ICES: Small: Artificial Human Agents for Virtual Economies

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The goal of the project is to be able to replace human agents withartificial agents in studying two-player games. This project, ifsuccessful, will greatly enhance the ability of economists to testeconomic theories by partially replacing laboratory experiments withsimulations. Over the last decades laboratory studies have proveninvaluable both for the validation (and invalidation) of economictheories, and for the practical purpose of testing mechanisms (such asauctions) in the laboratory prior to practical implementation. Theability to use simulations with artificial agents in place oflaboratory experiments with live human beings will both reduce thecost of validation and testing, and make it possible to explorequickly a much wider range of theories and policy alternatives. Itwill also enhance our understanding of human behavior and enrich ourknowledge of the connection between human and artificial intelligence.Agent-based modeling is an emerging and attractive approach tovalidating economic theories. Existing research has focused on simpleand naive agents. This project proposes as the next step to developartificial human (economic) agents capable of mimicking the behaviorof human laboratory subjects in the context of two player simultaneousmove games. Substantial and detailed data is available on human playunder these conditions. Existing algorithms fall only slightly shortof the ability to mimic human play but do not yet implement fullyautonomous agents. Based on hidden Markov models, the PIs propose todevelop and investigate a framework of belief learning that is broadenough to encompass many existing learning algorithms includingreinforcement learning, fictitious play, and smooth fictitious play.Moreover, based on this framework, the PIs propose to derive moresophisticated learning methods to fully develop artificial agents. Thisresearch will pursue two important directions. First, the PIs willintroduce the initial calibration of priors based on availableinformation and a cognitive hierarchy model. Second, the PIs will allow forthe reconsideration of the existing model when "surprises" occur. Theproject will lead to the next stage of research in both economics andcomputer science in broadening the class of artificial agents toattack broader and more economically important tasks. It will alsoenrich the research on graphical model learning for artificialintelligence.