Michael Kearns: Game Theory and Machine Learning

This discussion explores the convergence of game theory and machine learning, where algorithms like those in navigation and social media platforms compute selfish best responses to guide users toward a Nash equilibrium. While John Nash's foundational work provides the mathematical stability for analyzing such interactions, the presentation highlights a critical limitation: achieving this competitive equilibrium does not guarantee an optimal collective outcome and can sometimes degrade overall system efficiency. The conversation concludes by examining how modern algorithmic design aims to mitigate these risks by actively steering participants away from inefficient equilibria.