The Evolution of Complexity: an introduction

1. The Evolution of Complexity: an introduction Francis Heylighen Evolution, Complexity and Cognition group (ECCO) Vrije Universiteit Brussel

2. A Transdisciplinary Perspective • Conceptual scheme applicable to all complex, evolving systems • Particles, molecules, cells, organisms, societies, galaxies… • Unifying models in all classical disciplines • Physics, chemistry, biology, psychology, sociology, economics, etc. • Requires some simple concepts and assumptions that are generally valid

3. Classical science • Characterized by • analysis • reductionism • Focuses on separate components

4. Complexity • complexus = entwined, embracing • distinguishable parts • that are connected • so that they are difficult to separate • differentiation + integration • in between order and disorder • the "edge of chaos"

6. What is a System? • Distinguishable parts (differentiation) • Connected into a whole (integration) • Distinct from the environment • Separated by boundary • Yet, open • = interacting with the environment • Exchanges across boundary

7. Emergence • Whole = more than sum of the parts • connections create properties that are not inherent in the parts • emergent properties • examples • car: max. speed = emergent, weight = sum • music: melody, rhythm, harmony = emergent • salt(NaCl): taste, color, shape, ... = emergent

8. Evolution • Emergence and change of systems over time • Produced by BVSR • Blind Variation and • Selective Retention • of the “fittest” configurations • Fitness = ability to maintain and multiply • in a given environment

9. Evolutionary Progress • “Survival of the fittest” is a tautology • what is fit = what survives = what is selected • Logically necessary principle → • automatic mechanism, no explanation needed • Assume variation • Some configurations fitter, some less fit • Fitter ones are preferentially retained → • Fitness tends to increase

10. 3 ways to achieve fitness • 1. Intrinsic robustness/stability • E.g. a diamond • 2. Adaptedness • “fitting” in to a specific environment • E.g. koala in eucalyptus forest • 3. Adaptivity • Flexibility, ability to adapt to a variety of environments • E.g. humans • Each leads to different types of complexity

11. Co-evolution • System + Environment is too simple • The environment is much too complex to be reduced to a single influence • Better: interacting agents • Agent= (relatively) autonomous system • E.g. molecule, cell, organism, person, firm • Agents undergo variation and selection in an environment of other agents • Change in one agent requires adaptation in the agents it interacts with • → On-going, mutual adaptation

12. Emergence of Networks • Two Agents interact • Mutual variation and selection • Until they reach a fit configuration • Reciprocal adaptation • → creation of bond, link or coupling • Many agents developing many links → network

13. System as Network of Agents

14. Two systems encountering each other may develop a stable connection or bond e.g. Two atoms forming a molecule Two people forming a couple Formation of Bonds

15. Many agents may get linked together, forming a system or “superagent” Superagents in turn get linked together forming a “super-super-system” This produces structural complexity Formation of Bonds

16. Differentiation and Integration • linked components are integrated into new whole • non-linked components are more strongly differentiated

17. Growth of structural complexity Self-organization of Hierarchies

18. Evolution of adaptivity • Individual agents too tend to become more complex • By increasing their adaptivity • Adaptivity achieved by control or regulation • Compensating “perturbations” (changes in environmental conditions) • by appropriate actions • E.g. chameleon compensates changes in background color by changes in skin color

19. Law of requisite variety • The larger the variety of perturbations, the larger the variety of actions the agent should be able to perform (W.R. Ashby) • A complex, variable environment demands a large repertoire of actions • However, the agents must choose the right action for the right condition • → law of requisite knowledge agent must “know” appropriate rules of the form: condition → action

20. Functional complexity • Control laws → selective pressure for: • More variety of action (functional differentiation) • More knowledge rules to connect conditions and actions (functional integration) → growth in functional complexity • Growth in ability to deal with complex problems → growth in agent “intelligence”

21. Combining structural and functional complexity • Agents develop links → structural complexity • But become more adaptive in their actions → functional complexity • Becoming collectively more adaptive requires not bonds (“hard” connections), but coordinated actions Actions that together achieve more than alone: synergy, cooperation

22. Example: office organization

23. Coordination mechanisms • Alignment of targets Avoiding conflict or friction • Division of labor Differentiation or specialization of agents • Workflow Actions performed in right sequence • Aggregation of results • Regulation Correcting errors via feedback

24. Self-organization • spontaneous appearance of order or organization • not imposed by an outside system or inside components • organization distributed over all the components • collective • Robust

25. Self-organization of coordination • Stigmergy • Trace left by action stimulates performance of subsequent action • Examples • Ant pheromone trail laying • Wikipedia • Hebbian learning • Successful sequences of actions are reinforced • Unsuccessful ones are weakened

26. Conclusion • Variation and selection automatically increase fitness • which indirectly increases complexity • Fitness can be achieved via • Stable bonds → structural complexity • → Hierarchies of supersystems • More adaptive agents → functional complexity • → Evolvability and individual intelligence • More coordinated actions → organizational complexity • → Collective intelligence, “social” systems