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Lovelace Lecture: Learning and Efficiency of Outcomes in Games

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Duration: 0:56:09 | Added: 22 Aug 2017
Éva Tardos, Department of Computer Science, Cornell University, gives the 2017 Ada Lovelace Lecture on 6th June 2017.

Selfish behaviour can often lead to suboptimal outcome for all participants, a phenomenon illustrated by many classical examples in game theory. Over the last decade we developed good understanding on how to quantify the impact of strategic user behaviour on the overall performance in many games (including traffic routing as well as online auctions). In this talk we will focus on games where players use a form of learning that helps themadapt to the environment, and consider two closely related questions: What are broad classes of learning behaviours that guarantee that game outcomes converge to the quality guaranteed by the price of anarchy, and how fast is this convergence. Or asking these questions more broadly: what learning guarantees high social welfare in games, when the game or the population of players is dynamically changing.

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