Algorithmic game theory is a field that uses and extends tools from economics and game theory to reason about fundamental computer science problems and applications. The PI will pursue a broad research agenda across three central subfields of algorithmic game theory. The first set of goals is to develop general analysis frameworks that can identify robust guarantees on the worst-case inefficiency of equilibria, and to consider measures of inefficiency other than the quality of a worst-case Nash equilibrium. The second set of goals concern developing novel worst-case analysis frameworks to inform the design of incentive-compatible auctions for revenue-maximization problems. The final goal is to apply computational complexity theory to explain rigorously certain barriers in economics and game theory, in particular in optimal mechanism design and in equilibrium computation.
