Stability and Learning in Repeated Games

Explore the complexities of stability and learning in repeated games through this Richard M. Karp Distinguished Lecture delivered by Éva Tardos from Cornell University. Delve into the research community's understanding of strategic user behavior's impact on outcomes in games like traffic routing and online auctions. Examine how these findings extend to repeated games when players employ no-regret learning. Investigate the limitations of these results, particularly in packet routing and online auctions where game states evolve based on previous outcomes. Learn about the stability guarantees in simple routing games with increased server capacity and the potential shortcomings of no-regret learning in such environments. Gain insights into network-flow algorithms, selfish routing efficiency, and the interface between algorithms and incentives from a renowned expert in the field.