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Convergence for linear quadratic potential mean field games

Cecchin Alekos, Dianetti Jodi

Abstract

This paper studies the limits of empirical means of open-loop Nash equilibria of linear-quadratic stochastic differential games as the number of players goes to infinity, when the corresponding mean field game is of potential type and may have multiple equilibria. Via weak compactness arguments, the limit points are characterized as optimal trajectories of the related deterministic control problem, thus ruling out some of the mean field equilibria. Our result is obtained by first connecting the finite player game to a suitable control problem, whose optimal trajectories are the empirical means of Nash equilibria of the game, and in which the number of players $N$ becomes a parameter. True convergence to the unique minimizer of the limit control problem then holds for almost every initial mean. In cases of multiple optimizers, we focus on examples to show that some symmetry of the data ensures that the sequence admits a random limit which is distributes uniformly among the minimizers of the potential. Multidimensional examples of the convergence result appear here for the first time, which show the flexibility of our method. We also establish a similar convergence results for the corresponding linear-quadratic potential mean field games with common noise, as the noise vanishes.

Convergence for linear quadratic potential mean field games

Abstract

This paper studies the limits of empirical means of open-loop Nash equilibria of linear-quadratic stochastic differential games as the number of players goes to infinity, when the corresponding mean field game is of potential type and may have multiple equilibria. Via weak compactness arguments, the limit points are characterized as optimal trajectories of the related deterministic control problem, thus ruling out some of the mean field equilibria. Our result is obtained by first connecting the finite player game to a suitable control problem, whose optimal trajectories are the empirical means of Nash equilibria of the game, and in which the number of players becomes a parameter. True convergence to the unique minimizer of the limit control problem then holds for almost every initial mean. In cases of multiple optimizers, we focus on examples to show that some symmetry of the data ensures that the sequence admits a random limit which is distributes uniformly among the minimizers of the potential. Multidimensional examples of the convergence result appear here for the first time, which show the flexibility of our method. We also establish a similar convergence results for the corresponding linear-quadratic potential mean field games with common noise, as the noise vanishes.
Paper Structure (16 sections, 14 theorems, 82 equations)

This paper contains 16 sections, 14 theorems, 82 equations.

Table of Contents

  1. Introduction
  2. Background and motivation
  3. Our results and methodology
  4. Outline
  5. Linear quadratic mean field games and related control problems
  6. The $N$-player game
  7. A related control problem
  8. Potential mean field game
  9. $N$-player convergence result
  10. Mean field game with common noise
  11. Examples of selection
  12. One-dimensional symmetric selection
  13. The case of constant optimal controls
  14. Multi-dimensional symmetric selection
  15. Proof of Theorem \ref{['lemma existence uniqueness FBSDE.mean']}
  16. ...and 1 more sections

Key Result

Theorem 2

For $\sigma >0$, the FBSDE eq:FBSDE:mean admits a unique solution.

Theorems & Definitions (29)

  • Remark 1
  • Theorem 2
  • Theorem 3
  • Theorem 4
  • proof
  • Proposition 5
  • proof
  • Remark 6
  • Proposition 7
  • proof
  • ...and 19 more