In this talk, we approach the solution of mean-field game systems arising in price formation models employing machine learning. We use a min-max characterization of the optimal control and price variables. We guarantee the convergence of the training algorithm using first-order conditions of the underlying optimal control problem. Numerical results for linear-quadratic models illustrate our results.
This video was produced by the SITE Research Center at New York University, as part of their talk series.
