Backward Mean-Field Linear-Quadratic-Gaussian (LQG) Games: Full and Partial Information.

This paper introduces the <bold>backward</bold> mean-field (MF) linear-quadratic-Gaussian (LQG) games (for short, BMFLQG) of weakly coupled stochastic large-population system. In contrast to the well-studied <bold> forward</bold> mean-field LQG games, the individual state in our large-population system follows the backward stochastic differential equation (BSDE) whose <bold>terminal</bold> instead <bold> initial</bold> condition should be prescribed. Two classes of BMFLQG games are discussed here and their decentralized strategies are derived through the consistency condition. In the first class, the individual agents of large-population system are weakly coupled in their state dynamics and the full information can be accessible to all agents. In the second class, the coupling structure lies in the cost functional with only partial information structure. In both classes, the asymptotic near-optimality property (namely, $\epsilon$- Nash equilibrium) of decentralized strategies are verified. To this end, some estimates to BSDE, are presented in the large-population setting. [ABSTRACT FROM AUTHOR]