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CIS Seminar: “Learning in dynamic environments”
October 22 at 3:00 PM - 4:00 PM
In many online systems participants use data and algorithms to experiment and learn how to best use the system. Examples include traffic routing as well as online auctions. Game theory classically studies Nash equilibrium as the outcome of selfish interaction, and has many examples illustrating that selfish behavior can lead to suboptimal outcome for all participants. Over the last decade, we developed good understanding how to quantify the impact of strategic user behavior on overall performance in Nash equilibria of games. 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, possibly even in dynamically changing environments?
Jacob Gould Schurman Professor of Computer Science and Associate Dean for diversity & inclusion at Cornell University
Éva Tardos is a Jacob Gould Schurman Professor of Computer Science and associate dean for diversity & inclusion at Cornell University. She was department chair 2006-2010. She received her PhD from Eötvös University in Budapest. She joined the faculty at Cornell in 1989. Tardos’s research interest is algorithms and game theory. She has been elected to the National Academy of Engineering, the National Academy of Sciences, the American Academy of Arts and Sciences, and to the Hungarian Academy of Sciences. She is the recipient of a number of awards including the Fulkerson Prize in 1988, and the IEEE von Neumann Medal in 2019. She has served as editor for various journals, is currently the editor in chief of the Journal of the ACM and the editor of Combinatorica.