| Abstract: |
| In this talk, we study a linear-convex mean-field game with input constraints for partially observed forward-backward system, where both types of mean-field terms, asynchronous style (state-averages) and synchronous style (state expectations), are considered. The observation is a controlled process, whose drift term is linear with respect to state and control variable. For the general case, by using the mean-field method and the backward separation approach, we obtain the decentralized optimal strategies through a Hamiltonian system and related Consistency Condition (CC), which are given by two types of mean-field forward-backward stochastic differential equations with filtering. In virtue of continuation method and discounting method, the well-posedness of such kind of equations is proved under two different conditions. For the linear-quadratic case under linear subspace constraints, we give the feedback representation of the decentralized optimal strategies, and the Riccati type CC system is also given. As one application, an asset-liability management problem is solved. |
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