CSAIL Event Calendar

Games in Networks

Abstract:
Games in Networks

Network games play a fundamental role in understanding behavior in many
domains, ranging from communication networks through markets to social
networks. Such networks operate and evolve through interactions of large
numbers of diverse participants. In light of these competing forces, it is
surprising how efficient these networks are. It is an exciting challenge to
understand the operation and 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 a couple simple games that have been analyzed. One
measure we study 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.


Bio:
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
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.

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