6.1 Example strategies for agent model development
Two natural means of specializing models are by adding variables and by combining them in ways which facilitate domain-reduction.
When an agent adds a new variable to his model, this does not change the space of possibilities but it does change the size and perhaps the complexity of the model description. When the new variable is defined in terms of existing variables, it is a purely formal device in the model description, as whenever it occurs, the original reference to the variables it was defined by could be substituted instead. Placing conditions on this new variable is equivalent to a restrictive condition on the possible combinations of the environmental variables.
Take an example where there are two input variables v and w and one output variable p. If there is an existing model of the form p=g(x,y) for some relation g then the agent might try a model of the form p=f(v) where using some a priori information about the dimensionality of variables. The agent has thus decomposed the model into two separate parts to reduce the information considered. In effect, new conditions are added by specifying some relationship between the outputs of the models and combinations of the inputs to the model. This is a clear specialization of the original model (It is also an unambiguous simplification of the model as will be seen in section 7).
Model specialization takes the following form:
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