Algorithmic rationality: Game theory with costly computation

Algorithmic rationality: Game theory with costly computation

We develop a general game-theoretic framework for reasoning about strategic agents performing possibly costly computation. In this framework, many traditional game-theoretic results (such as the existence of a Nash equilibrium) no longer hold. Nevertheless, we can use the framework to provide psychologically appealing explanations of observed behavior in well-studied games (such as finitely rep...

Game Theory with Costly Computation

We develop a general game-theoretic framework for reasoning about strategic agents performing possibly costly computation. In this framework, many traditional game-theoretic results (such as the existence of a Nash equilibrium) no longer hold. Nevertheless, we can use the framework to provide psychologically appealing explanations to observed behavior in wellstudied games (such as finitely repe...

Game Theory with Sparsity-Based Bounded Rationality∗

This paper proposes a way to enrich traditional game theory via an element of bounded rationality. It builds on previous work by the author in the context of one-agent decision problems, but extends it to several players and a generalization of the concept of Nash equilibrium. Each decision maker builds a simplified representation of the world, which includes only a partial understanding of the...

Algorithmic Game Theory

I dedicate this thesis to my parents, Anjani and Sitanshu Mehta. iii ACKNOWLEDGEMENTS I would like to thank Dick Lipton and Vijay Vazirani, for their continuous support and guidance throughout these five years. Thanks also to the rest of the theory and ACO group at Georgia Tech, faculty and students, who have created such a motivating and enriching research environment. Thanks, of course, to my...

Algorithmic game theory