Management in complexity Physics and Biology

1. Management in complexityPhysics and Biology Walter Baets, PhD, HDR Associate Dean for Innovation and Social Responsibility Professor Complexity , Knowledge and Innovation Euromed Marseille – Ecole de Management

2. Physics

3. 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

4. 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

5. Biological complexity

6. 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)

7. Self-producing systems, autopoiesis radical constructivism, self-reference Maturana, Varela, Gödel, Mingers Biological principle of self-producing systems = Autopoeisis Has been interpreted a lot by different fields, differently In opposition to the focus on species and genes, Maturana and Varela pick out the single, biological individual (e.g. an amoebae) as the central example of a living system. Individual autonomy, self-defined entities within an organism.

8. Living systems operate in an essentially mechanistic way. The overall behavior of the whole is generated purely by the components and its interactions. Observers are external to the system. Observers perceive both an entity and its environment. Components within an entity act purely in response to other components. Any explanation of living systems should be nonteleological, having no recourse to idea of purpose, goal, ends and functions. Living systems are autopoietic (self-producing) circular, self-referring organization

9. 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) Law as an autopoietic system (Teubner)

10. Philosophical implications of autopoiesis Epistemological and ontological presuppositions. It constitutes a theory about the observer. It implies there is no claim to objectivity. Beliefs and theories are purely human constructs which ‘constitute’ rather than reflect reality constructivism. ‘Biology of cognition’ (1970): observer is the system in which description takes place.

11. 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.

12. Rupert Sheldrake (morphogenetic fields) • They are self-organised “collections” or “collectivities”; • They have a time and space aspect and they organise from time/space schemas of vibrations (energy) (and therefore from interaction); • They attract the systems under their influence towards characteristic forms or models. They organise the realisation of these activities and preserve the integrity of these • activities. The goals or the places • where these activities are attracted • are called he attractors;

13. Rupert Sheldrake (morphogenetic fields) 2 • The morphic fields are put in relationship with holons (units which are themselves entire). The morphic fields therefore include other morphic fields in a climbing hierarchy (nested hierarchy) or holarchy. These holarchies are created in an emergent fashion; • They are structures of probability and also their organising activity is probabilistic; • They include a so-called closed memory, formed by self-resonance with its own past and morphic • resonance with comparable anterior • systems. This memory is cumulative. • As more models repeat themselves they • become more normal.

14. Paradigm of mind : What are the stakes Based on cognitive artificial intelligence. The mind and the soul question. Behaviorism: mind as behavior experimentalism (one can observe); behavior is what counts. Mind as the brain: the mind-brain identity. Mind as a computer: machine functionalism (Turing machine idea).

15. Mind as a causal structure: causal-theoretical functionalism There exist a complex causal network in which mental events are nodes. Input-output relations play an important role. Mental causation : physical to mental: burning one’s fingers; mental to physical: typewriting; mental to mental: our thinking. Mental content: interpretation.

16. Emerging new paradigm of mind (Franklin) Overriding task of mind is to produce the next action. Minds are the control structures of autonomous agents. Structure is determined by evolution or design (structural coupling; Varela). 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).

17. Mind re-creates prior information (memories) to help produce actions. Mind tends to be embodied as collections of relatively independent modules, with little communication between them (connectionism). Mind is enabled by a multitude of disparate mechanisms. Mind, as the action selection mechanism of autonomous agents, to some degree, is implementable on machines. What is Intelligence (Khalfa)