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With the increasing importance of e-commerce across the Internet, the need for software agents to support customers and suppliers in buying and selling goods/services is growing rapidly. It is becoming increasingly evident that in a few years the Internet will host a large number of interacting software agents. Most of them will be economically motivated, and will negotiate a variety of goods and services. It is therefore important to consider the economic incentives and behaviors of economic software agents, and to use all available means to anticipate their collective interactions. This paper addresses this concern by presenting a multiagent market simulator designed for analyzing agent market strategies based on a complete understanding of buyer and seller behaviors, preference models and pricing algorithms, and considering risk preferences. The system includes agents that are capable of improving their performance with their own experience, by adapting to the market conditions. The results of the negotiations between agents are analyzed by data mining algorithms to extract rules that give agents feedback to improve their strategies.Index Terms-Agent-based simulation, agent risk preferences, data mining, dynamic agent strategic behavior, electronic markets.