3 Characterising Social Embeddedness

3.3 Being Socially Embedded

Extending the above characterisations of situatedness, I want to say that an agent is socially embedded in a collection of other agents to the extent that it is more appropriate to model that agent as part of the total system of agents and their interactions as opposed to modelling it as a single agent that is interacting with an essentially unitary environment. Thus saying an agent is socially embedded is stronger than saying it is merely socially situated. I have characterised social embeddedness as a construct which depends on one's modelling goals, since these goals will affect the criteria for the appropriateness of models. It can be read as contrasting modelling agent interaction from an internal perspective (the thought processes, beliefs etc.) with modelling from external vantage (messages, actions, structures etc.). This is illustrated below in figure 3.

Figure 3. Social embeddedness as the appropriate level of modelling

This is not an extreme `relativist' position since, if one fixes the modelling framework and criteria for model selection, the social embedding of agents within a collection of agents can sometimes be unambiguously assessed. When the modelling framework is agreed, the object of modelling (in this case `social systems') will constrain the models that fit the framework. If one is extremely careful (and lucky) it might entail a unique model - in such cases we can safely project the social embeddedness upon the social system itself. Note however, that this projective attribution onto the social system is a post-hoc attribution that can only occur unambiguously in special circumstances. Usually there will be many arbitrary choices involved in the modelling of the social phenomena, so that the model (and hence the social embeddedness) is underdetermined by the phenomena itself. It is for this reason that it is more useful to define the social embeddedness with respect to model properties and use the association of the best model (by the chosen model selection criteria) with the phenomena itself as a means of inferring properties on the object system.

According to this account the social embedding is dependent on the modelling framework. Such a modelling framework includes the language of model representation, the model selection criteria and the goals of modelling. Frequently such a framework is implicitly agreed but not always. I have not the space here to fully specify what such a framework entails, for more details on this see [13, 25].

Notice that criteria for model acceptability can include many things other than just its predictive accuracy, for example: complexity [13]. It is the inevitability of these other concerns which forces us to relativise this approach as one concerning the appropriateness of our constructs (along with the different modelling goals and frameworks). For example, a computer may be able to find obscure and meaningless models which (for computational purposes) separate out the behaviour of a single agent from its society (using something like genetic programming), which are totally inaccessible to a human intelligence. Also the modelling framework is indispensable; for example, an agent may not be at all embedded from an economic perspective but very embedded from the perspective of kinship relations.

Let us consider some examples to make this a little clearer.

Firstly a neo-classical economic model of interacting agents where each of these agents individually has a negligible effect on its environment, which would mean that a model of the whole system could be easily transformed into one of a representative agent interacting with an economic environment*1. Here one would say that each agent was not socially embedded since there is little need to model the system as a whole in order to successfully capture the agent's behaviour.

Secondly where an agent which interacts with a community via a negotiation process with just a few of the other agents. Here a model which just considers an agent, its beliefs and its interaction with these few other agents will usually provide a sufficient explanation for all that occurs but there may still be some situations in which interactions and causal flows within the whole community will become significant and result in surprising local outcomes. Here one could meaningfully attribute a low level of social embeddedness.

Thirdly, the behaviour of a termite. It is possible to attempt to account for the behaviour of an termite in terms of a set of internal rules in response to its environment, but in order for the account to make any sense to us it must be placed in the context of the whole colony. No one termite repairs a hole in one of its tunnels only the colony of termites (via a process of stigmergy: [19]). Here one could say that the ants were socially situated but not socially embedded, since one can model the system with an essentially unitary model of the environment, which each of the ants separately interact with.

Finally, in modelling the movements of people at a party, it is possible that to get any reasonably accurate results one would have to include explicit representations of each person and their relationship with each of the others present. This would represent a high level of social embeddedness.

At first sight this seems a strange way to proceed; why not define social embeddedness as a property of the system, so that the appropriate modelling choices fall out as a result? The constructivist approach to characterising social embedding, outlined above, results from my modelling goals. I am using artificial agents to model real social agents (humans, animals, organisations etc.). So it is not enough that the outcomes of the model are verified and the structure validated (as in [26]) because I also wish to characterise the emergent process in a meaningful way - for it is these processes that are of primary interest. This contrasts with the `engineering approach' where the goal is different - there one is more interested in ensuring certain specified outcomes using interacting agents. When observing or modelling social interaction this meaning is grounded in the modelling language, modelling goals and criteria for model acceptability (this is especially so for artificial societies). The validation and verification of models can not be dispensed with, since they allow one to decide which are the candidate models, but most of the meaning comes from the modelling framework. In simpler physical situations it may be possible to usefully attribute phenomena to an external reality but in social modelling we have to make too many choices in order to make progress. The proof of this particular pudding will ultimately be in the eating; whether this approach helps us obtain useful models of social agents or not.

The idea of social embedding is a special case of embedding in general - the `social' bit comes from the fact we are dealing with collections of parts that are worthy of being called agents.

Capturing Social Embeddedness: a constructivist approach - Bruce Edmonds - 30 OCT 98

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