Selfish behaviour can often lead to suboptimal outcome for all participants, a phenomenon illustrated by classical examples in game theory, such as the prisoner dilemma. Over the last decade, computer scientists and game theorists have developed good understanding how to quantify the impact of strategic user behaviour on overall performance in environments that include selfish traffic routing, service location, and bandwidth sharing. In this talk, we will consider online auctions from this perspective.
The Internet provides an environment running millions of auctions, an environment where simplicity is more important than perfect efficiency, and where the systems used do not satisfy the usual standards of mechanism design. We'll consider such auctions as games, and we discuss how to analyse such games providing robust guarantees for their performance even when players participate in multiple auctions, have valuations that are complex functions of multiple outcomes, and are using learning strategies to deal with an uncertain environment.
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.