Learning and Efficiency of Outcomes in Games

Eva Tardos, Cornell University

Selfish behavior can often lead to suboptimal outcome for all participants, a phenomenon illustrated by many classical examples in game theory. Over the last decade we have studied Nash equilibria of games, and developed good understanding how to quantify the impact of strategic user behavior on overall performance in many games (including traffic routing as well as online auctions). In this talk we will focus on games where players use a form of learning that helps them adapt to the environment. We ask if the quantitative guarantees obtained for Nash equilibria extend to such out of equilibrium game play, or even more broadly, when the game or the population of players is dynamically changing and where participants have to adapt to the dynamic environment.

Speaker Biography

Eva Tardos is the Jacob Gould Schurman Professor of Computer Science at Cornell University, was Computer Science department chair in 2006-2010. She received her BA and PhD from Eotvos University in Budapest in 1984 and joined the faculty at Cornell in 1989. Her research interest is algorithms, networks, and the interface of economics and computer science, focusing on the theory of designing systems and algorithms for users with diverse economic interests. For her work, Tardos has been elected to the National Academy of Engineering, National Academy of Sciences, and the American Academy of Arts and Sciences, and she is a fellow of multiple societies (ACM, AMS, SIAM, INFORMS). Dr. Tardos is also the recipient of several fellowships and awards including the Packard Fellowship, the Fulkerson Prize and the Goedel Prize. Most recently, IEEE announced that Dr. Tardos will receive the 2019 IEEE John von Neumann Medal in May for outstanding achievement in computer-related science and technology.