Unraveling Stigmergy: Key to Efficient Digital Ecosystems

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

2. 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?

3. The concept of stigmergy • 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)

4. Basic principle • 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

5. Mechanism • 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

6. Example: termite hill construction • 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

7. Ant trail networks • 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

8. Ant trail networks

9. Quantitative ↔ qualitative • 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

10. Collaboration in Wikipedia • 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 → ...

12. Medium as shared memory • 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

13. Coordination • 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

14. Advantages of stigmergy • No need for: • simultaneouspresence 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 • precisesequencing of actions (workflow) • next actions are triggered by previous ones

15. 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

16. 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

17. 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 Agent Input Output

18. DBE as network

19. 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

20. 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