Animat market - Trading interactions as collective social adaptive behavior

We argue that human economic interactions, particularly bargaining and trad ing in market environments, can be considered as collective social adaptive behaviors. Such interactions are social in the sense that they depend on s ocially-agreed market regulations and communication protocols, and are coll ective in the sense that global market dynamics depend on the interactions of groups of traders. Moreover, the tools and techniques of adaptive behavi or research could be profitably employed to build predictive models of exis ting or planned market systems. Despite these potential applications, we no te that there is a near-total absence of papers in the adaptive behavior li terature that deal with autonomous agents capable of exhibiting trading beh aviors. We summarize work in experimental economics where human trading beh avior is studied under laboratory conditions. We propose that such experime nts could and should be used as 'benchmarks' for evaluating and comparing d ifferent architectures and strategies For trading animals. We present resul ts From experiments where an elementary machine learning technique endows s imple autonomous-software agents with the capability co adapt while interac ting via price-bargaining in market environments. The environments are base d on artificial retail markets used in experimental economics research. We demonstrate that groups of simple agents can exhibit human-like collective market behaviors. These results invite a Braitenberg-style eliminative mate rialism perspective on the dynamics of experimental retail markets.