"Games, Learning, and the Price of Anarchy"
Eva Tardos, Cornell University
Thursday, February 12, 2015 at 4:00 P.M.
Room 368 (CIT 3rd Floor)
Selfish behavior can often lead to suboptimal outcome for all participants, a phenomenon illustrated by classical examples in game theory, such as the prisoner dilemma . Yet, many algorithms, that are originally designed without explicitly considering incentive properties, are later used in settings when participants can act strategically. How good are they in the presence of strategic behavior? We'll will show robust guarantees for performance on a broad range of algorithms in presence of strategic behavior of the participants. Joint work with Paul Duetting and Thomas Kesselheim.
Eva Tardos is 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.
Host: Philip Klein