Stigmergy: a fundamental paradigm for digital ecosystems? Francis Heylighen Evolution, Complexity and Cognition group Vrije Universiteit Brussel Francis Heylighen Evolution, Complexity and Cognition group Vrije Universiteit Brussel

Digital 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

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 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 Mechanism

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

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 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 ↔ 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

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 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 Coordination

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 stigmergy

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 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 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 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 OutputInput

DBE as network

Virtual 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 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