Feature-based Uncertainty Model for School Choice

TL;DR

This paper introduces a feature-based uncertainty model for school choice, where students' preferences over colleges are based on features with uncertain weights, aiming to improve stability and incentive compatibility.

Contribution

It proposes a novel probabilistic preference model based on college features and analyzes the trade-offs between stability and incentive compatibility in deferred acceptance algorithms.

Findings

DA with expected ranking prioritization achieves a $(1/n)^n$ approximation ratio on stability.

Iterated comparison vector DA guarantees the strongest incentive compatibility.

Additional results for specific model restrictions are provided.

Abstract

In this work, we consider a school choice scenario where a student does not exactly know which college is better for her. Although it is hard for a student to obtain an exact preference, she can usually compare specific features of colleges, such as reputation, location, and campus facilities. Motivated by this, we propose a feature-based uncertainty model for school choice where a student's preference is based on a linear combination of her utilities over different features, and the coefficients of the combination are treated as random variables. Our main goal is to achieve a higher probability of stability (ProS) and incentive compatibility (IC) for students. Unfortunately, these two goals are incompatible in general. We show that a student-proposing deferred acceptance (DA) that prioritizes colleges with higher expected ranking can achieve a worst-case approximation ratio of $(1/n)^n$ on ProS, while a DA with a carefully defined iterated comparison vector can guarantee the strongest achievable form of IC. Finally, we provide additional results for some specific restrictions on the model.