Machine Learning architectures for price formation models with common noise

Diogo Gomes; Julian Gutierrez; Mathieu Laurière

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arXiv · 2305.17618

Machine Learning architectures for price formation models with common noise

Diogo Gomes·Julian Gutierrez·Mathieu Laurière

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Abstract

We propose a machine learning method to solve a mean-field game price formation model with common noise. This involves determining the price of a commodity traded among rational agents subject to a market clearing condition imposed by random supply, which presents additional challenges compared to the deterministic counterpart. Our approach uses a dual recurrent neural network architecture encoding noise dependence and a particle approximation of the mean-field model with a single loss function optimized by adversarial training. We provide a posteriori estimates for convergence and illustrate our method through numerical experiments.

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BibTeX RIS

Diogo Gomes, Julian Gutierrez, Mathieu Laurière. 2023-05-28. Machine Learning architectures for price formation models with common noise. https://arxiv.org/abs/2305.17618

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