大会报告：黄民懿会士（卡尔顿大学）

Mean field games and their large network limits

Minyi Huang

School of Mathematics and Statistics, Carleton University

Abstract: Mean field game (MFG) theory provides a powerful tool to tackle large-population non-cooperative dynamic games, and has found applications in many areas including economics and finance, engineering, and public health.

This talk describes how to extend MFG theory to models with subpopulations distributed over large scale networks, addressing a new form of agent heterogeneity. The dense case can be treated using graphon networks. For the sparse case, we apply appropriate scaling to obtain a meaningful limit, allowing to capture influence of neighboring subpopulations in the large sparse network limit.  

Based on joint work with Prof Peter Caines and Dr Tian Chen

Short bio:

Minyi Huang received the B.Sc. degree from Shandong University, Jinan, Shandong, China, in 1995, the M.Sc. degree from the Institute of Systems Science, Chinese Academy of Sciences, Beijing, in 1998, and the Ph.D. degree from the Department of Electrical and Computer Engineering, McGill University, Montreal, QC, Canada, in 2003, all in systems and control. He was a Research Fellow first at the University of Melbourne, Australia, from February 2004 to March 2006, and then at the Australian National University, Canberra, from April 2006 to June 2007. He joined the School of Mathematics and Statistics, Carleton University, Ottawa, ON, Canada in 2007, where he is now a Professor. His research interests include mean field stochastic control and dynamic games, multi-agent control and computation in distributed networks with applications. He is a Fellow of IEEE and a member of SIAM.