Published as: Edmonds, B. (2002) Exploring the Value of Prediction in an Artificial Stock Market. In Butz V. M., Sigaud, O. and Gérard, P. (eds.) Anticipatory Behavior in Adaptive Learning Systems. Springer, Lecture Notes in Artificial Intelligence, 2684:262-281.
An action selection architecture is described which uses two learning modules: one to predict future events (the PPM) and one to decide upon appropriate actions (the IALM). These are only connected by the fact that they have access to each other’s past results and the IALM can use the predictions of the PPM as inputs to its action selection. This is instantiated in a model which uses GP-based learning mechanisms for the two modules and is tested in an artificial stock market. The point of the exercise is to start exploring the conditions under which prediction might be helpful to successful action selection. Preliminary results indicate that although prediction can be helpful it is not always an advantage. This set-up is briefly compared to that of the anticipatory classifier system and I speculate that prediction might have similar role to learning in the “Baldwin Effect”.
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