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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
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 ?
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 ?
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 ?
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 ?
Imagine… You are shareholder of Enron or Lernout & Hauspie Promising companies in exciting sectors Suddenly, your investment fades aways Strange ?
Flatland: Edwin Abbott, 1884 A. Square meets the third dimension
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
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
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)
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
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
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
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
Research methodology Traditional approach Problem statement Existing literature Research hypothesis Data gathering Analysis Acceptance/rejection of hypothesis General conclusions Further research
Research methodology Practical research Loop of : emergent problem statement exploration of data emergent research hypotheses Measurability (of perceptions) ?
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.
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.
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
Sensitivity to initial conditions (Lorenz) Xn+1 = a * Xn * (1 - Xn) 0. 294 1. 4 0. 3 0. 7
Cobweb Diagrams (Attractors/Period Doubling) Xn+1 = * Xn * (1 - Xn) d. X / dt = X (1 - X) (stepfunction) (continuous function) On the diagrams one gets: • Parabolic curve • Diagonal line Xn+1 = Xn • Line connecting iterations
Lorenz curve (Butterfly effect) Lorenz (1964) was finally able to materialize Poincaré’s claim Lorenz weather forecasting model d. X / dt = B ( Y - X ) d. Y / dt = - XZ + r. X - Y d. Z / dt = XY - b. Z
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
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
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)
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 28
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
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) 30
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)
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.
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
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
Numerical Variable Gödel Number A Possible Substitution Instance x y z 11 13 17 0 s 0 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
( x) (x =sy ) ( 8 4 x 11 ) 9 ( 8 x 11 = 5 s 7 y 13 28 * 34 * 511 * 79 * 118 *1311 * 175 * 197 * 2313 * 299 ) 9
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.
Chris Langton Artificial life research Genetic programming/algorithms Self-organization (the bee colony) Interacting (negotiating) agents 38
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 40
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)
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
Summary (until now) • Non - linearity • • • Dynamic behavior Dependence on initial conditions Period doubling Existence of attractors Determinism Emergence at the edge of chaos