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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
10th August, Edinburgh
by Kerstin Dautenhahn and Bruce Edmonds
Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-2
Rough Outline of the Tutorial
StartPart 1: Social Embedding - The Societal
Viewpoint (BE)
(Individual ) Society
Coffee BreakPart 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-3
Social Embedding- Origins, Occurrence and Opportunities
Part 1
The Societal Viewpoint
Bruce EdmondsCentre 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-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-5
Where the internal inference is sufficient as the model for action
Agent
Token environment
Internal process
ActionPerception
(Nature of SE)
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)
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
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)
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)
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)
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)
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
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
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)
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
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)
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)
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)
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)
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)
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)
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)
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)
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-1Trader-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
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
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)
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+1 t+2 etc.time=t
Design of system ?
No easily accessible
micro macro explanation!
Tutorial on Social Embeddedness: part 1 - the societal viewpoint, SAB 2002, bruce.edmonds.name/siatut slide-28
A priori vs. descriptive andmicro vs. macro
(Approaches)
a priori descriptive
micro
macro
Utility optimisation
Planning/inference/learning algorithms
Designed agents
Psychology
Cognitive science
Ethology
Equilibrium economicsPopulation dynamicsEvolutionary algorithmsPareto optimaSimulation outcomes
Descriptive statisticsExample historiesEcologyEthnology/Anthropology
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)
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)
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)
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)
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)
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)
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!
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 environmentPerson
Near social web
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!
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
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
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