The Evolution of Complexity:

an introduction

The Evolution of Complexity:

an introduction

Francis Heylighen

Evolution, Complexity and Cognition group (ECCO)

Vrije Universiteit Brussel

Francis Heylighen

Evolution, Complexity and Cognition group (ECCO)

Vrije Universiteit Brussel

A Transdisciplinary Perspective

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

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

Characterized by •analysis

•reductionism

Focuses on separate components

Characterized by •analysis

•reductionism

Focuses on separate components

Classical scienceClassical science

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"

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"

ComplexityComplexity

Distinguishable parts (differentiation)

Connected into a whole (integration)

Distinct from the environment

•Separated by boundary

Yet, open

•= interacting with the environment

•Exchanges across boundary

Distinguishable parts (differentiation)

Connected into a whole (integration)

Distinct from the environment

•Separated by boundary

Yet, open

•= interacting with the environment

•Exchanges across boundary

What is a System?What is a System?

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

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

EmergenceEmergence

EvolutionEvolution

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

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 ProgressEvolutionary 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

“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 fitness3 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

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-evolutionCo-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

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 NetworksEmergence 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

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

S

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b

c

d

e

f

g

h

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System as Network of AgentsSystem as Network of Agents

Formation of BondsFormation of Bonds

Two systems encountering each other may develop a stable connection or bond

e.g. Two atoms forming a moleculeTwo people forming a couple

Formation of BondsFormation of Bonds

•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

linked components are integrated into new whole

non-linked components are more strongly differentiated

linked components are integrated into new whole

non-linked components are more strongly differentiated

Differentiation and Integration

Differentiation and Integration

Self-organization

of Hierarchies

Self-organization

of Hierarchies

Growth of structural

complexity

Evolution of adaptivityEvolution 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

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 varietyLaw 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

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 complexityFunctional 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”

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

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

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

Example: office organization

Coordination mechanismsCoordination 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

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

spontaneous appearance of order or organization

not imposed by an outside system or inside components

organization distributed over all the components

•collective

•Robust

spontaneous appearance of order or organization

not imposed by an outside system or inside components

organization distributed over all the components

•collective

•Robust

Self-organizationSelf-organization

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Self-organization of coordination

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

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

ConclusionConclusion

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

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