2 Social Embeddedness

2.1 Characterising Social Embeddedness

This split is now mirrored in the world of artificial agents. In traditional AI it is the individual agent's mental processes and behaviour that are modelled and this has been extended to considerations of the outcomes when such autonomous agents interact. In Artificial Life and computational organisational theory the system as a whole is the starting point and the parts representing the agents tend to be relatively simple.

I wish to step back from disputes as to the extent to which people (or agents) are socially embedded to one of the appropriateness of different types of models of agents. From this view-point, 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. It contrasts 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 1.

Figure 1. Social embeddedness as the appropriate level of modelling

Notice that criteria for model acceptability can include many things other than just its predictive accuracy, for example: complexity [4]. 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 take a three examples to make this a little clearer.

• Consider first an 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 single agent interacting with an economic environment. 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.

• Next, consider an intermediate case: 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, but these may be controlled for using good software design practice. Here one could meaningfully attribute a moderate level of social embeddedness.

• Finally, consider the phenomena of fashion. Something is fashionable only if considered so by a sizable section of a population. Although there is some grounding of the selection of particular styles in the climate, economic mood etc. this is tenuous. Fashion is largely a self-producing social phenomena; the particular content of fashion is contingent - it has little immediate connection with an individual's needs, otherwise fashions would not vary so greatly or change so rapidly. A model of how fashions change which took the form of an individual interacting with a unitary social environment would not capture much of the dynamics. Here we have a high level of embedding - fashion is an essentially social phenomena, so it is appropriate to model it at this level.

Generated with CERN WebMaker