Robust Social Welfare Maximization via Information Design in Linear-Quadratic-Gaussian Games
Robust Social Welfare Maximization via Information Design in Linear-Quadratic-Gaussian Games
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DOI：
10.1109/lcsys.2023.3291659
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发表时间：
2023-03
影响因子：
3
通讯作者：
Furkan Sezer;Ceyhun Eksin
中科院分区：
文献类型：
--
作者：
Furkan Sezer;Ceyhun Eksin
信息设计涉及一名设计师，其目标是通过设计的概率分布产生的信号在不完整的信息游戏中，因此我们考虑了一个设置，在该设置中，设计师对玩家的回报有部分知识，并希望通过对玩家的偏好来解决Quarbust Informaff the Fornaul the Forner the Forn-Case worder worder-case the wor wor wor the wor wor the word-caress of wor wor the wor the wor the wor the wor the wor the word-cose the wor wor wor the wor the wor to wor to wor to wor to worder worder word corment。当玩家的二次收益取决于行动和与收益相关的状态，并在状态下遵循高斯分布的信号，可以将二次设计目标的信息设计问题表示为半际计划（SDP）可以获得类似的通过强大的信息设计近似社交福利最大化的SDP公式。
Information design involves a designer with the goal of influencing players’ actions in an incomplete information game through signals generated from a designed probability distribution so that its objective function is optimized. We consider a setting in which the designer has partial knowledge on players’ payoffs, and wants to maximize social welfare. We address the uncertainty about players’ preferences by formulating a robust information design problem against the worst-case payoffs. When the players have quadratic payoffs that depend on the actions and an unknown payoff-relevant state, and signals on the state that follow a Gaussian distribution, the information design problem under quadratic design objectives can be stated as a semidefinite program (SDP) (Ui, 2020). Given this fact, we consider ellipsoid perturbations over payoff coefficients in linear-quadratic-Gaussian (LQG) games. We show that we can obtain a similar SDP formulation that approximates the social welfare maximization via robust information design. Numerical experiments identify the relation between the uncertainty level on players’ payoffs and the optimal information structures.