Genetic programming learning and the Cobweb model

Using genetic programming to model the cobweb model as a multiagent system, this chapter generalizes the work done by Arifovic (1994), which is based on genetic algorithms. We find that the rational expectations equilibrium price which can be discovered by genetic algorithms can also be discovered by genetic programming. Furthermore, genetic programming requires much less prior knowledge than genetic algorithms. The reasonable upper limit of the price and the characteristic of the equilibrium which is assumed as the prior knowledge in genetic algorithms can all be discovered by genetic programming. In addition, GP-based markets have a self-stabilizing force which is capable of bringing any deviations caused by mutation back to the rational expectations equilibrium price. All of these features show that genetic programming can be a very useful tool for economists to model learning and adaptation in multiagent systems. In particular, with respect to the understanding of the dynamics of the market process, it provides us with a visible foundation for the “invisible hand”.

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