Robust Mean Field Games

Dario Bauso, Hamidou Tembine, Tamer Başar

Risultato della ricerca: Article

18 Citazioni (Scopus)

Abstract

Recently there has been renewed interest in large-scale games inseveral research disciplines, with diverse application domains as in the smartgrid, cloud computing, nancial markets, biochemical reaction networks, transportationscience and molecular biology. Prior works have provided rich mathematicalfoundations and equilibrium concepts but relatively little in terms ofrobustness in the presence of uncertainties. In this paper, we study mean-eldgames with uncertainty in both states and payos. We consider a populationof players with individual states driven by a standard Brownian motion anda disturbance term. The contribution is three-fold: First, we establish a meaneld system for such robust games. Second, we apply the methodology to productionof an exhaustible resource. Third, we show that the dimension of themean eld system can be signicantly reduced by considering a functional ofthe first moment of the mean field process.
Lingua originaleEnglish
pagine (da-a)277-300
Numero di pagine24
RivistaDynamic Games and Applications
Volume6
Stato di pubblicazionePublished - 2016

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All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design
  • Computational Theory and Mathematics
  • Computational Mathematics
  • Applied Mathematics

Cita questo

Bauso, D., Tembine, H., & Başar, T. (2016). Robust Mean Field Games. Dynamic Games and Applications, 6, 277-300.