Machine Learning architectures for price formation models
Optimization and Control
2023-01-26 v2
Abstract
Here, we study machine learning (ML) architectures to solve a mean-field games (MFGs) system arising in price formation models. We formulate a training process that relies on a min-max characterization of the optimal control and price variables. Our main theoretical contribution is the development of a posteriori estimates as a tool to evaluate the convergence of the training process. We illustrate our results with numerical experiments for linear dynamics and both quadratic and non-quadratic models.
Keywords
Cite
@article{arxiv.2204.03968,
title = {Machine Learning architectures for price formation models},
author = {Diogo Gomes and Julián Gutiérrez and Mathieu Laurière},
journal= {arXiv preprint arXiv:2204.03968},
year = {2023}
}