Desirable Rankings

TL;DR

This paper introduces a new method for aggregating preferences into collective rankings that accounts for agent-alternative matches, ensuring alternatives desired by agents are ranked higher, with proven convergence and practical application to ranking medical programs.

Contribution

The paper proposes axioms and an algorithm for desirable rankings that incorporate agent-alternative matches, with convergence proofs and real-world application to medical program rankings.

Findings

Desirable rankings converge to true quality rankings as the market size increases.

The proposed method outperforms revealed preference and Borda count benchmarks.

Application to Chilean medical programs demonstrates practical effectiveness.

Abstract

We study the problem of aggregating individual preferences over alternatives into a collective ranking. A distinctive feature of our setting is that agents are matched to alternatives. Applications include rankings of colleges or academic journals. The foundation of our approach is that alternatives agents desire -- that is, those they rank above their match -- should also be ranked higher socially. We introduce axioms to formalize this idea and call rankings that satisfy them desirable. We develop an algorithm to construct desirable rankings and prove that, as the market becomes large, desirable rankings converge to the true underlying ranking of the alternatives by quality. We support this convergence result through simulations and demonstrate the practical usefulness of our approach by ranking Chilean medical programs with data from their centralized admission system. Finally, we compare performance and show that our approach outperforms two benchmarks: revealed preference rankings and Borda counts.