3 Characterising Social Embeddedness
Let us suppose that our criteria for model goodness are complexity and explanatory power. By explanatory power, I mean the extent of the phenomena that the model describes. Thus there is a familiar trade-off between explanatory power and complexity in our modelling of our simulation [25]. If two descriptions of the simulation are functionally the same, the social embeddedness comes out as a difference between the complexity of the models at the agent and social levels*1. This is not quite the obvious way of going about things - it might seem more natural to fix some criteria for explanatory power and then expand the complexity (in this case by including more aspects of the social nature of the environment in the model) until it suffices. However, in social simulation where it is often unclear what an acceptable standard of explanatory power might be, it is easier to proceed by making judgements as to the complexity of models.
In the model below I will use a rough measure of the social embeddedness based on where most of the computation takes place that determines an agent's communication and action. This will be indicated by the proportion of subexpressions in their learnt strategies which preform an external reference to the individual actions of other agents to those that preform internal calculations (logical, arithmetic, statistical etc.). This ignores the computation due to the evaluation and production of the expressions inside each agent, but this is fairly constant across runs and agents.
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