Approximating Gains-from-Trade in Matching Markets

Moshe Babaioff; Aviad Rubinstein; Xizhi Tan; Kangning Wang

doi:10.1145/3798129.3800786

Approximating Gains-from-Trade in Matching Markets

Moshe Babaioff^*
, Aviad Rubinstein
, Xizhi Tan
, Kangning Wang

^*Corresponding author for this work

The Rachel and Selim Benin School of Engineering and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

A central challenge in mechanism design is to develop truthful trade mechanisms that maximize the expected gains-from-trade (GFT) in two-sided markets with strategic agents. As achieving the full GFT is generally impossible, much of the literature has focused on constant-factor approximations. Existing results, however, are limited to the highly structured settings of bilateral trade and double auctions, in which every buyer can trade with every seller. We consider the significantly more general setting of two-sided matching markets with arbitrary downward-closed constraints on the family of allowed matchings. For this setting, we present a simple randomized truthful mechanism that guarantees a constant-factor approximation to the optimal expected GFT. This result also resolves an open problem posed by Cai, Goldner, Ma, and Zhao (2021).

Original language	English
Title of host publication	STOC 2026 - Proceedings of the 58th Annual ACM Symposium on Theory of Computing
Editors	Aditya Bhaskara, Artur Czumaj
Publisher	Association for Computing Machinery
Pages	710-721
Number of pages	12
ISBN (Electronic)	9798400725364
DOIs	https://doi.org/10.1145/3798129.3800786
State	Published - 9 Jun 2026
Event	58th Annual ACM Symposium on Theory of Computing, STOC 2026 - Salt Lake City, United States Duration: 22 Jun 2026 → 26 Jun 2026

Publication series

Name	Proceedings of the Annual ACM Symposium on Theory of Computing
ISSN (Print)	0737-8017

Conference

Conference	58th Annual ACM Symposium on Theory of Computing, STOC 2026
Country/Territory	United States
City	Salt Lake City
Period	22/06/26 → 26/06/26

Bibliographical note

Publisher Copyright:
© 2026 Copyright held by the owner/author(s).

Keywords

Approximation Algorithms
Bilateral Trade
Mechanism Design

Access to Document

10.1145/3798129.3800786

Cite this

Babaioff, M., Rubinstein, A., Tan, X., & Wang, K. (2026). Approximating Gains-from-Trade in Matching Markets. In A. Bhaskara, & A. Czumaj (Eds.), STOC 2026 - Proceedings of the 58th Annual ACM Symposium on Theory of Computing (pp. 710-721). (Proceedings of the Annual ACM Symposium on Theory of Computing). Association for Computing Machinery. https://doi.org/10.1145/3798129.3800786

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Babaioff, M, Rubinstein, A, Tan, X & Wang, K 2026, Approximating Gains-from-Trade in Matching Markets. in A Bhaskara & A Czumaj (eds), STOC 2026 - Proceedings of the 58th Annual ACM Symposium on Theory of Computing. Proceedings of the Annual ACM Symposium on Theory of Computing, Association for Computing Machinery, pp. 710-721, 58th Annual ACM Symposium on Theory of Computing, STOC 2026, Salt Lake City, United States, 22/06/26. https://doi.org/10.1145/3798129.3800786

Approximating Gains-from-Trade in Matching Markets. / Babaioff, Moshe; Rubinstein, Aviad; Tan, Xizhi et al.
STOC 2026 - Proceedings of the 58th Annual ACM Symposium on Theory of Computing. ed. / Aditya Bhaskara; Artur Czumaj. Association for Computing Machinery, 2026. p. 710-721 (Proceedings of the Annual ACM Symposium on Theory of Computing).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Approximating Gains-from-Trade in Matching Markets

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