DLS: Learning and Efficiency in Games with Dynamic Population

Wednesday, March 30, 2016 - 14:00 to 16:00
120 Conference Room

DLS Presenter: Eva Tardos, Cornell University
Title: Learning and Efficiency in Games with Dynamic Population

Selfish behavior can often lead to suboptimal outcome for all participants. This is especially true in dynamically changing environments where the game or the set of the participants can change at any time without even the players realizing it. Over the last decade we have developed good understanding how to quantify the impact of strategic user behavior on overall performance via studying stable Nash equilibria of the games. In this talk we will consider the quality of outcomes in games when the population of players is dynamically changing, and where participants have to adapt to the dynamic environment. We show that in large classes of games (including congestion games), if players use a form of learning that helps them to adapt to the changing environment, this guarantees high social welfare, even under very frequent changes. Joint work with Thodoris Lykouris and Vasilis Syrgkanis.

Eva Tardos is a Jacob Gould Schurman Professor of Computer Science at Cornell University, was Computer Science department chair 2006-2010. She received her BA and PhD from Eotvos University in Budapest. She joined the faculty at Cornell in 1989. She has been elected to the National Academy of Engineering, the National Academy of Sciences, the American Academy of Arts and Sciences, is an external member of the Hungarian Academy of Sciences, and is the recipient of a number of fellowships and awards including the Packard Fellowship, the Goedel Prize, Dantzig Prize, Fulkerson Prize, and the IEEE Technical Achievement Award. She was editor editor-in-Chief of SIAM Journal of Computing 2004-2009, and is currently editor of several other journals including the Journal of the ACM and Combinatorica, served as problem committee member for many conferences, and was program committee chair for SODA’96, FOCS’05, and EC’13.

Tardos’s research interest is algorithms and algorithmic game theory, the subarea of theoretical computer science theory of designing systems and algorithms for selfish users. Her research focuses on algorithms and games on networks. She is most known for her work on network-flow algorithms, approximation algorithms, and quantifying the efficiency of selfish routing.

Visit her website for more information on her career: http://www.cs.cornell.edu/~eva/

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DLS: Learning and Efficiency in Games with Dynamic Population