This paper studies a multi-player, general-sum stochastic game characterized by a dual-stage temporal structure per period. The agents face uncertainty regarding the time-evolving state that is realized at the beginning of each period. During the first stage, agents engage in information acquisition regarding the unknown state. Each agent strategically selects from multiple signaling options, each carrying a distinct cost. The selected signaling rule dispenses private information that determines the type of the agent. In the second stage, the agents play a Bayesian game by taking actions contingent on their private types. We introduce an equilibrium concept, Pipelined Perfect Markov Bayesian Equilibrium (PPME), which incorporates the Markov perfect equilibrium and the perfect Bayesian equilibrium. We propose a novel equilibrium characterization principle termed fixed-point alignment and deliver a set of verifiable necessary and sufficient conditions for any strategy profile to achieve PPME.

翻译：本文研究了一个具有每周期双阶段时间结构的多智能体一般和随机博弈。智能体面临每周期开始时实现的状态随时间演化的不确定性。在第一阶段，智能体针对未知状态进行信息获取。每个智能体策略性地从多个信号选项中选择，每个选项具有不同的成本。选定的信号规则分配私有信息，决定了智能体的类型。在第二阶段，智能体根据其私有类型采取行动，进行贝叶斯博弈。我们引入了一种均衡概念——流水线完美马尔可夫贝叶斯均衡（PPME），它融合了马尔可夫完美均衡与完美贝叶斯均衡。我们提出了一种新颖的均衡刻画原理，称为不动点对齐，并给出了一组可验证的充要条件，用于判定任意策略配置是否达到PPME。