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&lt;/script&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://www.cs.jhu.edu/event/distinguished-lecturer-eva-tardos-cornell-university-learning-and-efficiency-of-outcomes-in-games/embed/" width="600" height="338" title="&#x201C;Distinguished Lecturer: Eva Tardos, Cornell University &#x2013; &#x201C;Learning and Efficiency of Outcomes in Games&#x201D;&#x201D; &#x2014; Department of Computer Science" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;</html><description>LocationHackerman Hall B-17AbstractSelfish behavior can often lead to suboptimal outcome for all participants, a phenomenon illustrated by many classical examples in game theory. Over the last decade we have studied Nash equilibria of games, and developed good understanding how to quantify the impact of strategic user behavior on overall performance in many games (including traffic&hellip;</description></oembed>
