Challenges, Agents and Coordination:

how an action ontology can help us tackle both practical and foundational problems

Challenges, Agents and Coordination:

how an action ontology can help us tackle both practical and foundational problems

Francis Heylighen

Evolution, Complexity and Cognition group

Vrije Universiteit Brussel

Francis Heylighen

Evolution, Complexity and Cognition group

Vrije Universiteit Brussel

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OntologyOntology

Philosophy of what is

•What reality is constituted of

Most basic elements or concepts

•E.g. matter, ideas, energy, fields, spirit…

Building blocks of all higher level theories

•Different ontologies result in different models

•This has practical implications for solving problems

Philosophy of what is

•What reality is constituted of

Most basic elements or concepts

•E.g. matter, ideas, energy, fields, spirit…

Building blocks of all higher level theories

•Different ontologies result in different models

•This has practical implications for solving problems

Newtonian OntologyNewtonian Ontology

The world is constituted out of particles

•Permanent pieces of matter

•Moving in space and time

•following fixed “laws of Nature”

Shortcomings

•No explanation for emergent phenomena: complexity, evolution, mind, life, society,

intelligence…

•No meaning or purpose

The world is constituted out of particles

•Permanent pieces of matter

•Moving in space and time

•following fixed “laws of Nature”

Shortcomings

•No explanation for emergent phenomena: complexity, evolution, mind, life, society,

intelligence…

•No meaning or purpose

Need for a process ontology

Need for a process ontology

Change is basic

•Not objects, but processes are primary

•Allows for novelty, creativity, evolution

Complexity is basic

•No primitive, independent elements Phenomena only exist in relation/interaction to

others

•Everything is connected

•Whole is more than sum of the parts

Change is basic

•Not objects, but processes are primary

•Allows for novelty, creativity, evolution

Complexity is basic

•No primitive, independent elements Phenomena only exist in relation/interaction to

others

•Everything is connected

•Whole is more than sum of the parts

Some precursorsSome precursors

Heraclitus

•You can never step in the same river twice

Process Metaphysics

•Whitehead, Bergson, Teilhard…

Valentin Turchin:

•“cybernetic ontology of action”

Heraclitus

•You can never step in the same river twice

Process Metaphysics

•Whitehead, Bergson, Teilhard…

Valentin Turchin:

•“cybernetic ontology of action”

My own historyMy own history

± 1976 relational principle, generalized natural selection

± 1984 “structural language” formalism ± 1987 discovery of cybernetics 1990’s self-organization & evolution of cooperation ± 2000 multi-agent systems ± 2007 stigmergy

2009 life is an adventure 2010-now challenges & coordination

The basic elementThe basic element

Action = elementary process

• Transforming some condition X into a different condition Y

• X → Y

Interpretations

• if X, then Y

• X = “cause”, Y = “effect”

• X = “initial state”, Y = “next state”

• X = “condition” (for action to occur), Y = “action” (creation of new condition)

Action = elementary process

• Transforming some condition X into a different condition Y

• X → Y

Interpretations

• if X, then Y

• X = “cause”, Y = “effect”

• X = “initial state”, Y = “next state”

• X = “condition” (for action to occur), Y = “action” (creation of new condition)

X

Y

Action ExamplesAction Examples

Elementary particle reaction

• n p + e- + e (Beta decay of neutron)

Chemical reaction

• 2H2 + O2 2H2O (production of water)

Causal rule

• Glass falls → Glass breaks

Elementary particle reaction

• n p + e- + e (Beta decay of neutron)

Chemical reaction

• 2H2 + O2 2H2O (production of water)

Causal rule

• Glass falls → Glass breaks

More examplesMore examples

Action of thermostat

•Temperature < 21° → switch on heating

Animal action

•Smell food → eat food

Human action

•See friend greet friend

Action of thermostat

•Temperature < 21° → switch on heating

Animal action

•Smell food → eat food

Human action

•See friend greet friend

ConditionsConditionsWhat are the conditions X and Y in X→ Y?

•Condition = distinguishable class of situations

•“state of the world” at the beginning of the action

Distinguished by the actions possible in that state

•states differ if and only if possible actions differ

•Observation/distinction is an action

Formally: state = set of all potential actions

• action performed => state changes

What are the conditions X and Y in X→ Y?

