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.

翻译：本文研究了资源和任务在空间分布的场景中，资源为获取更高收益可灵活战略移动至不同位置的平台问题。各服务地点的价格取决于该地资源数量及市场容量，其中市场容量被建模为随机状态。我们重点探讨平台如何利用潜在状态的信息来影响资源重新定位决策，并最终增加佣金收入。我们证明，在许多实际相关场景中，采用简单的单调分割型信息披露策略是最优的。该策略会披露低于某一阈值和高于另一（更高）阈值的状态实现值，同时将介于两者之间的所有状态合并并映射为唯一信号实现值。我们还提供了算法方法，用于在通用场景中获取单调分割的（近似）最优信息结构。