Convergence of the equilibrium measure for LQG mean field games with common noise

Informal Systems Seminar (ISS)
Centre for Intelligent Machines (CIM) and Groupe d'Etudes et de Recherche en Analyse des Decisions (GERAD)

Speaker: Jiamin Jian

Zoom Link
Meeting ID: 845 1388 1004       
Passcode: VISS

Abstract:

This work focuses on exploring the convergence properties of a generic player’s trajectory and empirical measures in an N-player Linear-Quadratic-Gaussian Nash game, where Brownian motion serves as the common noise. We establish three distinct convergence rates concerning the representative player and empirical measure. To investigate the convergence, the methodology relies on a specific decomposition of the equilibrium path in the N-player game and utilizes the associated mean field game framework. It is a joint work with Prof. Qingshuo Song and Dr. Jiaxuan Ye.


Bio:

Jiamin Jian is a Ph.D. candidate in Department of Mathematical sciences at Worcester Polytechnic Institute under the supervision of Prof. Qingshuo Song. His research interest includes stochastic control, mean field games and financial mathematics. Before that, He got the Master’s degree in Mathematical Finance and Statistics from City University of Hong Kong in 2019 and the Bachelor’s degrees in Mathematics and Applied Mathematics from Nankai University in 2018.