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Performance-maximizing large contests

Author

Listed:
  • Olszewski, Wojciech

    (Department of Economics, Northwestern University)

  • Siegel, Ron

    (Department of Economics, The Pennsylvania State University)

Abstract

Many sales, sports, and research contests are put in place to maximize contestants’ performance. We investigate and provide a complete characterization of the prize structures that achieve this objective in settings with many contestants. The contestants may be ex-ante asymmetric in their abilities and prize valuations, and there may be complete or incomplete information about these parameters. The prize valuations and performance costs may be linear, concave, or convex. A main novel takeaway is that awarding numerous different prizes whose values gradually decline with contestants’ ranking is optimal in the typical case of contestants with convex performance costs and concave prize valuations. This suggests that many real-world contests can be improved by increasing the number of prizes and making them more heterogeneous. The techniques we develop can also be used to formulate and solve other contest design questions that have so far proven intractable.

Suggested Citation

  • Olszewski, Wojciech & Siegel, Ron, 2020. "Performance-maximizing large contests," Theoretical Economics, Econometric Society, vol. 15(1), January.
    • Handle: RePEc:the:publsh:3588
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    References listed on IDEAS

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    Cited by:

    1. Yingkai Li & Xiaoyun Qiu, 2023. "Screening Signal-Manipulating Agents via Contests," Papers 2302.09168, arXiv.org, revised Feb 2024.
    2. Fu, Qiang & Wang, Xiruo & Wu, Zenan, 2021. "Multi-prize contests with risk-averse players," Games and Economic Behavior, Elsevier, vol. 129(C), pages 513-535.
    3. Serhat Doğan & Emin Karagözoğlu & Kerim Keskin & Çağrı Sağlam, 2023. "Large Tullock contests," Journal of Economics, Springer, vol. 140(2), pages 169-179, October.
    4. Liu, Bin & Lu, Jingfeng, 2023. "Optimal orchestration of rewards and punishments in rank-order contests," Journal of Economic Theory, Elsevier, vol. 208(C).
    5. Barbieri, Stefano & Serena, Marco, 2021. "Winner’s effort maximization in large contests," Journal of Mathematical Economics, Elsevier, vol. 96(C).
    6. Mengxi Zhang, 2023. "Optimal Contests with Incomplete Information and Convex Effort Costs," CRC TR 224 Discussion Paper Series crctr224_2023_156v2, University of Bonn and University of Mannheim, Germany.
    7. Sumit Goel, 2022. "Optimal grading contests," Papers 2205.05207, arXiv.org, revised Sep 2023.
    8. Bas J. Dietzenbacher & Aleksei Y. Kondratev, 2023. "Fair and Consistent Prize Allocation in Competitions," Management Science, INFORMS, vol. 69(6), pages 3319-3339, June.
    9. Yaron Azrieli & Christopher P. Chambers, 2024. "Success functions in large contests," Papers 2403.07152, arXiv.org.
    10. Qi Shi & Dong Hao, 2021. "Social Sourcing: Incorporating Social Networks Into Crowdsourcing Contest Design," Papers 2112.02884, arXiv.org, revised Nov 2022.
    11. Morgan, John & Tumlinson, Justin & Várdy, Felix, 2022. "The limits of meritocracy," Journal of Economic Theory, Elsevier, vol. 201(C).
    12. Edith Elkind & Abheek Ghosh & Paul Goldberg, 2022. "Contests to Incentivize a Target Group," Papers 2204.14051, arXiv.org.
    13. Zhu, Feng, 2021. "On optimal favoritism in all-pay contests," Journal of Mathematical Economics, Elsevier, vol. 95(C).
    14. Tomohiko Kawamori, 2020. "Extractive contest design," Papers 2006.01808, arXiv.org, revised Jul 2022.

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    More about this item

    Keywords

    Large contests; contest design; optimal contests; mechanism design;
    All these keywords.

    JEL classification:

    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
    • 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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