算法辅助决策中的说服、授权与私人信息
Persuasion, Delegation, and Private Information in Algorithm-Assisted Decisions
AI总结：
本文研究算法辅助决策中的最优算法设计与授权规则，发现授权仅在委托人与代理人决策一致时最优，信息量最大的算法可能次优，且善意增加信息或保留人类参与的政策可能反而降低决策质量。
摘要：
一个委托人设计一种算法，该算法生成关于二元状态的公开可观察预测。她必须决定是直接基于该预测采取行动，还是将决策授权给一个拥有私人信息但可能存在偏好不一致的代理人。我们研究了此类环境中预测算法和授权规则的最优设计。得出了三个关键发现：（1）当且仅当委托人在观察到代理人的信息后会做出与代理人相同的二元决策时，授权才是最优的。（2）即使委托人可以依据算法的预测采取行动，提供信息量最大的算法也可能是次优的。相反，最优算法可能会提供更多关于某一状态的信息，并限制关于另一状态的信息。（3）旨在提供更多信息的善意政策，例如保留“人在回路”或要求最大预测准确度，与没有人类或没有算法辅助的系统相比，可能会严格恶化决策质量。这些发现预测，如果不采取措施来缓解算法与人类决策者之间常见的偏好不一致，人机协作的表现将会不佳。
英文摘要：
A principal designs an algorithm that generates a publicly observable prediction of a binary state. She must decide whether to act directly based on the prediction or to delegate the decision to an agent with private information but potential misalignment. We study the optimal design of the prediction algorithm and the delegation rule in such environments. Three key findings emerge: (1) Delegation is optimal if and only if the principal would make the same binary decision as the agent had she observed the agent's information. (2) Providing the most informative algorithm may be suboptimal even if the principal can act on the algorithm's prediction. Instead, the optimal algorithm may provide more information about one state and restrict information about the other. (3) Well-intentioned policies aiming to provide more information, such as keeping a "human-in-the-loop" or requiring maximal prediction accuracy, could strictly worsen decision quality compared to systems with no human or no algorithmic assistance. These findings predict the underperformance of human-machine collaborations if no measures are taken to mitigate common preference misalignment between algorithms and human decision-makers.
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。
