Mixed Social Optima and Nash equilibrium in Linear-Quadratic-Gaussian Mean-field System
Xinwei Feng, Jianhui Huang, Zhenghong Qiu

TL;DR
This paper studies a complex large-scale stochastic control system involving a major agent and many minor agents, deriving decentralized strategies and equilibria using advanced mean-field and stochastic analysis techniques.
Contribution
It introduces a novel mean-field forward-backward stochastic differential equation framework for mixed social optimization and Nash equilibrium in LQG systems with both major and minor agents.
Findings
Derived decentralized social strategies via new CC system.
Proved well-posedness of the CC system using discounting.
Established asymptotic social optimality and Nash equilibrium.
Abstract
This paper investigates a class of mixed stochastic linear-quadratic-Gaussian (LQG) social optimization and Nash game in the context of a large scale system. Two types of interactive agents are involved: a major agent and a large number of weakly-coupled minor agents. All minor agents are cooperative to minimize the social cost as the sum of their individual costs, whereas such social cost are conflictive to that of major agent. Thus, major agent and all minor agents are further competitive to reach some non-zero Nash equilibrium. The control processes enter both diffusion and drift terms of all major and minors' states. This extends the standard setup in which control only enters the drift terms, and such extension brings more modeling difference and technical difficulties, in particular, when dealing with the feedback decentralized strategy via Riccati equation and mean-field…
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Taxonomy
TopicsInnovation Diffusion and Forecasting · Decision-Making and Behavioral Economics
