LQG Differential Stackelberg Game under Nested Observation Information Pattern

TL;DR

This paper studies a linear quadratic Gaussian Stackelberg game with nested observation information, deriving explicit feedback solutions through coupled Riccati equations and Kalman-Bucy filtering.

Contribution

It introduces a novel layered method to solve the complex forward-backward stochastic differential equations with nested information patterns.

Findings

Explicit feedback representation for the optimal solution.

Coupled Riccati equations characterize the feedback coefficients.

Kalman-Bucy filtering computes feedback variables.

Abstract

We investigate the linear quadratic Gaussian Stackelberg game under a class of nested observation information pattern. Two decision makers implement control strategies relying on different information sets: The follower uses its observation data to design its strategy, whereas the leader implements its strategy using global observation data. We show that the solution requires solving a new type of forward-backward stochastic differential equations whose drift terms contain two types of conditional expectation terms associated to the adjoint variables. We then propose a method to find the functional relations between each adjoint pair, i.e., each pair formed by an adjoint variable and the conditional expectation of its associated state. The proposed method follows a layered pattern. More precisely, in the inner layer, we seek the functional relation for the adjoint pair under the sigma-sub-algebra generated by follower's observation information; and in the outer layer, we look for the functional relation for the adjoint pair under the sigma-sub-algebra generated by leader's observation information. Our result shows that the optimal open-loop solution admits an explicit feedback type representation. More precisely, the feedback coefficient matrices satisfy tuples of coupled forward-backward differential Riccati equations, and feedback variables are computed by Kalman-Bucy filtering.