# Bandit Learning in Matching Markets: Utilitarian and Rawlsian Perspectives

arXiv: https://arxiv.org/abs/2412.00301 · HTML: https://mlsanity.com/paper/2412.00301/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 31 references that are in the map.
- First posted on arXiv 30 Nov 2024.

## Builds on (most cited first, 11 of 11)

- [Competing Bandits in Matching Markets](https://mlsanity.com/paper/1906.05363/) (2019)
- [Bandit Learning in Decentralized Matching Markets](https://mlsanity.com/paper/2012.07348/) (2020)
- [Learning Equilibria in Matching Markets from Bandit Feedback](https://mlsanity.com/paper/2108.08843/) (2021)
- [Beyond log2(T) Regret for Decentralized Bandits in Matching Markets](https://mlsanity.com/paper/2103.07501/) (2021)
- [Regret, stability & fairness in matching markets with bandit learners](https://mlsanity.com/paper/2102.06246/) (2021)
- [Player-optimal Stable Regret for Bandit Learning in Matching Markets](https://mlsanity.com/paper/2307.10890/) (2023)
- [Deep Learning for Two-Sided Matching](https://mlsanity.com/paper/2107.03427/) (2021)
- [Decentralized, Communication- and Coordination-free Learning in Structured Matching Markets](https://mlsanity.com/paper/2206.02344/) (2022)
- [Improved Bandits in Many-to-one Matching Markets with Incentive Compatibility](https://mlsanity.com/paper/2401.01528/) (2024)
- [Putting Gale & Shapley to Work: Guaranteeing Stability Through Learning](https://mlsanity.com/paper/2410.04376/) (2024)
- [Using Stable Matching to Optimize the Balance between Accuracy and Diversity in Recommendation](https://mlsanity.com/paper/2006.03715/) (2020)

## Built on by (most cited first, 2 of 2)

- [Rawlsian many-to-one matching with non-linear utility](https://mlsanity.com/paper/2511.02533/) (2025)
- [Exact Regret Frontiers and Externality Scheduling in Centralized Serial-Dictatorship Bandits](https://mlsanity.com/paper/2609.19963/) (2026)

## Abstract (arXiv)

Two-sided matching markets have demonstrated significant impact in many real-world applications, including school choice, medical residency placement, electric vehicle charging, ride sharing, and recommender systems. However, traditional models often assume that preferences are known, which is not always the case in modern markets, where preferences are unknown and must be learned. For example, a company may not know its preference over all job applicants a priori in online markets. Recent research has modeled matching markets as multi-armed bandit (MAB) problem and primarily focused on optimizing matching for one side of the market, while often resulting in a pessimal solution for the other side. In this paper, we adopt a welfarist approach for both sides of the market, focusing on two metrics: (1) Utilitarian welfare and (2) Rawlsian welfare, while maintaining market stability. For these metrics, we propose algorithms based on epoch Explore-Then-Commit (ETC) and analyze their regret bounds. Finally, we conduct simulated experiments to evaluate both welfare and market stability.