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Publisher
Springer, Berlin, Heidelberg
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Authors: Bruce Edmonds Scott Moss
Publish Date: 2001/8/21
Volume: , Issue: , Pages: 759-766
Abstract
We distinguish between two main types of model predictive and explanatory It is argued in the absence of models that predict on unseen data that in order for a model to increase our understanding of the target system the model must credibly represent the structure of that system including the relevant aspects of agent cognition Merely “plugging in” an existing algorithm for the agent cognition will not help in such understanding In order to demonstrate that the cognitive model matters we compare two multiagent stock market models that differ only in the type of algorithm used by the agents to learn We also present a positive example where a neural net is used to model an aspect of agent behaviour in a more descriptive manner
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