信号博弈中的支付信息与学习
Payoff Information and Learning in Signaling Games
摘要：
我们在信号博弈的非均衡学习模型中加入了博弈者知道对手的支付函数和理性的假设。代理人出生到球员的角色，并发挥对随机对手的每一个时期。没有经验的代理人不确定对手的游戏的流行分布，但相信对手从来没有选择条件劣势策略。智能体参与主动学习，并根据个人观察更新信念。回报信息可以改进或扩展学习预测，因为耐心的年轻人的实验激励取决于他们认为可信的接收器响应。我们表明，与回报的知识，长期学习成果的限制集上有界的合理性兼容的均衡（RCE），并有界的均匀RCE以下。RCE完善了直觉准则（Cho和Kreps，1987），并包括所有神圣平衡（Banks和Sobel，1987）。一致的RCE有时但不总是存在，并暗示普遍的神圣平衡。
英文摘要：
We add the assumption that players know their opponents' payoff functions and rationality to a model of non-equilibrium learning in signaling games. Agents are born into player roles and play against random opponents every period. Inexperienced agents are uncertain about the prevailing distribution of opponents' play, but believe that opponents never choose conditionally dominated strategies. Agents engage in active learning and update beliefs based on personal observations. Payoff information can refine or expand learning predictions, since patient young senders' experimentation incentives depend on which receiver responses they deem plausible. We show that with payoff knowledge, the limiting set of long-run learning outcomes is bounded above by rationality-compatible equilibria (RCE), and bounded below by uniform RCE. RCE refine the Intuitive Criterion (Cho and Kreps, 1987) and include all divine equilibria (Banks and Sobel, 1987). Uniform RCE sometimes but not always exists, and implies universally divine equilibrium.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
