Learning and Efficiency of Outcomes in Games

  • Eva Tardos | Cornell University

Selfish behavior can often lead to a suboptimal outcome for all participants, a phenomenon illustrated by many classical examples in game theory. Over the last decade, we developed a good understanding of how to quantify the impact of strategic user behavior on the 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, and consider two closely related questions: What are broad classes of learning behaviors that guarantee high social welfare in games when the game or the population of players is dynamically changing.

Speaker Details

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. Tardos’s research interest is algorithms and algorithmic game theory. She is most known for her work on network-flow algorithms, approximation algorithms, and quantifying the efficiency of selfish routing. She has been elected to the National Academy of Engineering, the National Academy of Sciences, the American Academy of Arts and Sciences, and is an external member of the Hungarian Academy of Sciences. She 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 is editor editor-in-Chief of the Journal of the ACM, and was editor in the past of several other journals including the SIAM Journal of Computing, and Combinatorica, served as problem committee member for many conferences, and was program committee chair for SODA’96, FOCS’05, and EC’13.

Series: MSR AI Distinguished Lectures and Fireside Chats