UWashington Distinguished Seminar in Optimization and Data: Éva Tardos

Over the last two decades we have developed good understanding of how to quantify the impact of strategic user behavior on outcomes in many games (including traffic routing and online auctions) and showed that the resulting bounds extend to repeated games assuming players use a form of no-regret learning to adapt to the environment. Unfortunately, these results do not apply when outcomes in one round affect the game in the future, as is the case in many applications. In this talk, we study this phenomenon in the context of a game modeling queuing systems: routers compete for servers, where packets that do not get served need to be resent, resulting in a system where the number of packets at each round depends on the success of the routers in the previous rounds. In joint work with Jason Gaitonde, we analyze the resulting highly dependent random process. We find that if the capacity of the servers is high enough to allow a centralized and knowledgeable scheduler to get all packets served even with double the packet arrival rate, then despite selfish behavior of the queues, the expected number of packets in the queues will remain bounded throughout time, assuming older packets have priority. Further, if queues are more patient in evaluating their outcomes , maximizing their long-run success rate, stability can be ensured with just 1.58 times extra capacity, strictly better than what is possible assuming the no-regret property.

Speaker Biography: Éva Tardos is a Jacob Gould Schurman Professor of Computer Science, currently chair of the Department of Computer Science for a second term after being chair 2006-2010. She was Interim Dean for Computing and Information Sciences 2012-2013 and more recently was Associate Dean for Diversity & Inclusion at Cornell University. She received her BA and PhD from Eötvös University in Budapest. She joined the faculty at Cornell in 1989. Tardos's research interest is algorithms and interface of algorithms and incentives. She is most known for her work on network-flow 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 Philosophical Society, the American Academy of Arts and Sciences, and to the Hungarian Academy of Sciences. She is the recipient of a number of fellowships and awards including the Packard Fellowship, the Gödel Prize, Dantzig Prize, Fulkerson Prize, ETACS prize, and the IEEE von Neumann Medal. She co-wrote the widely used textbook Algorithms Design. She has been editor-in-Chief of the Journal of the ACM and of the SIAM Journal of Computing, and was editor of several other journals, and was program committee member and chair for several ACM and IEEE conferences in her area.