This work considers a novel information design problem and studies how the craft of payoff-relevant environmental signals solely can influence the behaviors of intelligent agents. The agents' strategic interactions are captured by an incomplete-information Markov game, in which each agent first selects one environmental signal from multiple signal sources as additional payoff-relevant information and then takes an action. There is a rational information designer (principal) who possesses one signal source and aims to control the equilibrium behaviors of the agents by designing the information structure of her signals sent to the agents. An obedient principle is established which states that it is without loss of generality to focus on the direct information design when the information design incentivizes each agent to select the signal sent by the principal, such that the design process avoids the predictions of the agents' strategic selection behaviors. Based on the obedient principle, we introduce the design protocol given a goal of the principal referred to as obedient implementability (OIL) and study a Myersonian information design that characterizes the OIL in a class of obedient sequential Markov perfect Bayesian equilibria (O-SMPBE). A framework is proposed based on an approach which we refer to as the fixed-point alignment that incentivizes the agents to choose the signal sent by the principal, makes sure that the agents' policy profile of taking actions is the policy component of an O-SMPBE, and the principal's goal is achieved. The proposed approach can be applied to elicit desired behaviors of multi-agent systems in competing as well as cooperating settings and be extended to heterogeneous stochastic games in the complete- and the incomplete-information environments.

中文翻译：

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动态多Agent系统的信息设计

这项工作考虑了一个新颖的信息设计问题，并研究了与收益相关的环境信号的处理方式如何单独影响智能主体的行为。代理商的战略互动由不完整的信息马尔可夫博弈捕获，其中每个代理商首先从多个信号源中选择一个环境信号作为其他与收益相关的信息，然后采取行动。有一个理性的信息设计者（主要的），他拥有一个信号源，旨在通过设计发送给代理的信号的信息结构来控制代理的均衡行为。建立了一个服从性原则，该原则规定，当信息设计激励每个代理选择委托人发送的信号时，专注于直接信息设计是不失一般性的，因此设计过程避免了对代理策略的预测选择行为。基于服从原则，我们介绍了给定目标的目标即服从可实现性（OIL）的设计协议，并研究了以一类服从序列马尔可夫完美贝叶斯均衡（O-SMPBE）来表征该OIL的Myersonian信息设计。提出了一种基于框架的框架，该框架称为定点对齐方式，可以激励代理商选择委托人发送的信号，确保代理商的 采取行动的政策概况是O-SMPBE的政策组成部分，并且实现了负责人的目标。可以将所提出的方法应用于在竞争以及合作环境中引发多智能体系统的期望行为，并且可以将其扩展到完整和不完整信息环境中的异构随机游戏。