Information Design for Spatial Resource Allocation

TL;DR

This paper investigates how spatial resource platforms can strategically disclose information to optimize resource repositioning and revenue, proposing simple threshold-based policies and algorithms for near-optimal information design.

Contribution

It introduces the concept of monotone partitional information disclosure policies and provides algorithms for designing near-optimal information structures in spatial resource allocation.

Findings

Monotone partitional policies are often optimal for information disclosure.

Threshold-based policies effectively influence resource repositioning decisions.

Algorithms are developed for near-optimal information structure design.

Abstract

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.