Two-sided Competing Matching Markets With Complementary Preferences

Yuantong Li, Guang Cheng, Xiaowu Dai

Keywords: Two-sided matching; complementary sampling; bandit; incentive compatible

Abstract: In this paper, we propose a new algorithm for addressing the problem of two-sided matching markets with complementary preferences, where agents' preferences are unknown a priori and must be learned from data. The presence of complementary preferences can lead to instability in the matching process, making this problem challenging to solve. To overcome this challenge, we formulate the problem as a bandit learning framework and propose the Multi-agent Multi-type Thompson Sampling (MMTS) algorithm. The algorithm combines the strengths of Thompson Sampling for exploration with a double matching technique to achieve a stable matching outcome. Our theoretical analysis demonstrates the effectiveness of MMTS as it is able to achieve stability at every matching step and has a sublinear Bayesian regret over time. Our approach provides a useful method for addressing complementary preferences in real-world scenarios.

Primary Area: general machine learning (i.e., none of the above)

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Submission Number: 7857