Machine Learning Architectures for Price Formation Models with Common Noise

Diogo Gomes; Julian Gutierrez; Mathieu Laurière

doi:10.1109/CDC49753.2023.10383244

Machine Learning Architectures for Price Formation Models with Common Noise

Diogo Gomes, Julian Gutierrez, Mathieu Laurière

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English (US)
Title of host publication	2023 62nd IEEE Conference on Decision and Control, CDC 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4345-4350
Number of pages	6
ISBN (Electronic)	9798350301243
DOIs	https://doi.org/10.1109/CDC49753.2023.10383244
State	Published - 2023
Event	62nd IEEE Conference on Decision and Control, CDC 2023 - Singapore, Singapore Duration: Dec 13 2023 → Dec 15 2023

Publication series

Name	Proceedings of the IEEE Conference on Decision and Control
ISSN (Print)	0743-1546
ISSN (Electronic)	2576-2370

Conference

Conference	62nd IEEE Conference on Decision and Control, CDC 2023
Country/Territory	Singapore
City	Singapore
Period	12/13/23 → 12/15/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

Mean Field Games
Neural Networks
Price formation

ASJC Scopus subject areas

Control and Systems Engineering
Modeling and Simulation
Control and Optimization

Access to Document

10.1109/CDC49753.2023.10383244

Cite this

Gomes, D., Gutierrez, J., & Laurière, M. (2023). Machine Learning Architectures for Price Formation Models with Common Noise. In 2023 62nd IEEE Conference on Decision and Control, CDC 2023 (pp. 4345-4350). (Proceedings of the IEEE Conference on Decision and Control). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC49753.2023.10383244

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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 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",

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Gomes, D, Gutierrez, J & Laurière, M 2023, Machine Learning Architectures for Price Formation Models with Common Noise. in 2023 62nd IEEE Conference on Decision and Control, CDC 2023. Proceedings of the IEEE Conference on Decision and Control, Institute of Electrical and Electronics Engineers Inc., pp. 4345-4350, 62nd IEEE Conference on Decision and Control, CDC 2023, Singapore, Singapore, 12/13/23. https://doi.org/10.1109/CDC49753.2023.10383244

Machine Learning Architectures for Price Formation Models with Common Noise. / Gomes, Diogo; Gutierrez, Julian; Laurière, Mathieu.
2023 62nd IEEE Conference on Decision and Control, CDC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 4345-4350 (Proceedings of the IEEE Conference on Decision and Control).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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Machine Learning Architectures for Price Formation Models with Common Noise

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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