@inproceedings{47da031c045f4c5bb247d2331a57a0f8,
title = "Machine Learning Architectures for Price Formation Models with Common Noise",
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 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.",
keywords = "Mean Field Games, Neural Networks, Price formation",
author = "Diogo Gomes and Julian Gutierrez and Mathieu Lauri{\`e}re",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 62nd IEEE Conference on Decision and Control, CDC 2023 ; Conference date: 13-12-2023 Through 15-12-2023",
year = "2023",
doi = "10.1109/CDC49753.2023.10383244",
language = "English (US)",
series = "Proceedings of the IEEE Conference on Decision and Control",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4345--4350",
booktitle = "2023 62nd IEEE Conference on Decision and Control, CDC 2023",
address = "United States",
}