Optimal Policies in Large Population Games with Applications to Epidemic Control

This seminar on dynamic games and applications explores optimal policies in large population games with a focus on epidemic control. Learn how the COVID-19 pandemic demonstrated the importance of considering large-scale agent interactions when designing mitigation policies. Discover a Stackelberg mean field game model that positions government optimization of mitigation policies against the choices of numerous non-cooperative individuals who determine their own socialization levels to manage transitions between health states. Examine how Nash equilibrium responses to policies can be approximated using mean field games, understand the equilibrium characterization, and explore numerical approaches based on machine learning tools. The presentation concludes with a discussion on extending the model to implement underlying networks among individuals through graphon game methodology. Presented by Gökçe Dayanikli from the University of Illinois at Urbana-Champaign as part of GERAD Research Center's seminar series on March 13, 2025.