ITR: Representation and Learning in Computational Game Theory

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Computational Game Theory is a rapidly emerging discipline at the intersection of computer science, economics, and related fields. It is becoming a fundamental tool for understanding and designing complex multiagent environments such as the Internet, systems of autonomous agents, and electronic economies. The objective of this program is the development of powerful new representations for complex game-theoretic and economic reasoning problems, and strategic learning algorithms for adjusting their parameters.Special emphasis is being given to models permitting the specification of natural network structure in the interactions within a large population of players, and models generalizing the spirit of financial markets, in which interactions take place via global intermediate quantities. Powerful recent machine learning methods such as boosting and exponential updates are also being applied to the more subtle and complex setting of learning in games.The expected results of the program are a rich set of new modeling methods for game-theoretic applications, and computationally efficient algorithms for reasoning with them, including the computation of Nash, correlated, and other equilibria, as well as efficient learning methods with known convergence properties. Special emphasis will be given to formal analysis, and the resulting methods will provide a new toolbox for researchers in economics, social science, evolutionary biology, and other fields in which game-theoretic approaches are common. The findings of the program will be widely disseminated through international conferences and journals, as well as more specialized workshops deliberately bringing together researchers from the different relevant disciplines.