Adaptive Priority Mechanisms

TL;DR

This paper introduces adaptive priority mechanisms (APM) that optimize resource allocation by balancing match quality and diversity, demonstrating their superiority over traditional methods through theoretical analysis and empirical data from Chicago Public Schools.

Contribution

The paper develops an optimal adaptive priority mechanism framework and characterizes when traditional priority and quota mechanisms are optimal, supported by empirical evidence.

Findings

APM outperforms traditional mechanisms in diverse settings

Optimal APM achieves stable and efficient matchings

Significant gains observed in Chicago Public Schools data

Abstract

How should authorities that care about match quality and diversity allocate resources when they are uncertain about the market? We introduce adaptive priority mechanisms (APM) that prioritize agents based on both their scores and characteristics. We derive an APM that is optimal and show that the ubiquitous priority and quota mechanisms are optimal if and only if the authority is risk-neutral or extremely risk-averse over diversity, respectively. With many authorities, each authority using the optimal APM is dominant and implements the unique stable matching. Using Chicago Public Schools data, we find that the gains from adopting APM may be considerable.