稳健的信任
Robust Trust
AI总结：
研究者分析了在信息不对称情况下，代理人如何根据私人信息和可能有偏见的顾问建议做出稳健决策，通过信任区域政策最大化最坏情况下的预期收益。
摘要：
一个代理人根据她的私人信息和一个知情但可能有偏见的顾问的建议来选择行动。以已知的概率，顾问会真实报告他的信号；剩余的概率，他可以发送任何消息。我们刻画了最优稳健决策规则，这些规则最大化代理人的最坏情况预期收益。每一个最优规则等同于信念空间中的信任区域政策：顾问报告的信念如果落在信任区域内则被采信，否则会被截断到信任区域的边界。我们推导出对齐阈值，这些阈值之上建议是严格有价值的，并在二元状态和二元行动环境中完全刻画了解决方案。
英文摘要：
An agent chooses an action based on her private information and a recommendation from an informed but potentially misaligned adviser. With a known probability, the adviser truthfully reports his signal; with the remaining probability, he can send any message. We characterize optimal robust decision rules that maximize the agent's worst-case expected payoff. Every optimal rule is equivalent to a trust-region policy in belief space: the adviser's reported beliefs are taken at face value if they fall within the trust region but are otherwise clipped to the trust region's boundary. We derive alignment thresholds above which advice is strictly valuable and fully characterize the solution in both binary-state and binary-action environments.
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。
