We construct a computer simulation of a repeated double-auction market, designed to match those in experimental-market settings with human subjects, to model complex interactions among arti cially-intelligent traders endowed with varying degrees of learning capabilities. In the course of six di erent experimental designs, we i n vestigate a number of features of our agent-based model: the price e ciency of the market, the speed at which prices converge to the rational expectations equilibrium

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... ice, the dynamics of the distribution of wealth among the di erent types of AI-agents, trading volume, bid/ask spreads, and other aspects of market dynamics. We are able to replicate several ndings of human-based experimental markets, however, we also nd intriguing di erences between agent-based and human-based experiments.