The Santa Fe Artificial Stock Market [13, 4] is an agent-based artificial model in which agents continually explore and develop expectational models, buy and sell assets based on the predictions of those models that perform best, and confirm or discard these models based on their performance over time. The purpose of this paper is to classify the different types of behavior that emerge in the market as a function of evolutionary learning rate, and to explain these emergent behaviors. We observe four different types of behavior, which are distinguished by their effects on the volatility of prices, the complexity of strategies, and the wealth earned by agents over time. We also show that the differences between these behaviors may be attributed to variations in the rate at which agents revise their trading rules and the subsequent types of rules---technical or fundamental---that emerge in the market.
To appear in Proceedings of Complex Systems '98: Complexity Between the Ecos---from Ecology to Economics.
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Paper provided by Santa Fe Institute in its series Research in Economics with number
98-12-114e.
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