Machine Learning architectures for price formation models

Diogo Gomes; Julián Gutiérrez; Mathieu Laurière

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arXiv · 2204.03968

Machine Learning architectures for price formation models

Diogo Gomes·Julián Gutiérrez·Mathieu Laurière

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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.

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Diogo Gomes, Julián Gutiérrez, Mathieu Laurière. 2022-04-08. Machine Learning architectures for price formation models. https://arxiv.org/abs/2204.03968

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