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Modelling Bounded Rationality In Agent-Based Simulations using the Evolution of Mental Models

4 Adapting the GP Paradigm

Among evolutionary techniques, the paradigm of GP
[17] is particularly appropriate, due to the structure of the genome*1. In GP the genes are tree-structures, which conform to a pre-defined syntax but otherwise can be of any shape or size. This makes them appropriate for representing a very wide range of models including expressions in formal languages and networks. These techniques, however, can not be blindly applied. For example, the efficiency of the learning process is only a secondary concern when seeking to model economic agents by their software cousins, but many of the other features of this approach for modelling learning in an economic agent are appropriate, namely:

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

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). Also, when applied to large populations this introduces a globality to the search which is unrealistic.

The fundamental difference between these agents and, say, logic-based agents, is that the updating of internal belief structures is done in a competitive evolutionary manner using a continuously variable fitness measure rather than in a "crisp" consistency preserving manner. This is appropriate in situations of great uncertainty caused by a rationality that is not able to completely "cope" with its environment but is more restricted in its ability.

In the first example presented below (Section 5.2) 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. The second application (Section 5.3) uses a more traditional GP setup using the cross-over operator, but with a low level of cross-over compared to propagation, applied to very small populations and with the addition of some new random models each generation.

Modelling Bounded Rationality In Agent-Based Simulations using the Evolution of Mental Models - 17 MAR 98
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