Mean Field Linear-Quadratic-Gaussian (LQG) Games for Stochastic Integral Systems
Jianhui Huang, Xun Li, Tianxiao Wang

TL;DR
This paper introduces a novel class of mean field LQG games where the system states are modeled by stochastic Volterra-type integral equations, extending traditional differential equation models and including stochastic delay systems.
Contribution
It develops new analytical techniques for mean field LQG games with integral system dynamics, including Fredholm equations and stochastic Volterra estimates, and establishes decentralized control strategies with epsilon-Nash equilibrium.
Findings
Derived Nash certainty equivalence equations for integral systems
Proved epsilon-Nash equilibrium property for decentralized controls
Developed new estimates for stochastic Volterra equations
Abstract
In this paper we discuss a class of mean field linear-quadratic-Gaussian (LQG) games for large population system which has never been addressed by existing literature. The features of our works are sketched as follows. First of all, our state is modeled by stochastic Volterra-type equation which leads to some new study on stochastic "integral" system. This feature makes our setup significantly different from the previous mean field games where the states always follow some stochastic "differential" equations. Actually, our stochastic integral system is rather general and can be viewed as natural generalization of stochastic differential equations. In addition, it also includes some types of stochastic delayed systems as its special cases. Second, some new techniques are explored to tackle our mean-field LQG games due to the special structure of integral system. For example, unlike the…
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Taxonomy
TopicsMathematical and Theoretical Epidemiology and Ecology Models · Advanced Thermodynamics and Statistical Mechanics · Stochastic processes and financial applications
