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Social Embedding - Origins, Occurrence and Opportunities

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. Rough Outline of the Tutorial. Start Part 1: Social Embedding - The Societal Viewpoint (BE)

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Social Embedding - Origins, Occurrence and Opportunities

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

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

  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 Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-3

  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) Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-4

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

  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) Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-6

  7. 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 Physically situated • Focus on specific physical contingencies • Frequent sampling of physical environment • Close feedback via physical environment • Goal directed, interactive learning of physical environment • Subsumption architecture (Nature of SE) Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-7

  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) Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-8

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

  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) Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-10

  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) Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-11

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

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

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

  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 Low embeddedness High embeddedness (Nature of SE) Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-15

  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) Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-16

  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) Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-17

  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) Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-18

  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) Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-19

  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) Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-20

  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) Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-21

  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) Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-22

  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) Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-23

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

  25. SE market model Price variance (scaled by size) Model with random noise Size 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) Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-25

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

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

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

  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) Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-29

  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) Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-30

  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) Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-31

  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) Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-32

  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) Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-33

  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) Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-34

  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! Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-35

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

  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! Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-37

  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 Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-38

  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 Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-39

  40. The End of Part 1 andcoffee! 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 Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-40

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