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Incentive Compatibility of Large Centralized Matching Markets

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  • SangMok Lee

Abstract

We study the manipulability of stable matching mechanisms. To quantify incentives to manipulate stable mechanisms, we consider markets with random cardinal utilities, which induce ordinal preferences over match partners. We show that most agents in large matching markets are close to being indifferent of overall stable matchings. In one-to-one matching, the utility gain by manipulating a stable mechanism does not exceed the gap between utilities from the best and worst stable partners. Thus, most agents in a large market would not have significant incentives to manipulate stable mechanisms. The incentive compatibility extends to many-to-one matching when agents employ truncation strategies and capacity manipulations in a Gale—Shapley mechanism.

Suggested Citation

  • SangMok Lee, 2017. "Incentive Compatibility of Large Centralized Matching Markets," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 84(1), pages 444-463.
    • Handle: RePEc:oup:restud:v:84:y:2017:i:1:p:444-463.
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    File URL: http://hdl.handle.net/10.1093/restud/rdw041
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    Citations

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    Cited by:

    1. Martin Van der Linden, 2019. "Deferred acceptance is minimally manipulable," International Journal of Game Theory, Springer;Game Theory Society, vol. 48(2), pages 609-645, June.
    2. Ortega, Josué, 2018. "Social integration in two-sided matching markets," Journal of Mathematical Economics, Elsevier, vol. 78(C), pages 119-126.
    3. Che, Yeon-Koo & Tercieux, Olivier, 2018. "Payoff equivalence of efficient mechanisms in large matching markets," Theoretical Economics, Econometric Society, vol. 13(1), January.
    4. Max Albert, 2022. "How to Escape from Model Platonism in Economics: Critical Assumptions, Robust Conclusions, and Approximate Explanations," Homo Oeconomicus: Journal of Behavioral and Institutional Economics, Springer, vol. 39(1), pages 37-68, October.
    5. Aaron L. Bodoh-Creed, 2020. "Optimizing for Distributional Goals in School Choice Problems," Management Science, INFORMS, vol. 66(8), pages 3657-3676, August.
    6. Tomoya Tajika & Tomoya Kazumura, 2019. "Non-manipulability of uniform price auctions with a large number of objects," International Journal of Game Theory, Springer;Game Theory Society, vol. 48(2), pages 543-569, June.
    7. Itai Ashlagi & Mark Braverman & Yash Kanoria & Peng Shi, 2020. "Clearing Matching Markets Efficiently: Informative Signals and Match Recommendations," Management Science, INFORMS, vol. 66(5), pages 2163-2193, May.
    8. Eduardo M Azevedo & Eric Budish, 2019. "Strategy-proofness in the Large," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 86(1), pages 81-116.
    9. T. Tony Ke & Yuting Zhu, 2021. "Cheap Talk on Freelance Platforms," Management Science, INFORMS, vol. 67(9), pages 5901-5920, September.
    10. Christian Haas & Margeret Hall, 2019. "Two-Sided Matching for mentor-mentee allocations—Algorithms and manipulation strategies," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-27, March.

    More about this item

    Keywords

    Two-sided matching; Stable matching mechanism; Large market; Random bipartite graph;
    All these keywords.

    JEL classification:

    • C78 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Bargaining Theory; Matching Theory
    • D61 - Microeconomics - - Welfare Economics - - - Allocative Efficiency; Cost-Benefit Analysis
    • D78 - Microeconomics - - Analysis of Collective Decision-Making - - - Positive Analysis of Policy Formulation and Implementation

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