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We analyze the problem of learning about other agents in a class of dynamic multiagent systems, where performance of the primary agent depends on behavior of the others. We consider an online version of the problem, where agents must learn models of the others in the course of continual interactions. Various levels of recursive models are implemented in a simulated double auction market. Our experiments show learning agents on average outperform non-learning agents who do not use information about others. Among learning agents, those with minimum recursion assumption generally perform better than the agents with more complicated, though often wrong assumptions.