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Optimal crowdsourcing contests

Author

Listed:
  • Chawla, Shuchi
  • Hartline, Jason D.
  • Sivan, Balasubramanian

Abstract

We study the design and approximation of optimal crowdsourcing contests. Crowdsourcing contests can be modeled as all-pay auctions because entrants must exert effort up-front to enter. Unlike all-pay auctions where a usual design objective would be to maximize revenue, in crowdsourcing contests, the principal only benefits from the submission with the highest quality. We give a theory for optimal crowdsourcing contests that mirrors the theory of optimal auction design: the optimal crowdsourcing contest is a virtual valuation optimizer (the virtual valuation function depends on the distribution of contestant skills and the number of contestants). We also compare crowdsourcing contests with more conventional means of procurement. In this comparison, crowdsourcing contests are relatively disadvantaged because the effort of losing contestants is wasted. We show that the total wasted effort is at most the maximum effort which implies that crowdsourcing contests are a 2-approximation to an idealized model of conventional procurement.

Suggested Citation

  • Chawla, Shuchi & Hartline, Jason D. & Sivan, Balasubramanian, 2019. "Optimal crowdsourcing contests," Games and Economic Behavior, Elsevier, vol. 113(C), pages 80-96.
    • Handle: RePEc:eee:gamebe:v:113:y:2019:i:c:p:80-96
    DOI: 10.1016/j.geb.2015.09.001
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    References listed on IDEAS

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    1. Benny Moldovanu & Aner Sela, 2001. "The Optimal Allocation of Prizes in Contests," American Economic Review, American Economic Association, vol. 91(3), pages 542-558, June.
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    5. Moldovanu, Benny & Sela, Aner, 2006. "Contest architecture," Journal of Economic Theory, Elsevier, vol. 126(1), pages 70-96, January.
    6. Shuchi Chawla & Jason Hartline & David Malec & Balasubramanian Sivan, 2010. "Sequential Posted Pricing and Multi-parameter Mechanism Design," Discussion Papers 1486, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
    7. Yeon-Koo Che & Ian Gale, 2003. "Optimal Design of Research Contests," American Economic Review, American Economic Association, vol. 93(3), pages 646-671, June.
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    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Konstantinos I. Stouras & Jeremy Hutchison-Krupat & Raul O. Chao, 2022. "The Role of Participation in Innovation Contests," Management Science, INFORMS, vol. 68(6), pages 4135-4150, June.
    2. Hyeon Jo & Youngsok Bang, 2023. "Factors influencing continuance intention of participants in crowdsourcing," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-13, December.
    3. Jason Milionis & Ciamac C. Moallemi & Tim Roughgarden, 2023. "A Myersonian Framework for Optimal Liquidity Provision in Automated Market Makers," Papers 2303.00208, arXiv.org, revised Nov 2023.
    4. Qi Shi & Dong Hao, 2021. "Social Sourcing: Incorporating Social Networks Into Crowdsourcing Contest Design," Papers 2112.02884, arXiv.org, revised Nov 2022.
    5. Fupeng Sun & Yanwei Sun & Chiwei Yan & Li Jin, 2022. "Restricting Entries to All-Pay Contests," Papers 2205.08104, arXiv.org, revised Mar 2024.
    6. Zhuoqiong Chen, 2021. "All-pay auctions with private signals about opponents’ values," Review of Economic Design, Springer;Society for Economic Design, vol. 25(1), pages 33-64, June.
    7. Edith Elkind & Abheek Ghosh & Paul Goldberg, 2021. "Contest Design with Threshold Objectives," Papers 2109.03179, arXiv.org, revised Dec 2021.
    8. Xiaotie Deng & Yotam Gafni & Ron Lavi & Tao Lin & Hongyi Ling, 2021. "From Monopoly to Competition: Optimal Contests Prevail," Papers 2107.13363, arXiv.org.
    9. Yingkai Li & Xiaoyun Qiu, 2023. "Screening Signal-Manipulating Agents via Contests," Papers 2302.09168, arXiv.org, revised Feb 2024.
    10. Liu, Bin & Lu, Jingfeng, 2023. "Optimal orchestration of rewards and punishments in rank-order contests," Journal of Economic Theory, Elsevier, vol. 208(C).
    11. 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.
    12. Carmen Beviá & Luis Corchón, 2022. "Contests with dominant strategies," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 74(4), pages 1-19, November.
    13. Liu, Bin & Lu, Jingfeng, 2019. "The optimal allocation of prizes in contests with costly entry," International Journal of Industrial Organization, Elsevier, vol. 66(C), pages 137-161.
    14. Wasser, Cédric & Zhang, Mengxi, 2023. "Differential treatment and the winner's effort in contests with incomplete information," Games and Economic Behavior, Elsevier, vol. 138(C), pages 90-111.
    15. Bo Chen & Emilios Galariotis & Lijun Ma & Zijia Wang & Zhaobo Zhu, 2023. "On disclosure of participation in innovation contests: a dominance result," Annals of Operations Research, Springer, vol. 328(2), pages 1615-1629, September.

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    More about this item

    Keywords

    Crowdsourcing contest; All-pay auction; Bayes–Nash equilibrium; Approximation;
    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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