The Evolution of Complexity an introduction Francis Heylighen

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

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

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

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"

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

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 (Na. Cl): taste, color, shape, . . . = emergent

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

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

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

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

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

System as Network of Agents

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

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

Selforganization of Hierarchies Growth of structural complexity

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

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

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”

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

Example: office organization

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

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

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

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