A Linear Quadratic Partially Observed Stackelberg Stochastic Differential Game with Applications
Yueyang Zheng, Jingtao Shi

TL;DR
This paper develops a comprehensive framework for solving linear-quadratic partially observed Stackelberg stochastic differential games, deriving explicit equilibrium strategies and applying them to a dynamic advertising problem with asymmetric information.
Contribution
It introduces new necessary and sufficient conditions for Stackelberg equilibrium, including explicit solutions and Riccati equations, under partial observation and non-convex control constraints.
Findings
Explicit equilibrium strategies derived for the game.
Application to a dynamic advertising problem demonstrating practical relevance.
Numerical simulations validate the theoretical results.
Abstract
This paper is concerned with a linear-quadratic partially observed Stackelberg stochastic differential game with correlated state and observation noises, where the diffusion coefficient does not contain the control variable and the control set is not necessarily convex. Both the leader and the follower have their own observation equations, and the information filtration available to the leader is contained in that to the follower. By spike variational, state decomposition and backward separation techniques, necessary and sufficient conditions of the Stackelberg equilibrium points are derived. In the follower's problem, the state estimation feedback of optimal control can be represented by a forward-backward stochastic differential filtering equation and some Riccati equation. In the leader's problem, via the innovation process, the state estimation feedback of optimal control is…
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Taxonomy
TopicsStochastic processes and financial applications · Climate Change Policy and Economics · Consumer Market Behavior and Pricing
