EMERGENT: Efficient and Manipulation-resistant Matching using GFlowNets

TL;DR

This paper introduces EMERGENT, a GFlowNets-based algorithm for one-sided matching that achieves a better balance between efficiency and resistance to manipulation, outperforming traditional methods in experiments.

Contribution

The paper presents a novel application of GFlowNets to one-sided matching, achieving both efficiency and manipulation resistance simultaneously.

Findings

EMERGENT outperforms RSD in rank efficiency.

It significantly reduces strategic manipulation compared to RM and PS.

Experiments demonstrate the effectiveness of GFlowNets in social choice mechanisms.

Abstract

The design of fair and efficient algorithms for allocating public resources, such as school admissions, housing, or medical residency, has a profound social impact. In one-sided matching problems, where individuals are assigned to items based on ranked preferences, a fundamental trade-off exists between efficiency and strategyproofness. Existing algorithms like Random Serial Dictatorship (RSD), Probabilistic Serial (PS), and Rank Minimization (RM) capture only one side of this trade-off: RSD is strategyproof but inefficient, while PS and RM are efficient but incentivize manipulation. We propose EMERGENT, a novel application of Generative Flow Networks (GFlowNets) to one-sided matching, leveraging its ability to sample diverse, high-reward solutions. In our approach, efficient and manipulation-resistant matches emerge naturally: high-reward solutions yield efficient matches, while the stochasticity of GFlowNets-based outputs reduces incentives for manipulation. Experiments show that EMERGENT outperforms RSD in rank efficiency while significantly reducing strategic vulnerability compared to matches produced by RM and PS. Our work highlights the potential of GFlowNets for applications involving social choice mechanisms, where it is crucial to balance efficiency and manipulability.