Thursday, January 20, 2022
“Choosing Incentives in Large Population Games with Applications to Epidemic Control”
Gökçe Dayanıklı, PhD Candidate, Operations Research and Financial Engineering Department, Princeton University
Abstract: In this talk, we consider a Stackelberg mean field game model between a principal and a mean field of agents evolving on a finite state space, motivated by models of epidemic control in large populations. The agents play a non-cooperative game in which they can control their transition rates between states to minimize an individual cost. The principal can influence the resulting Nash equilibrium through incentives to optimize its own objective. Later, we propose an application to an epidemic model of SIR type in which the agents control their interaction rate and the principal is a regulator acting with non-pharmaceutical interventions. To compute the solutions, we use an innovative numerical approach based on Monte Carlo simulations and machine learning tools for stochastic optimization. Finally, we briefly discuss another game formulation for a continuum of non-identical players evolving on a finite state space where their interactions are represented by a graphon.
Biography: Gökçe Dayanıklı is a PhD Candidate at the Operations Research and Financial Engineering Department at Princeton University. During Fall 2021, she was a visiting researcher at the Institute for Mathematical and Statistical Innovation at the University of Chicago. She holds a BSc in Industrial Engineering from Bogazici University. Her research interests are at the intersection of game & control theory and machine learning where she aims to find optimal policies and incentives for the real life societal problems. In 2021, she was given the Award for Excellence by Princeton University School of Engineering and Applied Science.