# Learning Strategies in Decentralized Matching Markets under Uncertain Preferences

arXiv: https://arxiv.org/abs/2011.00159 · HTML: https://mlsanity.com/paper/2011.00159/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2020.

## Built on by (most cited first, 14 of 14)

- [Beyond log2(T) Regret for Decentralized Bandits in Matching Markets](https://mlsanity.com/paper/2103.07501/) (2021)
- [Player-optimal Stable Regret for Bandit Learning in Matching Markets](https://mlsanity.com/paper/2307.10890/) (2023)
- [Learning in Multi-Stage Decentralized Matching Markets](https://mlsanity.com/paper/2102.06988/) (2021)
- [Thompson Sampling for Bandit Learning in Matching Markets](https://mlsanity.com/paper/2204.12048/) (2022)
- [Decentralized Competing Bandits in Non-Stationary Matching Markets](https://mlsanity.com/paper/2206.00120/) (2022)
- [Incentive-Aware Recommender Systems in Two-Sided Markets](https://mlsanity.com/paper/2211.15381/) (2022)
- [Two-sided Competing Matching Recommendation Markets With Quota and Complementary Preferences Constraints](https://mlsanity.com/paper/2301.10230/) (2023)
- [Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning](https://mlsanity.com/paper/2205.02450/) (2022)
- [Online Auction Design Using Distribution-Free Uncertainty Quantification with Applications to E-Commerce](https://mlsanity.com/paper/2405.07038/) (2024)
- [Wasserstein gradient flow for optimal probability measure decomposition](https://mlsanity.com/paper/2406.00914/) (2024)
- [Learn then Decide: A Learning Approach for Designing Data Marketplaces](https://mlsanity.com/paper/2503.10773/) (2025)
- [Optimal Analysis for Bandit Learning in Matching Markets with Serial Dictatorship](https://mlsanity.com/paper/2512.06758/) (2025)
- [An ODE Model for Dynamic Matching in Heterogeneous Networks](https://mlsanity.com/paper/2302.09757/) (2023)
- [From Preference to Reciprocity: Decentralized Matching with Empirically Grounded LLM-agent Based Modeling](https://mlsanity.com/paper/2609.34679/) (2026)

## Abstract (arXiv)

We study the problem of decision-making in the setting of a scarcity of shared resources when the preferences of agents are unknown a priori and must be learned from data. Taking the two-sided matching market as a running example, we focus on the decentralized setting, where agents do not share their learned preferences with a central authority. Our approach is based on the representation of preferences in a reproducing kernel Hilbert space, and a learning algorithm for preferences that accounts for uncertainty due to the competition among the agents in the market. Under regularity conditions, we show that our estimator of preferences converges at a minimax optimal rate. Given this result, we derive optimal strategies that maximize agents' expected payoffs and we calibrate the uncertain state by taking opportunity costs into account. We also derive an incentive-compatibility property and show that the outcome from the learned strategies has a stability property. Finally, we prove a fairness property that asserts that there exists no justified envy according to the learned strategies.