Machine Learning Architectures for Price Formation Models

Diogo Gomes, Julian Gutierrez*, Mathieu Laurière

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish (US)
Article number23
JournalApplied Mathematics and Optimization
Issue number1
StatePublished - Aug 2023


  • Lagrange multiplier
  • Mean field games
  • Neural networks
  • Price formation

ASJC Scopus subject areas

  • Control and Optimization
  • Applied Mathematics


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