Mean field stochastic games with binary actions: Stationary threshold policies

This paper considers mean field games in a multiagent Markov decision process (MDP) framework. Each player has a continuum state and binary action. We analyze two stationary mean field games with discounted individual costs and long-run average individual costs, respectively. We show existence of a solution to the associated equation system, leading to threshold policies. Uniqueness is obtained under a product form cost and positive externalities.

[1]  U. Rieder,et al.  Markov Decision Processes with Applications to Finance , 2011 .

[2]  Minyi Huang,et al.  Mean Field Stochastic Games with Binary Action Spaces and Monotone Costs , 2017, 1701.06661.

[3]  Benjamin Van Roy,et al.  Markov Perfect Industry Dynamics with Many Firms , 2005 .

[4]  Jean C. Walrand,et al.  How Bad Are Selfish Investments in Network Security? , 2011, IEEE/ACM Transactions on Networking.

[5]  Minyi Huang,et al.  Large-Population Cost-Coupled LQG Problems With Nonuniform Agents: Individual-Mass Behavior and Decentralized $\varepsilon$-Nash Equilibria , 2007, IEEE Transactions on Automatic Control.

[6]  Huang Minyi,et al.  Mean field stochastic games: Monotone costs and threshold\\ policies , 2016 .

[7]  D. Earn,et al.  Vaccination and the theory of games. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[8]  Nabil I. Al-Najjar Aggregation and the law of large numbers in large economies , 2004, Games Econ. Behav..

[9]  P. Lions,et al.  Mean field games , 2007 .

[10]  Piero Poletti,et al.  Optimal vaccination choice, vaccination games, and rational exemption: an appraisal. , 2009, Vaccine.

[11]  Marc Lelarge,et al.  A local mean field analysis of security investments in networks , 2008, NetEcon '08.

[12]  R. Johari,et al.  Equilibria of Dynamic Games with Many Players: Existence, Approximation, and Market Structure , 2011 .

[13]  L. Shapley,et al.  Stochastic Games* , 1953, Proceedings of the National Academy of Sciences.

[14]  Steven I. Marcus,et al.  Structured solutions for stochastic control problems , 1992 .

[15]  Peter E. Caines,et al.  Large population stochastic dynamic games: closed-loop McKean-Vlasov systems and the Nash certainty equivalence principle , 2006, Commun. Inf. Syst..

[16]  Diogo A. Gomes,et al.  Mean Field Games Models—A Brief Survey , 2013, Dynamic Games and Applications.

[17]  Minyi Huang,et al.  On a class of large-scale cost-coupled Markov games with applications to decentralized power control , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[18]  A. Bensoussan,et al.  Mean Field Games and Mean Field Type Control Theory , 2013 .

[19]  A. Müller,et al.  Comparison Methods for Stochastic Models and Risks , 2002 .

[20]  R. Rosenthal,et al.  Anonymous sequential games , 1988 .

[21]  T. Başar,et al.  Risk-sensitive mean field stochastic differential games , 2011 .

[22]  Tao Li,et al.  Asymptotically Optimal Decentralized Control for Large Population Stochastic Multiagent Systems , 2008, IEEE Transactions on Automatic Control.

[23]  P. Caines,et al.  Individual and mass behaviour in large population stochastic wireless power control problems: centralized and Nash equilibrium solutions , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[24]  O. Hernández-Lerma Adaptive Markov Control Processes , 1989 .

[25]  Eitan Altman,et al.  Optimality of monotonic policies for two-action Markovian decision processes, with applications to control of queues with delayed information , 1995, Queueing Syst. Theory Appl..