Complexity and Knowledge: The paradigm of the ‘Now-economy’ 

1. 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

3. 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 ?

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 they don’t want to learn If that would not be the case, you could become a 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 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

10. 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

11. 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 computers Became 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 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

14. 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

15. 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 Manager’s epistemology Manager Managerial problem Management context Management strategy Subsequent findings and its validity The impact of the unforeseen (opportunity or threat) Manager’s understanding of the political context Manager’s resource constraints

16. I IT Interior-Individual Intentional Exterior-Individual Behavioral World of: sensation, impulses, emotion, concepts, vision World of: atoms, molecules, neuronal organisms, neocortex Truthfulness Truth Functional fit Justness World of: societies, division of labour, groups, families, tribes, nation/state, agrarian, industrial and informational World of: magic, mythic, values Exterior-Collective Social Interior-collective Cultural WE ITS Ken Wilber: A Brief History of Everything

17. Research methodology Traditional approach Problem statement Existing literature Research hypothesis Data gathering Analysis Acceptance/rejection of hypothesis General conclusions Further research

18. Research methodology Practical research Loop of : emergent problem statement exploration of data emergent research hypotheses Measurability (of perceptions) ?

19. 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.

20. 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.

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) 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)

28. 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 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

30. 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 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.

33. 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

34. Constant Gödel Sign Number Meaning ~ 1 not v 2 or  3 If ….. Then  4 There is an ….. = 5 equal 0 6 zero s 7 The immediate successor of ( 8 punctuation mark ) 9 punctuation mark ‘ 10 punctuation mark

35. Numerical Gödel A Possible Variable Number Substitution Instance x 11 0 y 13 s0 z 17 y Sentential Gödel A Possible Variable Number Substitution Instance p 112 0 = 0 q 132 (x)(x=sy) r 172 p  q Predicate Gödel A Possible VariableNumber Substitution Instance P 113 Prime Q 133 Composite R 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 any kind) can be found in which everything can be correct or complete. Societal consequences of self-reference.

38. 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)

40. 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