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     Department of Systems Engineering and Engineering Management



                        The Chinese University of Hong Kong



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Date: Friday, March 7, 4:30 pm – 5:30 pm



Venue: ERB 513, The Chinese University of Hong Kong



Title: Deep Learning for Stackelberg Mean Field Games via Single-Level

Reformulation



Speaker: Professor Mathieu Laurière, NYU Shanghai



Abstract: We propose a single-level numerical approach to solve

Stackelberg mean field game (MFG) problems. In Stackelberg MFG, an

infinite population of agents play a non-cooperative game and choose

their controls to optimize their individual objectives while

interacting with the principal and other agents through the population

distribution. The principal can influence the mean field Nash

equilibrium at the population level through policies, and she

optimizes her own objective, which depends on the population

distribution. This leads to a bi-level problem between the principal

and mean field of agents that cannot be solved using traditional

methods for MFGs. We propose a reformulation of this problem as a

single-level mean field optimal control problem through a penalization

approach, and we prove convergence of the reformulated problem to the

original problem. We propose a machine learning method based on neural

networks and illustrate it with several examples from the literature,

including with applications to finance. Joint work with Gökçe Dayanikli.





Bio: Mathieu Laurière is an Assistant Professor of Mathematics and

Data Science at NYU Shanghai. Prior to joining NYU Shanghai, he was a

Postdoctoral Research Associate at Princeton University in the

Operations Research and Financial Engineering (ORFE) department and a

Visiting Faculty Researcher at Google Brain. He obtained his MS from

Sorbonne University and ENS Paris-Saclay and his PhD from the

University of Paris. Before joining Princeton University, he was a

Postdoctoral Fellow at the NYU-ECNU Institute of Mathematical Sciences

at NYU Shanghai.