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We argue that human economic interactions, particularly bargaining and trading in market environments, can be considered as adaptive behaviors. Moreover, the tools and techniques of adaptive behavior research could be profitably employed to build predictive models of existing or planned market systems. In addition to applications in economic modeling, “trading animats” could find use in market-based resource-allocation and control, and in internet-based commerce. After a brief overview of core concepts in experimental economics (where human trading behavior is studied under laboratory conditions), we propose that such experiments could and should be used as benchmarks' for evaluating and comparing different architectures and strategies for trading a.nimats. We then present empirical results from simulations that invite a Bra.itenberg-style eliminative materialism perspective on the dynamics of experimental retail markets. In these experiments, an elementary machine learning technique endows simple software animats with the capability to adapt while interacting via price-bargaining in market environments. The environments are based on artificial retail markets used in experimental economics research. We demonstrate that groups of simple agents can exhibit human-like collective market behaviors, and discuss the implications.