论文标题
线性界限 - 高斯网络游戏中各个代理商的信息偏好
Information Preferences of Individual Agents in Linear-Quadratic-Gaussian Network Games
论文作者
论文摘要
我们考虑线性 - 季度高斯（LQG）网络游戏，其中代理具有二次收益，取决于其个人和邻居的行为以及与回报相关的状态。信息设计师确定向代理商揭示的有关回报状态的信息的保真度，以最大程度地提高社会福利。先前的结果表明，在收益的某些假设下，全部信息披露是最佳的，即对普通个人有益。在本文中，我们根据对邻居行动的依赖的强度（即竞争，预期有理代理人受益，即从全面信息披露中获得更高的回报，我们提供了条件。我们发现，当游戏是对称的，子模型或超模型时，所有代理都会受益于恒星网络结构的信息披露。我们还确定，除非竞争强劲并且外围代理的数量足够小，否则中央代理不仅仅是全面信息披露的外围药物。尽管所有代理商都期望从Ex-Ante获得信息披露，但中央代理商在强烈竞争下对回报状态的许多实现中的信息披露可能会更糟，这表明具有抗风险的中央代理商可能偏爱非信息性信号Ex-Ante。
We consider linear-quadratic-Gaussian (LQG) network games in which agents have quadratic payoffs that depend on their individual and neighbors' actions, and an unknown payoff-relevant state. An information designer determines the fidelity of information revealed to the agents about the payoff state to maximize the social welfare. Prior results show that full information disclosure is optimal under certain assumptions on the payoffs, i.e., it is beneficial for the average individual. In this paper, we provide conditions based on the strength of the dependence of payoffs on neighbors' actions, i.e., competition, under which a rational agent is expected to benefit, i.e., receive higher payoffs, from full information disclosure. We find that all agents benefit from information disclosure for the star network structure when the game is symmetric and submodular or supermodular. We also identify that the central agent benefits more than a peripheral agent from full information disclosure unless the competition is strong and the number of peripheral agents is small enough. Despite the fact that all agents expect to benefit from information disclosure ex-ante, a central agent can be worse-off from information disclosure in many realizations of the payoff state under strong competition, indicating that a risk-averse central agent can prefer uninformative signals ex-ante.
