Stigmergy: a fundamental paradigm for digital ecosystems?

Stigmergy: a fundamental paradigm for digital ecosystems?

Francis Heylighen

Evolution, Complexity and Cognition groupVrije Universiteit Brussel

Francis Heylighen

Evolution, Complexity and Cognition groupVrije Universiteit Brussel

Digital EcosystemDigital Ecosystem

•Complex, self-organizing system

•Agents: businesses, organizations, individuals...

•exchanging information, services, goods

•co-evolving, mutually adapting

•Supported by shared ICT infrastructure

•digital environment or medium

•How to design an efficient digital medium for DE?

•Complex, self-organizing system

•Agents: businesses, organizations, individuals...

•exchanging information, services, goods

•co-evolving, mutually adapting

•Supported by shared ICT infrastructure

•digital environment or medium

•How to design an efficient digital medium for DE?

• Introduced by the entomologist Grassé in the 1950’s

• to explain activity of social insects

•such as termites, ants and wasps.

•apparently complex and coordinated

•yet individuals very dumb

•→ effective self-organization

•Now popular in Multi-Agent Systems (robots, simulations)

• Introduced by the entomologist Grassé in the 1950’s

• to explain activity of social insects

•such as termites, ants and wasps.

•apparently complex and coordinated

•yet individuals very dumb

•→ effective self-organization

•Now popular in Multi-Agent Systems (robots, simulations)

The concept of stigmergy

The concept of stigmergy

•Greek etymology

•stigma = stimulus, sign

•ergon = work

•work performed by an agent leaves a trace in the environment or medium

•perceiving the trace stimulates another agent to perform further work

•thus extending or elaborating previous work

•Greek etymology

•stigma = stimulus, sign

•ergon = work

•work performed by an agent leaves a trace in the environment or medium

•perceiving the trace stimulates another agent to perform further work

•thus extending or elaborating previous work

Basic principleBasic principle

•Perceived condition function as "stimulus",

•action as "response" or "work"

•Feedback loop:

•condition → action → new condition → new action ...

•action changes medium

•change is perceived → new condition

•each action corrects or builds upon the previous one

•Perceived condition function as "stimulus",

•action as "response" or "work"

•Feedback loop:

•condition → action → new condition → new action ...

•action changes medium

•change is perceived → new condition

•each action corrects or builds upon the previous one

MechanismMechanism

first termites drop mud randomly

later termite tend to drop mud on already present mud

•positive feedback: mud → more mud

→ the mud heap grows into a column

columns tend to grow towards each other

→ cathedral-like structure with arches

first termites drop mud randomly

later termite tend to drop mud on already present mud

•positive feedback: mud → more mud

→ the mud heap grows into a column

columns tend to grow towards each other

→ cathedral-like structure with arches

Example: termite hill construction

Example: termite hill construction

Ants coming back from food source leave pheromone trace

Ants searching for food preferentially follow pheromone trail

•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

•external memory of food locations

•adapts constantly to new circumstances

Ants coming back from food source leave pheromone trace

Ants searching for food preferentially follow pheromone trail

•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

•external memory of food locations

•adapts constantly to new circumstances

Ant trail networksAnt trail networks

Ant trail networksAnt trail networks

Quantitative stigmergy:

•trace changes probability or amount of further action

•e.g. amount of mud for termite, or of pheromone for ant

Qualitative stigmergy:

•trace elicits new type of action

•e.g. Wikipedia

Quantitative stigmergy:

•trace changes probability or amount of further action

•e.g. amount of mud for termite, or of pheromone for ant

Qualitative stigmergy:

•trace elicits new type of action

•e.g. Wikipedia

Quantitative ↔ qualitativeQuantitative ↔ qualitative

•Person A writes text on topic X

•action

•Person B reads text

•stimulus, perception

•Person B thinks text can be improved

•Person B then adds or corrects text

•qualitatively new action

•Positive feedback:

•more edits → better text → more readers → more edits → ...

•Person A writes text on topic X

•action

•Person B reads text

•stimulus, perception

•Person B thinks text can be improved

•Person B then adds or corrects text

•qualitatively new action

•Positive feedback:

•more edits → better text → more readers → more edits → ...

