Learning and Efficiency of Outcomes in Games with Eva Tardos, Dept of Computer Science, Cornell University

September 30, 2016 | 3:00 pm
Rice Hall Auditorium



Co-Sponsored by: Department of Computer Science, Department of Economics


SPEAKER:                 Eva Tardos, Department of Computer Science, Cornell University

TOPIC:                        Learning and Efficiency of Outcomes in Games

DATE:                        Friday, September 30

TIME:                         3:30 pm (short reception before talk at 3:00)

PLACE:                      Rice Hall Auditorium


Abstract: 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


Bio: Éva Tardos received her Dipl.Math. in 1981, and her Ph.D. 1984, from Eötvös University, Budapest, Hungary. She was Chair of the Department of Computer Science at Cornell University 2006-2010 and she is currently serving as the Associate Dean of the College of Computing and Information Science. She has been elected to the National Academy of Engineering and the American Academy of Arts and Sciences, and is the recipient of Packard, Sloan Foundation, and Guggenheim fellowship, an ACM Fellow, INFORMS fellow; and has received the Fulkerson Prize, and the Dantzig prize. She was editor editor-in-Chief of SIAM Journal of Computing 2004-2009, and is currently editor of several other journals: Journal of the ACM, Theory of Computing, and Combinatorica. Tardos's research interest is algorithms and algorithmic game theory. Her work focuses on the design and analysis of efficient methods for combinatorial-optimization problems on graphs or networks. She is most known for her work on network-flow algorithms, approximation algorithms for network flows, cut, and clustering problems. Her recent work focuses on algorithmic game theory, an emerging new area of designing systems and algorithms for selfish users