# Competing Bandits in Matching Markets

arXiv: https://arxiv.org/abs/1906.05363 · HTML: https://mlsanity.com/paper/1906.05363/

## Key facts

- Cited by 56 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2019.

## Built on by (most cited first, 30 of 56)

- [Misspecified Gaussian Process Bandit Optimization](https://mlsanity.com/paper/2111.05008/) (2021)
- [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)
- [A survey on multi-player bandits](https://mlsanity.com/paper/2211.16275/) (2022)
- [Player-optimal Stable Regret for Bandit Learning in Matching Markets](https://mlsanity.com/paper/2307.10890/) (2023)
- [Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets](https://mlsanity.com/paper/2203.03684/) (2022)
- [Deep Learning for Two-Sided Matching](https://mlsanity.com/paper/2107.03427/) (2021)
- [Learning in Multi-Stage Decentralized Matching Markets](https://mlsanity.com/paper/2102.06988/) (2021)
- [Decentralized, Communication- and Coordination-free Learning in Structured Matching Markets](https://mlsanity.com/paper/2206.02344/) (2022)
- [Thompson Sampling for Bandit Learning in Matching Markets](https://mlsanity.com/paper/2204.12048/) (2022)
- [Multi-Player Multi-Armed Bandits with Finite Shareable Resources Arms: Learning Algorithms & Applications](https://mlsanity.com/paper/2204.13502/) (2022)
- [Decentralized Learning in Online Queuing Systems](https://mlsanity.com/paper/2106.04228/) (2021)
- [Modeling Recommender Ecosystems: Research Challenges at the Intersection of Mechanism Design, Reinforcement Learning and Generative Models](https://mlsanity.com/paper/2309.06375/) (2023)
- [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)
- [Competing for Shareable Arms in Multi-Player Multi-Armed Bandits](https://mlsanity.com/paper/2305.19158/) (2023)
- [Explore-then-Commit Algorithms for Decentralized Two-Sided Matching Markets](https://mlsanity.com/paper/2408.08690/) (2024)
- [Decentralized Competing Bandits in Non-Stationary Matching Markets](https://mlsanity.com/paper/2206.00120/) (2022)
- [Two-sided Competing Matching Recommendation Markets With Quota and Complementary Preferences Constraints](https://mlsanity.com/paper/2301.10230/) (2023)
- [Probably Correct Optimal Stable Matching for Two-Sided Markets Under Uncertainty](https://mlsanity.com/paper/2501.03018/) (2025)
- [Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits](https://mlsanity.com/paper/2305.18784/) (2023)
- [Improved Offline Contextual Bandits with Second-Order Bounds: Betting and Freezing](https://mlsanity.com/paper/2502.10826/) (2025)
- [Competing Bandits in Matching Markets via Super Stability](https://mlsanity.com/paper/2506.15926/) (2025)
- [Improving Social Welfare While Preserving Autonomy via a Pareto Mediator](https://mlsanity.com/paper/2106.03927/) (2021)
- [Competing Bandits in Time Varying Matching Markets](https://mlsanity.com/paper/2210.11692/) (2022)
- [Active Learning for Fair and Stable Online Allocations](https://mlsanity.com/paper/2406.14784/) (2024)
- [Multiplayer Information Asymmetric Bandits in Metric Spaces](https://mlsanity.com/paper/2503.08004/) (2025)
- [Multiplayer Information Asymmetric Contextual Bandits](https://mlsanity.com/paper/2503.08961/) (2025)
- [Stable Matching with Ties: Approximation Ratios and Learning](https://mlsanity.com/paper/2411.03270/) (2024)

## Abstract (arXiv)

Stable matching, a classical model for two-sided markets, has long been studied with little consideration for how each side's preferences are learned. With the advent of massive online markets powered by data-driven matching platforms, it has become necessary to better understand the interplay between learning and market objectives. We propose a statistical learning model in which one side of the market does not have a priori knowledge about its preferences for the other side and is required to learn these from stochastic rewards. Our model extends the standard multi-armed bandits framework to multiple players, with the added feature that arms have preferences over players. We study both centralized and decentralized approaches to this problem and show surprising exploration-exploitation trade-offs compared to the single player multi-armed bandits setting.