IDEAS home Printed from https://ideas.repec.org/p/dpr/wpaper/1266.html
   My bibliography  Save this paper

On the Performance of the Crémer–McLean Auction: An Experiment

Author

Listed:
  • Takeshi Nishimura
  • Nobuyuki Hanaki

Abstract

The paradoxical full-surplus-extraction (FE) result, which can impair the mechanism design paradigm, is a long-standing concern in the literature. We tackle this problem by experimentally testing the performance of an FE auction, which is a second-price (2P) auction with lotteries. In the FE treatment, overbid amounts given entry increased and entry rates decreased through rounds, thus FE failed. By contrast, most subjects learned value bidding in the 2P treatment. To identify the causes of failure in the FE, we take an evolutionary-game approach. The FE auction with risk-neutral bidders has exactly two symmetric equilibria, either value bidding with full or partial entry, and only the partial-entry equilibrium is (evolutionarily or asymptotically) stable. Replicator dynamics with vanishing trends well explain observed dynamic bidding patterns. Together, these findings suggest that the FE outcome is not robust to trial-and-error learning by bidders.

Suggested Citation

  • Takeshi Nishimura & Nobuyuki Hanaki, 2024. "On the Performance of the Crémer–McLean Auction: An Experiment," ISER Discussion Paper 1266, Institute of Social and Economic Research, Osaka University.
  • Handle: RePEc:dpr:wpaper:1266
    as

    Download full text from publisher

    File URL: https://www.iser.osaka-u.ac.jp/library/dp/2024/DP1266.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Borgers, Tilman & Sarin, Rajiv, 1997. "Learning Through Reinforcement and Replicator Dynamics," Journal of Economic Theory, Elsevier, vol. 77(1), pages 1-14, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ponti, Giovanni, 2000. "Cycles of Learning in the Centipede Game," Games and Economic Behavior, Elsevier, vol. 30(1), pages 115-141, January.
    2. Philippe Jehiel & Aviman Satpathy, 2024. "Learning to be Indifferent in Complex Decisions: A Coarse Payoff-Assessment Model," Papers 2412.09321, arXiv.org, revised Dec 2024.
    3. Antonio Cabrales & Rosemarie Nagel & Roc Armenter, 2007. "Equilibrium selection through incomplete information in coordination games: an experimental study," Experimental Economics, Springer;Economic Science Association, vol. 10(3), pages 221-234, September.
    4. Dehai Liu & Hongyi Li & Weiguo Wang & Chuang Zhou, 2015. "Scenario forecast model of long term trends in rural labor transfer based on evolutionary games," Journal of Evolutionary Economics, Springer, vol. 25(3), pages 649-670, July.
    5. Ianni, A., 2002. "Reinforcement learning and the power law of practice: some analytical results," Discussion Paper Series In Economics And Econometrics 203, Economics Division, School of Social Sciences, University of Southampton.
    6. Hopkins, Ed, 1999. "Learning, Matching, and Aggregation," Games and Economic Behavior, Elsevier, vol. 26(1), pages 79-110, January.
    7. Tsakas, Elias & Voorneveld, Mark, 2009. "The target projection dynamic," Games and Economic Behavior, Elsevier, vol. 67(2), pages 708-719, November.
    8. Antonio Morales, 2005. "On the Role of the Group Composition for Achieving Optimality," Annals of Operations Research, Springer, vol. 137(1), pages 387-397, July.
    9. DeJong, D.V. & Blume, A. & Neumann, G., 1998. "Learning in Sender-Receiver Games," Other publications TiSEM 4a8b4f46-f30b-4ad2-bb0c-1, Tilburg University, School of Economics and Management.
    10. Gaunersdorfer, A. & Hommes, C.H. & Wagener, F.O.O., 2000. "Bifurcation Routes to Volatility Clustering," CeNDEF Working Papers 00-04, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    11. Norman, Thomas W.L., 2009. "Rapid evolution under inertia," Games and Economic Behavior, Elsevier, vol. 66(2), pages 865-879, July.
    12. Fernando Lozano & Jaime Lozano & Mario García, 2007. "An artificial economy based on reinforcement learning and agent based modeling," Documentos de Trabajo 3907, Universidad del Rosario.
    13. Karine Nyborg & Mari Rege, 2000. "The Evolution of Considerate Smoking Behavior," Discussion Papers 279, Statistics Norway, Research Department.
    14. Carpenter, Jeffrey P., 2007. "Punishing free-riders: How group size affects mutual monitoring and the provision of public goods," Games and Economic Behavior, Elsevier, vol. 60(1), pages 31-51, July.
    15. Antonio Cabrales & Giovanni Ponti, 2000. "Implementation, Elimination of Weakly Dominated Strategies and Evolutionary Dynamics," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 3(2), pages 247-282, April.
    16. Jean-François Laslier & Bernard Walliser, 2015. "Stubborn learning," Theory and Decision, Springer, vol. 79(1), pages 51-93, July.
    17. Demichelis, Stefano & Ritzberger, Klaus, 2003. "From evolutionary to strategic stability," Journal of Economic Theory, Elsevier, vol. 113(1), pages 51-75, November.
    18. Herbert Gintis & Antoine Mandel, 2012. "The Stability of Walrasian General Equilibrium," Documents de travail du Centre d'Economie de la Sorbonne 12065r, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, revised Apr 2013.
    19. Mari Rege, 2000. "Networking Strategy: Cooperate Today in Order to Meet a Cooperator Tomorrow," Discussion Papers 282, Statistics Norway, Research Department.
    20. Mohlin, Erik & Östling, Robert & Wang, Joseph Tao-yi, 2020. "Learning by similarity-weighted imitation in winner-takes-all games," Games and Economic Behavior, Elsevier, vol. 120(C), pages 225-245.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:dpr:wpaper:1266. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Librarian (email available below). General contact details of provider: https://edirc.repec.org/data/isosujp.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.