(鲁棒) 信息获取设计
(Robust) Information Acquisition Design
AI总结：
本文研究信息获取博弈的设计，分析可实施分配的激励属性，并证明鲁棒性等价于机制的实验中性。通过一般物品分配和学校选择应用，展示鲁棒分配的广泛适用性。
摘要：
我们研究信息获取博弈的设计——环境中设计者将行动委托给发送者选择实验和实现的信号。我们为该设定开发了一个揭示性原则，并表征了可实施分配的激励属性。我们随后研究鲁棒分配——无论先验条件如何都能实施的分配，并证明鲁棒性等价于机制的实验中性。最后，我们考虑两个应用。首先，在一般物品分配问题中，我们表明所有有效分配都是鲁棒的，即使代理偏好具有状态依赖的外部选项和分配外部性。其次，我们将模型应用于学校选择，并发现当学校偏好依赖于学生未知能力时，延迟接受机制获得新的信息解释。
英文摘要：
We study the design of information acquisition games-environments where a designer contracts their action on Sender's choice of experiment and the realized signals about some state. We develop a revelation-like principle for this setting and characterize the incentive properties of implementable allocations. We next turn to robust allocations-those that can be implemented regardless of the prior-and show that robustness is equivalent to experiment-neutrality of the mechanism. We conclude by considering two applications. First, for general good allocation problems, we show all efficient allocations are robust, even when agent preferences feature state-dependent outside options and allocation externalities. Second, we apply our model to school choice and uncover a novel informational justification for deferred acceptance when school preferences depend on students' unknown ability.
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。