Collaboration in Wikipedia

Collaboration in Wikipedia

Actions leave signs in medium

• information is reliably stored

• information is easily retrieved

➥ Signs function as external memory

•accessible by all agents

•shared between all agents

Topological differentiation of space

•different regions accumulate different types of signs

Actions leave signs in medium

• information is reliably stored

• information is easily retrieved

➥ Signs function as external memory

•accessible by all agents

•shared between all agents

Topological differentiation of space

•different regions accumulate different types of signs

Medium as shared memoryMedium as shared memory

•Coordinating different actions requires knowing which action is to be done when by whom

•This is difficult for agents with limited memory

•especially when the action pattern is very complex

•External memory overcomes this problem

•This makes possible a highly organized and intelligent pattern of activity

•performed by agents with very incomplete knowledge

•Coordinating different actions requires knowing which action is to be done when by whom

•This is difficult for agents with limited memory

•especially when the action pattern is very complex

•External memory overcomes this problem

•This makes possible a highly organized and intelligent pattern of activity

•performed by agents with very incomplete knowledge

CoordinationCoordination

No need for:

•simultaneous presence of agents

• interaction can be asynchronous

•direct communication between agents

•agents can be anonymous, unaware of each other

•planning or prediction of activities

•agents can be ignorant of what happens next

•precise sequencing of actions (workflow)

•next actions are triggered by previous ones

No need for:

•simultaneous presence of agents

• interaction can be asynchronous

•direct communication between agents

•agents can be anonymous, unaware of each other

•planning or prediction of activities

•agents can be ignorant of what happens next

•precise sequencing of actions (workflow)

•next actions are triggered by previous ones

Advantages of stigmergyAdvantages of stigmergy

No need for: imposed division of labourNo 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

•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

•since they are most stimulated to act

•and can do the job with least effort

E.g. collaboratively developing Wikipedia page or open-source application

People tend to check pages/modules they are interested in

•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

•since they are most stimulated to act

•and can do the job with least effort

Open Knowledge Ecosystems

Open Knowledge Ecosystems

•Agents use and produce knowledge

•knowledge publicly available

• in shared external memory

•e.g. Wikipedia

•Everybody can use the knowledge freely

•Everybody can contribute freely

•Agents use and produce knowledge

•knowledge publicly available

• in shared external memory

•e.g. Wikipedia

•Everybody can use the knowledge freely

•Everybody can contribute freely

Business EcosystemsBusiness Ecosystems

•Agents (SMEs) supply goods or services (output)

•but require (demand) resources (input)

•Agents process input into output

•Problem: match input of one agent to output of other agent

•Agents (SMEs) supply goods or services (output)

•but require (demand) resources (input)

•Agents process input into output

•Problem: match input of one agent to output of other agent

Agent

OutputOutputInputInput

DBE as networkDBE as network

Virtual MarketsVirtual Markets•Demand and Supply posted on public

medium

•need A (qualitative), am willing to pay X (quantitative)

•can supply B (qual.), for the price Y (quant.)

•Agents browse through medium to find supply that best matches their demand, or vice-versa

•Law of supply and demand : prices should automatically adjust to make supply match demand

•Demand and Supply posted on public medium

•need A (qualitative), am willing to pay X (quantitative)

•can supply B (qual.), for the price Y (quant.)

•Agents browse through medium to find supply that best matches their demand, or vice-versa

•Law of supply and demand : prices should automatically adjust to make supply match demand

Required technologiesRequired technologies

•Shared digital medium: open, non-proprietary

•Ontology for characterizing available demand/supply offers (qualitative)

•Bidding algorithms to increase/decrease price when no reaction is forthcoming (quantitative)

•Feedback for rewarding qualitatively best offers

•Software agents for finding most attractive demand/supply opportunities

•given knowledge of own preferences/expertise

•Shared digital medium: open, non-proprietary

•Ontology for characterizing available demand/supply offers (qualitative)

•Bidding algorithms to increase/decrease price when no reaction is forthcoming (quantitative)

•Feedback for rewarding qualitatively best offers

•Software agents for finding most attractive demand/supply opportunities

•given knowledge of own preferences/expertise