Yixin Chen, PhD, professor of computer science and engineering, has received a two-year, $200,000 grant from the National Science Foundation for research titled "ICES: Small: Artificial Human Agents for Virtual Economies."
The goal of the research is to develop artificial intelligence agents to mimic human behaviors in playing games, based on data mining on human records. David Levine, professor of Economics, will guide its use in testing economic theories.
The goal of the project is to be able to replace human agents with artificial agents in studying two-player games. This project, if successful, will greatly enhance the ability of economists to test economic theories by partially replacing laboratory experiments with simulations. Over the last decades laboratory studies have proven invaluable both for the validation (and invalidation) of economic theories, and for the practical purpose of testing mechanisms (such as auctions) in the laboratory prior to practical implementation. The ability to use simulations with artificial agents in place of laboratory experiments with live human beings will both reduce the cost of validation and testing, and make it possible to explore quickly a much wider range of theories and policy alternatives. It will also enhance our understanding of human behavior and enrich our knowledge of the connection between human and artificial intelligence. Agent-based modeling is an emerging and attractive approach to validating economic theories. Existing research has focused on simple and naive agents. This project proposes as the next step to develop artificial human (economic) agents capable of mimicking the behavior of human laboratory subjects in the context of two player simultaneous move games. Substantial and detailed data is available on human play under these conditions. Existing algorithms fall only slightly short of the ability to mimic human play but do not yet implement fully autonomous agents. Based on hidden Markov models, the PIs propose to develop and investigate a framework of belief learning that is broad enough to encompass many existing learning algorithms including reinforcement learning, fictitious play, and smooth fictitious play. Moreover, based on this framework, the PIs propose to derive more sophisticated learning methods to fully develop artificial agents. This research will pursue two important directions. First, the PIs will introduce the initial calibration of priors based on available information and a cognitive hierarchy model. Second, the PIs will allow for the reconsideration of the existing model when "surprises" occur. The project will lead to the next stage of research in both economics and computer science in broadening the class of artificial agents to attack broader and more economically important tasks. It will also enrich the research on graphical model learning for artificial intelligence.
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