Seminars & Colloquia
Computer Science, Cornell U.
"Games in Networks"
Monday January 14, 2008 04:00 PM
Location: 3211, EB-2 NCSU Centennial Campus
(Visitor parking instructions)
This talk is part of the Triangle Computer Science Distinguished Lecturer Series
Many large networks operate and evolve through interactions of large numbers of diverse participants. Such networks play a fundamental role in many domains, ranging from communication networks to social networks. In light of these competing forces, it is surprising how efficient these networks are. It is an exciting challenge to understand the success of these networks in game theoretic terms: what principles of interaction lead selfish participants to form such efficient networks? In this talk we present a number of network formation and routing games. We focus on simple games that have been analyzed in terms of the efficiency loss that results from selfishness. In each setting our goal is to quantify the degradation of quality of solution caused by the selfish behavior of users, comparing the selfish outcome to a centrally designed optimum, or comparing outcomes with different levels of cooperation.
Éva Tardos is a Jacob Gould Schurman Professor of Computer Science at Cornell University. She was born and educated in Hungary, and received her Ph.D. at Eötvös University in Budapest, Hungary in 1984. After teaching at Eötvös and the MIT, she joined Cornell in 1989. She is currently the chair of the Computer Science department at Cornell. She is a member of the National Academy of Engineering, American Academy of Arts and Sciences, an ACM Fellow, INFORMS fellow, was a Guggenheim Fellow, a Packard Fellow, a Sloan Fellow; an NSF Presidential Young Investigator; and has received the Fulkerson Prize in 1988, and the Dantzig prize in 2006. She is the editor editor-in-Chief of SIAM Journal of Computing, and editor of several other journals including Journal of the ACM, 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 mostly interested in fast combinatorial algorithms that provide provably optimal or close-to-optimal results. 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.
Host: Kamesh Mungala, Computer Science, Duke
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