•Condition = distinguishable class of situations

•“state of the world” at the beginning of the action

Distinguished by the actions possible in that state

•states differ if and only if possible actions differ

•Observation/distinction is an action

Formally: state = set of all potential actions

• action performed => state changes

Bootstrapping logicBootstrapping logic

Action is defined as change of state

State is defined as collection of possible actions

Action is the true primitive

• State is a more complex, derived concept

• But which fits in better with our “classical” intuition

Example: n p + e- + e

• state n (neutron) defined by reactions in which it participates

• e.g. ability to decay into a proton, electron and neutrino

• proton, electron, etc. are similarly defined by the actions in which they take part

Action is defined as change of state

State is defined as collection of possible actions

Action is the true primitive

• State is a more complex, derived concept

• But which fits in better with our “classical” intuition

Example: n p + e- + e

• state n (neutron) defined by reactions in which it participates

• e.g. ability to decay into a proton, electron and neutrino

• proton, electron, etc. are similarly defined by the actions in which they take part

AgentsAgents

Agent = part of condition necessary for action

But which is not affected by action

• A + X → A + Y

• A = agent or catalyst of the action X → Y

Agents have a certain invariance or stability

• “objects” rather than processes

Agents are produced by variation and selection

• stable conditions survive longer than unstable ones

• => they will become more common

Agent = part of condition necessary for action

But which is not affected by action

• A + X → A + Y

• A = agent or catalyst of the action X → Y

Agents have a certain invariance or stability

• “objects” rather than processes

Agents are produced by variation and selection

• stable conditions survive longer than unstable ones

• => they will become more common

PhysicsPhysics

Particle = simplest possible agent

•Fermion (e.g. proton, neutron, electron…)

• Invariant during action: A + X → A + Y

•Observed via boson (e.g. photon) exchanges

•Example:

• e- → e- +

• photographic plate + → photographic plate + trace

Particle = simplest possible agent

•Fermion (e.g. proton, neutron, electron…)

• Invariant during action: A + X → A + Y

•Observed via boson (e.g. photon) exchanges

•Example:

• e- → e- +

• photographic plate + → photographic plate + trace

Space-Time Space-Time Network of actions determines “causal

structure”

• light-cone separates “time-like” from “space-like” connections

• Actions without parallel actions are “horismotic” (= “light-like”)

• Particles follow time-like trajectories

Topology of space and time can be reconstructed from this causal structure

(Kronheimer & Penrose, 1967)

Conclusion:

particles, space and time emerge from networks of actions, not vice-versa

Network of actions determines “causal structure”

• light-cone separates “time-like” from “space-like” connections

• Actions without parallel actions are “horismotic” (= “light-like”)

• Particles follow time-like trajectories

Topology of space and time can be reconstructed from this causal structure

(Kronheimer & Penrose, 1967)

Conclusion:

particles, space and time emerge from networks of actions, not vice-versa

Macroscopic CausalityMacroscopic Causality

Particular action:

• X + B (background conditions) → Y + B’

• Every X + B state is unique

General Action

• X → Y

X reduced to a general category including many unique states

Abstraction is made of the background

Either because it does not affect the action, or is invariant (agent)

Example

• Dropping + B → falling + B’

• B = gravitation, weight, object heavier than air, etc.

Particular action:

• X + B (background conditions) → Y + B’

• Every X + B state is unique

General Action

• X → Y

X reduced to a general category including many unique states

Abstraction is made of the background

Either because it does not affect the action, or is invariant (agent)

Example

• Dropping + B → falling + B’

• B = gravitation, weight, object heavier than air, etc.

