LQG mean field games with a major agent: Nash certainty equivalence versus probabilistic approach. (December 2022)

• Record Type: Journal Article
• Authors: Firoozi, Dena
• Is Part Of: Automatica. Volume 146(2022)
• Publication Date: 2022-12
• Subjects: Major–minor LQG mean field games -- Nash equilibrium -- Nash certainty equivalence -- Probabilistic approach; Automatic control -- Periodicals; Automation -- Periodicals; 629.805
• DOI: 10.1016/j.automatica.2022.110559
• Languages: English
• ISSNs: 0005-1098

Abstract

Abstract: Mean field game (MFG) systems consisting of a major agent and a large number of minor agents were introduced in (Huang, 2010) in an LQG setup. The Nash certainty equivalence was used to obtain a Markovian closed-loop Nash equilibrium for the limiting system when the number of minor agents tends to infinity. In the past years several approaches to major–minor mean field game problems have been developed, principally (i) the Nash certainty equivalence and analytic approach, (ii) master equations, (iii) asymptotic solvability, and (iv) the probabilistic approach. For the LQG case, (Firoozi, Jaimungal, and Caines, 2020) develops a convex analysis approach and retrieves the equilibrium obtained via (i). Moreover, (Huang, 2021) establishes the equivalency of the Markovian closed-loop Nash equilibrium obtained via (i) with those obtained via (ii) and (iii). Finally, in this work we demonstrate that the Markovian closed-loop Nash equilibrium of (i) is equivalent to that of (iv). These three studies answer the long-standing questions about the consistency of the solutions to major-minor LQG MFG systems derived using different approaches.