空间资源配置的信息设计
Information Design for Spatial Resource Allocation
AI总结：
针对空间分布资源可策略性迁移的平台，本文研究通过单调分区信息披露影响资源重定位以提升佣金收入，证明其在多类实际情形下最优，并给出一般情形下求取近优信息结构的算法。
摘要：
本文研究资源与任务在空间上分布、且资源能够策略性移动到不同地点以获取更高收益的平台。每个地点的服务价格取决于在场资源数量以及被建模为随机状态的市场规模。我们关注平台如何利用关于潜在状态的信息来影响资源重新定位决策，并最终提高佣金收入。我们证明，在许多具有实际相关性的情形中，一种简单的单调分区信息披露策略是最优的。该策略揭示低于某一阈值以及高于第二个（更高）阈值的状态实现，而将介于两者之间的所有状态合并，并把它们映射为唯一的信号实现。我们还提供了算法方法，用于在一般情形下获得具有单调分区性质的（近）最优信息结构。
英文摘要：
In this paper, we study platforms where resources and jobs are spatially distributed, and resources have the flexibility to strategically move to different locations for better payoffs. The price of the service at each location depends on the number of resources present and the market size, which is modeled as a random state. Our focus is on how the platform can utilize information about the underlying state to influence resource repositioning decisions and ultimately increase commission revenues. We establish that in many practically relevant settings a simple monotone partitional information disclosure policy is optimal. This policy reveals state realizations below a threshold and above a second (higher) threshold, and pools all states in between and maps them to a unique signal realization. We also provide algorithmic approaches for obtaining (near-)optimal information structures that are monotone partitional in general settings.
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。
