Modelling Bounded Rationality using Evolutionary Techniques - Bruce Edmonds

5 Adapting the GP Paradigm

There are several possible ways of using evolving populations to simulate a community of economic agents:

each member of the evolving population could represent one agent;
each agent could be modelled by a whole evolving population;
the whole population could be modelled by the whole evolving population but without an individually intended agent <-> gene correspondence.

Method (1) has been used in several models of agents which evolve (e.g. [7, 19]), here the genetic development has nothing to do with the nature of an agent's cognitive processes but helps determine its goals or strategies. Method (3) above is the most popular in economics (e.g. [1, 3]), but unless such a model predicts pertinent properties of real populations of agents it represents a bit of a fudge, and means that the observable behaviour and content of individual entities in the model do not have a clear referent in what is being modelled. This makes it far less useful if one wants to use such models to gain a detailed insight into the internal dynamics of populations. Method (2) actually addresses the cognitive process as the agent corresponds to a population of mental models. This has been done before in a limited way in [15], but here agents have a fixed menu of possible models which do not develop.

In using the evolutionary paradigm in this sort of modelling we tend to:

represent the agent by a whole evolving population - each gene corresponding to one of its alternative models (this is the approach taken in the example in Section 7);
populations of agents are thus modelled as populations of evolving populations (i.e. populations of populations), with an intended agent to evolving population correspondence (e.g. [14]);
give the agents only small populations of models, representing limited memory;
base the fitness function on a combination of its error compared to past data, size of model and its predictivity (precision and range of applicability);
restrict the variation operators to such as generalisation, specialisation, averaging, combining and mutating;
and give them only a limited inferential ability to use its best model to choose its action.

This paradigm needs to be integrated with an agent-based approach and adapted to relate to credible models of economic agents. In particular the cross-over operator is somewhat arbitrary when simulating the development of models in economic agents (although undeniably efficient). This also introduces a globality to the search which is unrealistic.

In the example application presented below we use a process of combining old models together as branches from a new node and introducing randomly generated small new models. This produces more realistic results, for example it allows for better fitting by parameterisation.

Modelling Bounded Rationality using Evolutionary Techniques - Bruce Edmonds - 09 JUN 97

[Next] [Previous] [Top] [Contents]

Generated with CERN WebMaker