Network Models for Game Theory and Economics – Michael Kearns (University of Pennsylvania)
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Over the last several years, a number of authors have developed graph-theoretic or network models for large-population game theory and economics. In such models, each player or organization is represented by a vertex in a graph, and payoffs and transactions are restricted to obey the topology of the graph. This allows the detailed specification of rich structure (social, technological, organizational, political, regulatory) in strategic and economic systems. In this talk, I will survey these models and the attendant algorithms for certain basic computations, including Nash, correlated, and Arrow-Debreu equilibria. Connections to related topics, such as Bayesian and Markov networks for probabilistic modeling and inference, will be discussed. I will also discuss some recent work marrying this general line of thought with topics in social network theory.