Learning and Efficiency of Outcomes in Games

Опубликовано 6 марта 2018, 22:48

Selfish behavior can often lead to a suboptimal outcome for all participants, a phenomenon illustrated by many classical examples in game theory. Over the last decade, we developed a good understanding of how to quantify the impact of strategic user behavior 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 them adapt to the environment, and consider two closely related questions: What are broad classes of learning behaviors that guarantee high social welfare in games when the game or the population of players is dynamically changing.

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