Facilitating Matches on Allocation Platforms

TL;DR

This paper explores how an allocation platform can improve overall utility by selectively relaxing agents' restrictions, ensuring no worse-off agents, and optimizing the process through formal models, algorithms, and real-world data analysis.

Contribution

It introduces a formal framework for facilitation in allocation platforms, proposes polynomial algorithms for optimization, and empirically demonstrates the benefits of restriction relaxation.

Findings

Relaxation of restrictions can significantly improve social welfare.

Algorithms effectively optimize restriction relaxation under various constraints.

Empirical results show practical benefits on real-world datasets.

Abstract

We consider a setting where goods are allocated to agents by way of an allocation platform (e.g., a matching platform). An ``allocation facilitator'' aims to increase the overall utility/social-good of the allocation by encouraging (some of the) agents to relax (some of) their restrictions. At the same time, the advice must not hurt agents who would otherwise be better off. Additionally, the facilitator may be constrained by a ``bound'' (a.k.a. `budget'), limiting the number and/or type of restrictions it may seek to relax. We consider the facilitator's optimization problem of choosing an optimal set of restrictions to request to relax under the aforementioned constraints. Our contributions are three-fold: (i) We provide a formal definition of the problem, including the participation guarantees to which the facilitator should adhere. We define a hierarchy of participation guarantees and also consider several social-good functions. (ii) We provide polynomial algorithms for solving various versions of the associated optimization problems, including one-to-one and many-to-one allocation settings. (iii) We demonstrate the benefits of such facilitation and relaxation, and the implications of the different participation guarantees, using extensive experimentation on three real-world datasets.