DirectionalityDirectionality

Actions tend to have a preferred direction

•X → Y, but not Y → X

• In general irreversible

This produces attractors in the state space

•regions that you can enter but not leave

This implies equifinality

•Different initial states lead to the same final states

Actions tend to have a preferred direction

•X → Y, but not Y → X

• In general irreversible

This produces attractors in the state space

•regions that you can enter but not leave

This implies equifinality

•Different initial states lead to the same final states

Phase portraitPhase portrait

attractor

attractorattractor

Goal-directednessGoal-directedness

Attractors = implicit goals of actions/agents

• i.e. situations that all actions go towards

• and will return to even when perturbed

Fitness = “attractivity” of a state = underlying goal/value of all agents

Examples:

• Physics: goal = minimal potential energy

• Biology: goal = maximal survival and reproduction

• Psychology: goal = maximal happiness

• Economics: goal = maximal “utility” (benefit)

Attractors = implicit goals of actions/agents

• i.e. situations that all actions go towards

• and will return to even when perturbed

Fitness = “attractivity” of a state = underlying goal/value of all agents

Examples:

• Physics: goal = minimal potential energy

• Biology: goal = maximal survival and reproduction

• Psychology: goal = maximal happiness

• Economics: goal = maximal “utility” (benefit)

The Intentional StanceThe Intentional Stance

Action: A + X → A + Y

Agent A has

•Belief or Sensation about the situation it is in

initial condition X to which A reacts

•Intention about what action to do next

Action Y that A performs

•Desire or Goal

Attractor to which A’s actions eventually lead

Action: A + X → A + Y

Agent A has

•Belief or Sensation about the situation it is in

initial condition X to which A reacts

•Intention about what action to do next

Action Y that A performs

•Desire or Goal

Attractor to which A’s actions eventually lead

Intentional vs. causalIntentional vs. causal

Causal stance:

• A + X (cause) → A + Y (effect)

• Effect fully determined by cause => no need for goal

Intentional and causal stances are formally equivalent

• Causal stance is typical for mechanistic models

• Intentional stance is typical for “mental” explanations

Advantages of intentional stance

• Can deal with more complex and intelligent agents

• Does not require full information about causes

Since end states are to some degree independent of initial states

Causal stance:

• A + X (cause) → A + Y (effect)

• Effect fully determined by cause => no need for goal

Intentional and causal stances are formally equivalent

• Causal stance is typical for mechanistic models

• Intentional stance is typical for “mental” explanations

Advantages of intentional stance

• Can deal with more complex and intelligent agents

• Does not require full information about causes

Since end states are to some degree independent of initial states

InterpretationsInterpretations

The intentional stance can be interpreted metaphysically

Panpsychism: all phenomena have “mindlike qualities”

• E.g. particles have rudimentary “consciousness” (Chalmers)

Animism: all phenomena are “sentient” agents

In fact: interpretations are a question of personal preference

• they are all formally equivalent, even including the Newtonian interpretation

The intentional stance can be interpreted metaphysically

Panpsychism: all phenomena have “mindlike qualities”

• E.g. particles have rudimentary “consciousness” (Chalmers)

Animism: all phenomena are “sentient” agents

In fact: interpretations are a question of personal preference

• they are all formally equivalent, even including the Newtonian interpretation

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ChallengesChallenges

Intentional agents: typically living organisms or people

Basic value = maximizing fitness

Challenge = condition that potentially elicits action from the agent

•because performing that action may lead to a fitness increase

•at least relative to not performing the action

Challenges are intrinsically meaningful conditions

Intentional agents: typically living organisms or people

Basic value = maximizing fitness

Challenge = condition that potentially elicits action from the agent

•because performing that action may lead to a fitness increase

•at least relative to not performing the action

Challenges are intrinsically meaningful conditions

CognitionCognitionTackling problems (complex challenges) requires

• Selecting which challenge(s) to take on

• Selecting which actions to perform for a given challenge

Intelligence = ability to make good selections

Knowledge = interiorized decision rules:

• Anticipate challenge: if X, expect Y X → Y

• Choose action: if Y, do Z Y → Z

Planning:

• Make inference: if X, do Z X → Z

Tackling problems (complex challenges) requires

• Selecting which challenge(s) to take on

• Selecting which actions to perform for a given challenge

Intelligence = ability to make good selections

Knowledge = interiorized decision rules:

• Anticipate challenge: if X, expect Y X → Y

• Choose action: if Y, do Z Y → Z

Planning:

