Social Embedding - Origins, Occurrence and Opportunities

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


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


Social Embedding- Origins, Occurrence and Opportunities

Part 1

The Societal Viewpoint

Bruce EdmondsCentre for Policy Modelling

Manchester Metropolitan University

http://bruce.edmonds.name


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)


Where the internal inference is sufficient as the model for action

Agent

Token environment

Internal process

ActionPerception

(Nature of SE)


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)


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


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)


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)


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)


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)


Moved from modelling with a unitary environment …

(Nature of SE)

Agent

Environment


… to modelling with some of the interactions between agents

(Nature of SE)

Agent

Environment composed of agents


Social embeddedness as the appropriate level of modelling

Difference in the model goodness according to modelling goals and criteria

(Nature of SE)


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


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)


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)


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)


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)


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)


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)


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)


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)


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


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


A fragmentation of sources

• Social Science

• Ethology

• Ethnology

• Biology

• Ecology

• Cognitive Science

• Computer Science

• Folk psychology

(Approaches)


A problem with modelling socially embedded systems

(Approaches)

t+1 t+2 etc.time=t

Design of system ?

No easily accessible

micro macro explanation!


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


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)



• 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)



• 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)


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)


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)


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)


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!


Rest of Body

An analogy between social & physical embodiment

Brain

Nervous system

Physical environment

Extended social web

Social environmentPerson

Near social web


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!


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


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


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

Documents

Social Embedding - Origins, Occurrence and Opportunities