• Corpus ID: 239768672

Learning in Multi-Stage Decentralized Matching Markets

@inproceedings{Dai2021LearningIM,
  title={Learning in Multi-Stage Decentralized Matching Markets},
  author={Xiaowu Dai and Michael I. Jordan},
  booktitle={Neural Information Processing Systems},
  year={2021}
}

• Xiaowu Dai, Michael I. Jordan
• Published in
  Neural Information Processing…
  13 February 2021
• Economics

Matching markets are often organized in a multi-stage and decentralized manner. Moreover, participants in real-world matching markets often have uncertain preferences. This article develops a framework for learning optimal strategies in such settings, based on a nonparametric statistical approach and variational analysis. We propose an efficient algorithm, built upon concepts of “lower uncertainty bound” and “calibrated decentralized matching,” for maximizing the participants’ expected payoff. We show that there exists a welfare-versus-fairness trade-off that is charac-terized by the uncertainty level of acceptance. Participants will strategically act in favor of a low uncertainty level to reduce competition and increase expected payoff. We prove that participants can be better off with multi-stage matching compared to single-stage matching. We demonstrate aspects of the theoretical predictions through simulations and an experiment using real data from college admissions.