Composable Mechanisms, Learning, and Price of Anarchy in Auctions
September 2, 2014 | Wu & Chen Auditorium
Warren Center Distinguished Lecturer, Eva Tardos, from Computer Science Deptartment at Cornell University will present the following talk:
“Composable Mechanisms, Learning, and Price of Anarchy in Auctions”
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. Over the last decade we have developed good understanding how to quantify the impact of strategic user behavior on overall performance in some concrete games. In this talk, we will consider online auctions from this perspective. A key property of this environments is that players typically participate in multiple auctions, have valuations that are complex functions of multiple outcomes, and are using learning strategies to deal with an uncertain environment. In this talk we show how to provide robust guarantees for the performance of many simple auctions even in such complex environments.
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.