Challenges, Agents and
Coordination:
how an action ontology can
help us tackle both practical
and foundational problems
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Francis Heylighen
Evolution, Complexity and Cognition
group
Vrije Universiteit Brussel
Ontology
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 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
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
Some precursors
Heraclitus
•
You can never step in the same river twice
Process Metaphysics
•
Whitehead, Bergson, Teilhard…
Valentin Turchin:
•
“cybernetic ontology of action”
My own history
± 1976
± 1984
± 1987
1990Õs
± 2000
± 2007
2009
2010-now
relational principle, generalized natural
selection
Òstructural languageÓformalism
discovery of cybernetics
self-organization & evolution of cooperation
multi-agent systems
stigmergy
life is an adventure
challenges & coordination
The basic element
Action = elementary process
•
Transforming some condition X into a different
condition Y
•
X→Y
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
Action 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
More examples
Action of thermostat
•
Temperature < 21° → switch on heating
Animal action
•
Smell food → eat food
Human action
•
See friend  greet friend
Conditions
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 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
Agents
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
Physics
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
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 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.
Directionality
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 portrait
attractor
attractor
attractor
Goal-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)
The Intentional Stance
Action:
A+X→A+Y
Agent A has
• Belief or Sensation about the situation it is in

•
Intention about what action to do next

•
initial condition X to which A reacts
Action Y that A performs
Desire or Goal

Attractor to which A’s actions eventually lead
Intentional 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
Interpretations
The intentional stance can be interpreted metaphysically
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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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Challenges
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
Cognition
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
if X, do Z
X→Z
Planning:
•
Make inference:
Challenge 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
Course 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
Course of action
Without diversions
Course of action
with diversions
With diversions
Prospect and Mystery
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 Mystery
prospect
mystery
Course of action
?
prospect
mystery
prospect
agent
Stigmergy
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
Stigmergy =>
(branching) chain of challenges producing new
challenges
•
E.g. A + X → A + Y, A’ + Y → A’ + Z , ...
Z
U
A”
X
Y
A
A’
S
A’
A
A”’
A
W
A’
V
Example: building a house
tubing
plumbers
builders
foundations
carpente
rs
electricia
walls
windows
ns
plasterer
s
painters
plastered
walls
finished
house
electricity
walls + carpenters → house with windows (+ carpenters)
house with windows + electricians → house with electricity
(+ electricians)
Example: office organization
Coordination
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 coordination
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
Alignment
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
laying
electricity
roofing
parallel
Qu i c k Ti m e ™ a n d a
No n e d e c o m p re s s o r
a re n e e d e d to s e e th i s p i c tu re .
plumbing
plastering
painting
sequential
Self-organization
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
organism
system = multicellular
System as Network of Actions/Agents
E
f
k
l
c
e
j
h
I
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a
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d
g
b
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Some 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
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
Practical 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
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”