Graduate Summer School: Mean Field Games and Applications

June 18 - 29, 2018



Mean field games theory is a framework developed to advance the understanding of problems in game theory with a very large number of agents. This theory has been prominently applied in areas such as economics, crowd dynamics and network engineering. In recent years, there has been a surge of interest in mean field games as several tools have been brought to bear on its important problems, including theory of stochastic differential equations, methods of optimization, optimal transport theory, numerical methods and numerical analysis, among others.

The summer school, Mean Field Games and Applications, will be a series of lectures that aims to introduce graduate students and postdocs to this recently accessible and fast growing area. The main goals of the summer school are: (i) To introduce students and postdocs to Mean Field Games and certain cognate areas through lectures by leading researchers; (ii) To make available to the wider mathematical community a series of broad-interest talks on mean field games; (iii) To provide a collaborative environment that is welcoming to underrepresented minorities and women and which brings scientists together with trainees from varied backgrounds.

The summer school lecturers have also been selected to provide points of view from different aspects of mean field games and to emphasize a variety of applications. In addition to lectures, there will be tutorial-style activities to allow participants to work with problems in mean field games themselves. Therefore, participants will have an opportunity to learn the state of the art within mean field games research, to learn about applications of this theory to other research areas and also to get involved with research on this exciting topic. Moreover, leading experts and practitioners will be present to facilitate interaction and collaborative activities.

Organizing Committee

David Ambrose (Drexel University)
Wilfrid Gangbo (University of California, Los Angeles (UCLA))
Ryan Hynd (University of Pennsylvania)