership (search/learn principles) Remains with
those that use it Those that want to learn decide
what to learn Just-in-time, just-enough
Learning platform Provide an ICT infrastructure t
hat allows full access and sharing facilities

• Content
• What knowledge
• to share
• explicit
• implicit
• learned

Culture Turn XYZ into a learning culture
(via projects) Rewarding
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Learning platform and search/learn principles
The knowledge net
Explicit knowledge (database)
Open learning platform Collaborative
tools Dedicated search engines Accessibility for
all Open to connect any application Solution
for e-learning
Implicit knowledge (case base) Case based
reasoning system Cases stored in an adapted way A
methodology for case analysis and
storage Corporate knowledge repository

Notion
Search engine
The user with its learning agenda
Learned knowledge (case base) Explicit knowledge
that is enhanced via experience Using the same
methodology for implicit knowledge Interviews
with key knowledge owners

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For a 18-24 months period
Workshops 200 study hours Innovative projects
700 study hours Virtual grouplearning 600 study
hours
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Some interesting technologies
Artificial Neural Networks Genetic
Algorithms Genetic Programming Fuzzy
Logic Artificial life/Agent simulations Negotiatin
g Agents Semantic Search Engines Case Based
Reasoning Language technologies Machine learning
technologies Conversational technologies
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Methodology
Outcomes (company-specific)
Actions
The Hybrid Business School
White Paper (Board approval) E-learning view
Building Blocks
Brainstorm
4 Action plans (Board approval)
4 Brainstorms

• Project team
• Notion
• MD/HRM
• Line mgt
• IT
• Marketing/RD

Hyper linked
Knowledge platform
Explicit knowledge
Infrastructure (Plan)
learner learning agenda
Search engine
Implicit knowledge
Skills Activities
Hyper linked
IT/Application plan
Architecture
cases
Practices
Concepts
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Some statements
Knowledge products can easily be copied (pharma
example) Information even faster Is legal
protection possible in the knowledge economy?
(patents) Protection on HIV drugs ethics
against law (South Africa) Mobile phones money
is not made on the hardware, but on the
services What is a companys value added is it
learning or repetition (can it be machine
replaced ?) Information against faster learning
based innovation
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Ken Wilber A Brief History of Everything
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