Robust Mean Field Games

Dario Bauso*, Hamidou Tembine, Tamer Basar

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

23 Citations (Scopus)

Abstract

Recently there has been renewed interest in large-scale games in several research disciplines, with diverse application domains as in the smart grid, cloud computing, financial markets, biochemical reaction networks, transportation science, and molecular biology. Prior works have provided rich mathematical foundations and equilibrium concepts but relatively little in terms of robustness in the presence of uncertainties. In this paper, we study mean field games with uncertainty in both states and payoffs. We consider a population of players with individual states driven by a standard Brownian motion and a disturbance term. The contribution is threefold: First, we establish a mean field system for such robust games. Second, we apply the methodology to production of an exhaustible resource. Third, we show that the dimension of the mean field system can be significantly reduced by considering a functional of the first moment of the mean field process.

Original languageEnglish
Pages (from-to)277-303
Number of pages27
JournalDynamic Games and Applications
Volume6
Issue number3
DOIs
Publication statusPublished - Sep-2016
Externally publishedYes

Keywords

  • Mean field games
  • Differential games
  • Optimal control
  • NASH EQUILIBRIA
  • APPROACHABILITY
  • REGRET

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