• Make inference: if X, do Z X → Z

Challenge TypesChallenge Types

Positive: opportunity to increase fitness

Negative: danger of losing fitness

Expected: goals, threats (“anti-goals”)

Unexpected: diversions, disturbances, affordances

Perceived: prospect

As yet invisible: mystery

Positive: opportunity to increase fitness

Negative: danger of losing fitness

Expected: goals, threats (“anti-goals”)

Unexpected: diversions, disturbances, affordances

Perceived: prospect

As yet invisible: mystery

Course of ActionCourse of Action

Intended/anticipated sequence of actions

•from present state to present goal

Will need correction because of diversions

•Disturbances → counteract

•Affordances → exploit

•Neutral diversions → change course

Intended/anticipated sequence of actions

•from present state to present goal

Will need correction because of diversions

•Disturbances → counteract

•Affordances → exploit

•Neutral diversions → change course

Course of actionCourse of action

Without diversionsWithout diversions

Course of action with diversions

Course of action with diversions

With diversionsWith diversions

Prospect and MysteryProspect and MysteryThe course of action (path ahead) is only partly anticipatable

Prospect (perceived challenges) is always mixed up with mystery (as yet invisible challenges)

The course of action (path ahead) is only partly anticipatable

Prospect (perceived challenges) is always mixed up with mystery (as yet invisible challenges)

Prospect and MysteryProspect and Mystery

agent

prospect

mystery

prospect

prospect

mystery

Course of action

?

StigmergyStigmergy

Stigmergy = stimulation of actions by the results of actions

• Primitive mechanism of coordination between actions/agents

Agent A performs action: A + X → A + Y

• X = initial challenge that elicits action

• Y = result, “trace” left by the action

There is stigmergy if Y too is a challenge

• for the same or for another agent

• in that case, Y will trigger a subsequent action

• E.g. A’ + Y → A’ + Z

Stigmergy = stimulation of actions by the results of actions

• Primitive mechanism of coordination between actions/agents

Agent A performs action: A + X → A + Y

• X = initial challenge that elicits action

• Y = result, “trace” left by the action

There is stigmergy if Y too is a challenge

• for the same or for another agent

• in that case, Y will trigger a subsequent action

• E.g. A’ + Y → A’ + Z

Propagation of challenges

Propagation of challenges

Stigmergy =>

(branching) chain of challenges producing new challenges

•E.g. A + X → A + Y, A’ + Y → A’ + Z , ...

Stigmergy =>

(branching) chain of challenges producing new challenges

•E.g. A + X → A + Y, A’ + Y → A’ + Z , ...

X Y

Z

V

U

W

S

A

A’

A

A’A

A”

A”’

A’

Example: building a house

Example: building a house

foundations

plastered

wallselectricit

y

windows

tubing

walls finishedhouse

builders carpenters electrici

ans

painters

plumbers

plasterers

walls + carpenters → house with windows (+ carpenters)

house with windows + electricians → house with electricity (+ electricians)

walls + carpenters → house with windows (+ carpenters)

house with windows + electricians → house with electricity (+ electricians)

Example: office organization

Example: office organization

CoordinationCoordination

Actions are coordinated when

•There is minimal friction

Overall loss of fitness because of interaction

E.g. conflict, obstruction

•There is maximal synergy

Overall gain in fitness because of interaction

E.g. cooperation, complementarity

Coordinated actions/agents can achieve much more together than alone

Actions are coordinated when

•There is minimal friction

Overall loss of fitness because of interaction

E.g. conflict, obstruction

•There is maximal synergy

Overall gain in fitness because of interaction

E.g. cooperation, complementarity

Coordinated actions/agents can achieve much more together than alone

Aspects of coordinationAspects of coordinationAlignment

•Actions should aim at the same targets

Division of labor (parallel, simultaneous)

•Actions should be performed by most competent agents

Workflow (sequential)

•Actions should follow each other efficiently

Aggregation

•Results of actions should be integrated into coherent whole

Alignment

•Actions should aim at the same targets

Division of labor (parallel, simultaneous)

•Actions should be performed by most competent agents

Workflow (sequential)

