Stigmergy:
a fundamental paradigm for
digital ecosystems?
Francis Heylighen
Evolution, Complexity and Cognition group
Vrije Universiteit Brussel
Digital Ecosystem
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Complex, self-organizing system
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Agents: businesses, organizations, individuals...
•
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exchanging information, services, goods
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co-evolving, mutually adapting
Supported by shared ICT infrastructure
•
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digital environment or medium
How to design an efficient digital medium for DE?
The concept of stigmergy
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Introduced by the entomologist
Grassé in the 1950’s
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to explain activity of social insects
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such as termites, ants and wasps.
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apparently complex and
coordinated
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yet individuals very dumb
• → effective self-organization
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Now popular in Multi-Agent
Systems (robots, simulations)
Basic principle
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Greek etymology
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stigma = stimulus, sign
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ergon = work
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work performed by an agent leaves a trace in the
environment or medium
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perceiving the trace stimulates another agent to
perform further work
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thus extending or elaborating previous work
Mechanism
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Perceived condition function as "stimulus",
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action as "response" or "work"
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Feedback loop:
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condition → action → new condition → new action ...
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action changes medium
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change is perceived → new condition
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each action corrects or builds upon the previous
one
Example: termite hill construction
•first termites drop mud randomly
•later termite tend to drop mud on
already present mud
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positive feedback: mud → more
mud
•→ the mud heap grows into a
column
•columns tend to grow towards each
other
•→ cathedral-like structure with
arches
Ant trail networks
•Ants coming back from food source leave pheromone trace
•Ants searching for food preferentially follow pheromone trail
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preference increases with strength of trail
•Strong trails get reinforced as more ants use them
•Trails to exhausted food sources evaporate
•Result: network of trails connecting food sources in most efficient way
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external memory of food locations
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adapts constantly to new circumstances
Ant trail networks
Quantitative ↔ qualitative
•Quantitative stigmergy:
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trace changes probability or amount of further
action
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e.g. amount of mud for termite, or of pheromone
for ant
•Qualitative stigmergy:
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trace elicits new type of action
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e.g. Wikipedia
Collaboration in Wikipedia
• Person A writes text on topic X
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•
action
Person B reads text
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stimulus, perception
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Person B thinks text can be improved
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Person B then adds or corrects text
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qualitatively new action
• Positive feedback:
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more edits → better text → more readers → more edits → ...
Medium as shared memory
•Actions leave signs in medium
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information is reliably stored
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information is easily retrieved
•➥ Signs function as external memory
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accessible by all agents
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shared between all agents
•Topological differentiation of space
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different regions accumulate different types of
signs
Coordination
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Coordinating different actions requires knowing
which action is to be done when by whom
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This is difficult for agents with limited memory
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especially when the action pattern is very
complex
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External memory overcomes this problem
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This makes possible a highly organized and
intelligent pattern of activity
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performed by agents with very incomplete
knowledge
Advantages of stigmergy
•No need for:
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simultaneous presence of agents
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direct communication between agents
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agents can be anonymous, unaware of each other
planning or prediction of activities
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interaction can be asynchronous
agents can be ignorant of what happens next
precise sequencing of actions (workflow)
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next actions are triggered by previous ones
No need for: imposed division of labour
•E.g. collaboratively developing Wikipedia page or
open-source application
•People tend to check pages/modules they are
interested in
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and therefore tend to have some expertise in
•Non-experts are not inclined to change page/module
•→ tasks are preferentially performed by the most
expert
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since they are most stimulated to act
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and can do the job with least effort
Open Knowledge
Ecosystems
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Agents use and produce knowledge
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knowledge publicly available
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in shared external memory
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e.g. Wikipedia
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Everybody can use the knowledge freely
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Everybody can contribute freely
Business Ecosystems
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Agents (SMEs) supply
goods or services (output)
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but require (demand)
resources (input)
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Agents process input into
output
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Problem: match input of
one agent to output of
other agent
Agent
Input
Output
DBE as network
Virtual Markets
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Demand and Supply posted on public medium
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need A (qualitative), am willing to pay X (quantitative)
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can supply B (qual.), for the price Y (quant.)
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Agents browse through medium to find supply that best
matches their demand, or vice-versa
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Law of supply and demand : prices should
automatically adjust to make supply match demand
Required technologies
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Shared digital medium: open, non-proprietary
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Ontology for characterizing available
demand/supply offers (qualitative)
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Bidding algorithms to increase/decrease price
when no reaction is forthcoming (quantitative)
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Feedback for rewarding qualitatively best offers
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Software agents for finding most attractive
demand/supply opportunities
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given knowledge of own
preferences/expertise