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Matching in the large: An experimental study

Author

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  • Chen, Yan
  • Jiang, Ming
  • Kesten, Onur
  • Robin, Stéphane
  • Zhu, Min

Abstract

We compare the performance of the Boston Immediate Acceptance (IA) and Gale–Shapley Deferred Acceptance (DA) mechanisms in a laboratory setting where we increase the number of participants per match. In our experiment, we first increase the number of students per match from 4 to 40; when we do so, participant truth-telling increases under DA but decreases under IA, leading to a decrease in efficiency under both mechanisms. Furthermore, we find that DA remains more stable than IA, regardless of scale. We then further increase the number of participants per match to 4,000 through the introduction of robots. When robots report their preferences truthfully, we find that scale has no effect on human best response behavior. By contrast, when we program the robots to draw their strategies from the distribution of empirical human strategies, we find that our increase in scale increases human ex-post best responses under both mechanisms.

Suggested Citation

  • Chen, Yan & Jiang, Ming & Kesten, Onur & Robin, Stéphane & Zhu, Min, 2018. "Matching in the large: An experimental study," Games and Economic Behavior, Elsevier, vol. 110(C), pages 295-317.
    • Handle: RePEc:eee:gamebe:v:110:y:2018:i:c:p:295-317
    DOI: 10.1016/j.geb.2018.04.004
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    2. He, Simin & Wu, Jiabin & Zhang, Hanzhe, 2021. "Experimental and Noncooperative Analyses of Decentralized Matching with Transfers," Working Papers 2021-2, Michigan State University, Department of Economics.
    3. Hakimov, Rustamdjan & Kübler, Dorothea, 2019. "Experiments On Matching Markets: A Survey," Rationality and Competition Discussion Paper Series 153, CRC TRR 190 Rationality and Competition.
    4. Yan Chen & Peter Cramton & John A. List & Axel Ockenfels, 2021. "Market Design, Human Behavior, and Management," Management Science, INFORMS, vol. 67(9), pages 5317-5348, September.
    5. Adam Kapor & Mohit Karnani & Christopher Neilson, 2019. "Negative Externalities of Off Platform Options and the Efficiency of Centralized Assignment Mechanisms," Working Papers 635, Princeton University, Department of Economics, Industrial Relations Section..
    6. Christoph March, 2019. "The Behavioral Economics of Artificial Intelligence: Lessons from Experiments with Computer Players," CESifo Working Paper Series 7926, CESifo.
    7. Di Feng & Yun Liu & Gaowang Wang, 2023. "On the Asymptotic Performance of Affirmative Actions in School Choice," Annals of Economics and Finance, Society for AEF, vol. 24(2), pages 289-307, November.
    8. Hoyer, B. & Stroh-Maraun, N., 2020. "Matching strategies of heterogeneous agents under incomplete information in a university clearinghouse," Games and Economic Behavior, Elsevier, vol. 121(C), pages 453-481.
    9. Chen, Yan & He, YingHua, 2021. "Information acquisition and provision in school choice: An experimental study," Journal of Economic Theory, Elsevier, vol. 197(C).
    10. Hakimov, Rustamdjan & Kübler, Dorothea, 2021. "Experiments on centralized school choice and college admissions: a survey," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 24(2), pages 434-488.
    11. Eric Budish & Judd B. Kessler, 2022. "Can Market Participants Report Their Preferences Accurately (Enough)?," Management Science, INFORMS, vol. 68(2), pages 1107-1130, February.
    12. Daniel Stephenson, 2022. "Assignment feedback in school choice mechanisms," Experimental Economics, Springer;Economic Science Association, vol. 25(5), pages 1467-1491, November.
    13. Chen, Yan & Kesten, Onur, 2019. "Chinese college admissions and school choice reforms: An experimental study," Games and Economic Behavior, Elsevier, vol. 115(C), pages 83-100.
    14. March, Christoph, 2021. "Strategic interactions between humans and artificial intelligence: Lessons from experiments with computer players," Journal of Economic Psychology, Elsevier, vol. 87(C).
    15. Umut Dur & Robert G. Hammond & Thayer Morrill, 2019. "The Secure Boston Mechanism: theory and experiments," Experimental Economics, Springer;Economic Science Association, vol. 22(4), pages 918-953, December.
    16. Wu, Binzhen & Zhong, Xiaohan, 2020. "Matching inequality and strategic behavior under the Boston mechanism: Evidence from China's college admissions," Games and Economic Behavior, Elsevier, vol. 123(C), pages 1-21.
    17. Cho, Wonki Jo & Hafalir, Isa E. & Lim, Wooyoung, 2022. "Tie-breaking and efficiency in the laboratory school choice," Journal of Economic Theory, Elsevier, vol. 205(C).

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    More about this item

    Keywords

    Matching; School choice; Experiment; Scale;
    All these keywords.

    JEL classification:

    • C78 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Bargaining Theory; Matching Theory
    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
    • D47 - Microeconomics - - Market Structure, Pricing, and Design - - - Market Design
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design

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