•Actions should follow each other efficiently

Aggregation

•Results of actions should be integrated into coherent whole

AlignmentAlignment

Actions pointing in opposite directions obstruct each other

• conflict, friction

Actions pointing towards the same target reinforce each other

• cooperation, synergy

Actions pointing in opposite directions obstruct each other

• conflict, friction

Actions pointing towards the same target reinforce each other

• cooperation, synergy

Parallel and Sequential Coordination

Parallel and Sequential Coordination

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laying

electricity

plumbing

paintingparallel

sequential

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Self-organizationSelf-organizationVariation and natural selection → increase in

fitness

•Decrease in friction

• Increase in synergy

•Emergence of coordination between actions/agents

Coordinated group of agents = system or organization, e.g.

•agents = atoms system = molecule

•agents = cells system = multicellular organism

•agents = individuals system = company

Variation and natural selection → increase in fitness

•Decrease in friction

• Increase in synergy

•Emergence of coordination between actions/agents

Coordinated group of agents = system or organization, e.g.

•agents = atoms system = molecule

•agents = cells system = multicellular organism

•agents = individuals system = company

S

IOa

b

c

d

e

f

g

h

i

j

k

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System as Network of Actions/Agents

System as Network of Actions/Agents

Some Ethical ImperativesSome Ethical Imperatives

Fundamental Value: increase fitness for all agents

• By stimulating their individual development

• By promoting the coordination of their actions, more specifically:

Maximize synergy/cooperation

• Promote complementarity / diversity

Minimize friction/conflict

• Prevent “free riders”

Facilitate self-organization

Fundamental Value: increase fitness for all agents

• By stimulating their individual development

• By promoting the coordination of their actions, more specifically:

Maximize synergy/cooperation

• Promote complementarity / diversity

Minimize friction/conflict

• Prevent “free riders”

Facilitate self-organization

Facilitators of self-organization

Facilitators of self-organization

Increased variation / diversity

• “order from noise”

Easier propagation

• More alignment → more pressure to align

Stigmergic medium

• Registers and broadcasts challenges (e.g. Wikipedia)

Hebbian learning

• Synergetic connections between actions/agents are reinforced

Become easier to use next time

Increased variation / diversity

• “order from noise”

Easier propagation

• More alignment → more pressure to align

Stigmergic medium

• Registers and broadcasts challenges (e.g. Wikipedia)

Hebbian learning

• Synergetic connections between actions/agents are reinforced

Become easier to use next time

Practical applicationsPractical applications

Self-organizing technologies

• Artificial agents, action rules, medium

• E.g. computer simulations, self-configuring engineering systems, networks of mobile sensors…

Mobilization systems

• Produce motivating challenges for individuals

Using flow and other criteria: clear goals, immediate feedback, challenges adapted to abilities, variation in challenges, …

Minimize boredom, anxiety, confusion, procrastination…

• Facilitate coordination

E.g. via alignment of goals and terminology, stigmergy and propagation of challenges

Self-organizing technologies

• Artificial agents, action rules, medium

• E.g. computer simulations, self-configuring engineering systems, networks of mobile sensors…

Mobilization systems

• Produce motivating challenges for individuals

Using flow and other criteria: clear goals, immediate feedback, challenges adapted to abilities, variation in challenges, …

Minimize boredom, anxiety, confusion, procrastination…

• Facilitate coordination

E.g. via alignment of goals and terminology, stigmergy and propagation of challenges

Conclusion: benefits of action ontology

Conclusion: benefits of action ontology

• Generalization of Newtonian ontology

• Transcendence of mind-matter dualism

• Explanation for emergence, goal-directedness, evolution…

• A very simple and practical philosophy

• Foundations for metaphysics, epistemology and ethics

• A framework for transdisciplinary unification

• A methodology for tackling complex problems

• A basis for building meaningful narratives

• Thus, bridging the gap between the “two cultures”

• Generalization of Newtonian ontology

• Transcendence of mind-matter dualism

• Explanation for emergence, goal-directedness, evolution…

• A very simple and practical philosophy

• Foundations for metaphysics, epistemology and ethics

• A framework for transdisciplinary unification

• A methodology for tackling complex problems

• A basis for building meaningful narratives

• Thus, bridging the gap between the “two cultures”