1. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-1 Social Embedding - Origins, Occurrence and Opportunities A Tutorial on Socially Intelligent Agents At SAB 2002 10 th August, Edinburgh by Kerstin Dautenhahn and Bruce Edmonds

2. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-2 Rough Outline of the Tutorial Start Part 1: Social Embedding - The Societal Viewpoint (BE) (Individual  ) Society Coffee Break Part 2: Social Embedding - Implications for the Individual and its Interactions (KD) (Society  ) Individual End

3. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-3 Social Embedding - Origins, Occurrence and Opportunities Part 1 The Societal Viewpoint Bruce Edmonds Centre for Policy Modelling Manchester Metropolitan University http://bruce.edmonds.name

4. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-4 Outline of Part 1 - The societal viewpoint Nature of social embedding Causes of social embedding Consequences of social embedding Example: a stock market Approaches to understanding social embedding systems Social embedding in existing systems (Outline)

5. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-5 Where the internal inference is sufficient as the model for action Agent Token environment Internal process ActionPerception (Nature of SE)

6. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-6 Brooks’ (1991), and later others’, critique of GOFAI Slow, off-line deliberation Emphasis on internal processing One-shot decision making Unnecessary generality of approach Symbolic, representational models Lack of practical success Lack of relation a real problem Lack of embodiment (Nature of SE)

7. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-7 Physically situated (Nature of SE) Focus on specific physical contingencies Frequent sampling of physical environment Close feedback via physical environment Goal directed, interactive learning of physical environment Subsumption architecture Socially situated Focus on human social contingencies Frequent sampling of environment (gossip) Close feedback via social interaction Goal directed, interactive learning of self and society Layers of social skills and abilities

8. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-8 Vera and Simon (1993) on what situated action is The utilisation of external rather than internal representations via the functional modelling of the affordances provided by the environment which allows the paring down of the internal representation so that its processing can occur in real-time. (Nature of SE)

9. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-9 Where external causation is also part of the model for action Agent Model of the environment including external causation Internal process ActionPerception Causation (Nature of SE)

10. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-10 Suchman (1987) on situatedness … the contingence of action on a complex world … [is not] an extraneous problem … but... an essential resource that makes knowledge possible and gives action its sense. … the coherence of action is not adequately explained by either preconceived cognitive schema or institutionalised social norms. Rather the organisation of situated action is an emergent property of moment-by- moment interactions … (Nature of SE)

11. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-11 Granovetter (1985) - embeddedness … extent to which … action is embedded in structures of social relations … [not] … an “undersocialized” or atomized-actor explanation of such action … [but] … “oversocialized” accounts are paradoxically similar in their neglect of ongoing structure of social relations (Nature of SE)

12. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-12 Moved from modelling with a unitary environment … (Nature of SE) Agent Environment

13. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-13 … to modelling with some of the interactions between agents (Nature of SE) Agent Environment composed of agents

14. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-14 Social embeddedness as the appropriate level of modelling Difference in the model goodness according to modelling goals and criteria (Nature of SE)

15. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-15 Some examples of differing degrees of embeddedness Neo-classical economic model of a market, each individual has negligible impact An agent interacting with a community via negotiation with one or two representatives A termite in its colony - interacting via a process of stigmergy The movement of people at a party (Nature of SE) Low embeddedness High embeddedness

16. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-16 Parallel and interacting evolutionary processes Biological Evolution Neural Selection –development and selection of neural structure –development and selection of behaviours Social –cultural adaptation to fit biological niches –memetic/imitative processes –evolution of language E.g. Donald: Origins of the modern mind (1991) (Causes of SE)

17. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-17 Co-evolution of social & individual Bootstrapping process starting from the ‘needs’ of individuals in various ways, e.g.: –reciprocal altruism, kin selection, symbiosis Formation of inter-individual ecology Formation of groups (e.g. using tags) Individuals evolve/learn new behaviours in response to new social environment etc. E.g. Deacon: The symbolic species (1997) (Causes of SE)

18. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-18 Cognitive “arms races” Populations of individuals in competition Advantage in out-modelling competitors (e.g. partially predicting their behaviour) Advantage in using more social knowledge (e.g. to form groups, alliances etc.) Modelling and knowledge “arms race” Resulting in complex social knowledge and social models of each other and hence deep social embedding (Causes of SE)

19. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-19 Facilitators of social embedding Rich environment to exploit A transformable resource Ability of participants to learn/evolve Open-ended learning ability Partial competition for resources Ability to observe other’s actions Ability to recognise particular individuals (e.g. via names) Ability to recognise groups (e.g. via tags) (Causes of SE)

20. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-20 Impossibility of total modelling/strategic burden In society of (partially competing) peers cognitive modelling/strategic resources roughly equal (small differences matter) social web and heuristics also complexified complete modelling of social environment beyond any one individual’s capacity leads to use of proxies of what is happening which itself leads to further embedding etc. (SE Consequences)

21. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-21 Individual’s coping strategies Imitation Watch what a particular individual does Follow an identifiable trend (fashion) Concentrate on interaction with one’s group Learn from other’s failures Frequent sampling of social environment Use of several local social networks Referral and passing on of social information (SE Consequences)

22. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-22 Social results of embeddedness Heterogeneity and specialisation Dense & locally connected social networks Dynamic group formation and dissolution Efficient reuse of information Social artefacts/styles Susceptibility to sub-optimal lock-in Resistant to outsiders Rules/norms to simplify interaction? E.g. academic fields (SE Consequences)

23. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-23 Example: Stock market (set-up) Competing traders Can observe each other’s actions Local social information networks Open-ended & competitive learning by individuals Trade by buying or selling a number of stocks at current price Market maker set prices according to demand hence actions change prices (Example)

24. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-24 Agent-based stock market model Following Palmer et. al (1991) but with social hooks for naming, imitation and with open-ended (GP-based) learning Trader-1 Trader-2 Trader-3 If [trader-1 bought] then [sell 10] else [[do as last time] * 90%] one model of trader-3 (Example) Observation of each other

25. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-25 Stock market model (outcomes) Cognitive “arms races” Social embedding (dense web of referral) Reuse and spread of information Proxies: market “moods” and “leaders” Emergent unpredictability & heterogeneity Not random (law of large numbers fail) - Kaneko (1990) Globally coupled chaos... (Example) Size Price variance (scaled by size) SE market model Model with random noise

26. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-26 A fragmentation of sources Social Science Ethology Ethnology Biology Ecology Cognitive Science Computer Science Folk psychology (Approaches)

27. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-27 A problem with modelling socially embedded systems (Approaches) t+1t+2etc. time=t Design of system ? No easily accessible micro  macro explanation!

28. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-28 A priori vs. descriptive and micro vs. macro (Approaches) a prioridescriptive micro macro Utility optimisation Planning/inference/lear ning algorithms Designed agents Psychology Cognitive science Ethology Equilibrium economics Population dynamics Evolutionary algorithms Pareto optima Simulation outcomes Descriptive statistics Example histories Ecology Ethnology/Anthropology

29. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-29 Existing modelling approaches (1) Descriptive –Good as sources & validation, but difficult to generalise from Economic –Puts techniques above problem (e.g. law of large numbers, single utilities, only price etc.) Game theory –Only soluble with a small number of discrete choices, no modelling “arms races” Population dynamics –Does not (really) relate to micro behaviour (Approaches)

30. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-30 Existing modelling approaches (2) Sociological Theory –Rich but vague, difficult to unambiguously relate to any specific case, more of a framework Artificial life computational models –Good on process, can be disconnected Physics-derived models –Can be useful for post hoc encapsulation Artificial Intelligence/Machine Learning –Useful techniques but strongly a priori (Approaches)

31. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-31 Existing modelling approaches (3) Descriptive computational simulation –Good but difficult to get enough observations and data to motivate design and validate Robotic experiments –Good but robots are costly and unreliable, experiments take a lot of time and effort Experiments with groups of animals –Valid, but almost impossible to do, many ethical considerations and no re-running of trials (Approaches)

32. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-32 Ants and Termites Stigmergy, Grassé (1959) –Interaction of individuals via effects on their environment (e.g. pheromone trails, walls) –Set of individual behaviours only makes sense in context of others’ actions in the environment –No named individuals (except types of individuals and perhaps the queen) –No 1-1 social relationships –Each behaviour relatively simple –Combinations of behaviours quite complex (Social Systems)

33. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-33 Song Birds Grant & Grant (1997 ) –Particular songs imitated and modified –Young males imprint on song of father –Hybrid females breed with males with a similar song as father –Regional dialects of songs developed –But discrimination is a weak effect –Not clear other birds are recognised by song and hence any local embedding (Social Systems)

34. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-34 Apes Different species of ape differ a lot in terms of social sophistication Learning via imitation (some species) Development of complex web of specific social relationships and Manipulation of these relationships for individual advantage (social “arms race”) Ask Kerstin! (Social Systems)

35. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-35 Humans Social embedding seems eminently plausible for many situations Suggested conditions and outcomes from models frequently all present Embeddedness (following Granovetter) has strong use as part of explanatory framework But no conclusive studies/evidence yet!

36. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-36 Rest of Body An analogy between social & physical embodiment Brain Nervous system Physical environment Extended social web Social environment Person Near social web

37. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-37 Robots Imitation and learning among robots Many interesting experiments approaching the sociality of robots Conditions for social embeddedness among robots probably not met yet! But ask Kerstin!

38. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-38 Mixed Societies Humans and animals –Much biological/ecological embedding –Some social inclusion of domesticated animals –Limited embedding, except occasionally between humans and great apes Humans and robots –Presently science fiction –Most likely to first occur via the internet –Necessary for good integration of robots

39. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-39 Conclusion of part 1 Including the details of (at least some of) the individual social relationships in models can make a difference to outcomes This is necessary in order to adequately model some aspects of some systems Social embedding seems to be a feature of several social systems Its presence would have definite consequences It does not require high level cognition (e.g. complex inference or planning) A special case of embedding in general

40. Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-40 The End of Part 1 and coffee! Some relevant web pages - These slides (and handout) will be at: bruce.edmonds.name/siatut Socially Intelligent Agents home page: homepages.feis.herts.ac.uk/ ~comqkd/aaai-social.html Centre for Policy Modelling (where I work, does descriptive agent-based social simulation): cfpm.org