Mean Field Linear-Quadratic-Gaussian (LQG) Games for Stochastic Integral Systems

• Published in: arXiv.org
• Main Authors: Huang, Jianhui, Li, Xun, Wang, Tianxiao
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 08.08.2013
• Subjects: Differential equations; Games; Integrals; Riccati equation; Volterra integral equations

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 Riccati equation in linear controlled differential system, some Fredholm-type equations are introduced to characterize the consistency condition of our integral system via the resolvent kernels. Third, based on the state aggregation technique, the Nash certainty equivalence (NCE) equation is derived and the set of associated decentralized controls are verified to satisfy the \(\epsilon\)-Nash equilibrium property. To this end, some new estimates of stochastic Volterra equations are developed which also have their own interests.