We argue that human economic interactions, particularly bargaining and trading in market envi ronments, can be considered as collective social adaptive behaviors. Such interactions are social in the sense that they depend on socially-agreed market regulations and communication protocols, and are collective in the sense that global market dynamics depend on the interactions of groups of traders. Moreover, the tools and techniques of adaptive behavior research could be profitably employed to build predictive models of existing or planned market systems. Despite these poten tial applications, we note that there is a near-total absence of papers in the adaptive behavior lit erature that deal with autonomous agents capable of exhibiting trading behaviors. We summarize work in experimental economics where human trading behavior is studied under laboratory con ditions. We propose that such experiments could and should be used as 'benchmarks' for evaluat ing and comparing different architectures and strategies for trading animats. We present results from experiments where an elementary machine learning technique endows simple autonomous- software agents with the capability to adapt while interacting via price-bargaining in market envi ronments. The environments are based on artificial retail markets used in experimental econom ics research. We demonstrate that groups of simple agents can exhibit human-like collective mar ket behaviors. These results invite a Braitenberg-style eliminative materialism perspective on the dynamics of experimental retail markets.
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