On nonlocal interactions in mean field games - Part 1

Numerous applications of mean-field games theory assume nonlocal interactions between agents. Although somewhat simpler from a mathematical analysis perspective, nonlocal models are often challenging for numerical solutions. Indeed, direct discretizations of mean-field interaction terms yield dense systems that are not economical from computational and memory perspectives. In this talk, I will discuss several options to mitigate the challenges above by importing methods from Fourier analysis and kernel methods in machine learning.

Part 2 of this talk continues here: https://mathtube.org/lecture/video/nonlocal-interactions-mean-field-game...