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Optimal cardinal contests

Author

Listed:
  • Goutham Takasi
  • Milind Dawande
  • Ganesh Janakiraman

Abstract

We study the design of crowdsourcing contests in settings where the outputs of the contestants are quantifiable, for example, a data science challenge. This setting is in contrast to those where the output is only qualitative and cannot be objectively quantified, for example, when the goal of the contest is to design a logo. The literature on crowdsourcing contests focuses largely on ordinal contests, where contestants' outputs are ranked by the designer and awards are based on relative ranks. Such contests are ideally suited for the latter setting, where output is qualitative. For our setting (quantitative output), it is possible to design cardinal contests, where awards could be based on the actual outputs and not on their ranking alone—thus, the family of cardinal contests includes the family of ordinal contests. We study the problem of designing an optimal cardinal contest. We use mechanism design theory to derive an optimal cardinal mechanism and provide a convenient implementation—a decreasing reward‐meter mechanism—of the optimal contest. We establish the practicality of our mechanism by showing that it is “Obviously Strategy‐Proof,” a recently introduced formal notion of simplicity in the literature. We also compare the optimal cardinal contest with the most popular ordinal contest—namely, the Winner‐Takes‐All (WTA) contest, along several metrics. In particular, the optimal cardinal mechanism delivers a superior expected best output, whereas the WTA contest yields a greater expected contestant welfare. Furthermore, under a sufficiently large budget, the contest designer's expected net‐benefit is higher under the optimal cardinal mechanism than that under the WTA contest, regardless of the number of contestants in the two mechanisms. Our numerical analysis suggests that, for the contest designer, the average improvement provided by the optimal cardinal mechanism over the WTA contest is about 23%. For a given number of contestants, the benefit of the optimal cardinal mechanism is especially appreciable for projects where the ratio of the designer's utility to agents' cost‐of‐effort falls within a wide practical range. For projects where this ratio is very high, the expected profit of the best WTA contest is reasonably close to that of the optimal cardinal mechanism.

Suggested Citation

  • Goutham Takasi & Milind Dawande & Ganesh Janakiraman, 2023. "Optimal cardinal contests," Production and Operations Management, Production and Operations Management Society, vol. 32(11), pages 3433-3451, November.
    • Handle: RePEc:bla:popmgt:v:32:y:2023:i:11:p:3433-3451
    DOI: 10.1111/poms.14043
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    References listed on IDEAS

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    1. Benny Moldovanu & Aner Sela, 2008. "The Optimal Allocation of Prizes in Contests," Springer Books, in: Roger D. Congleton & Arye L. Hillman & Kai A. Konrad (ed.), 40 Years of Research on Rent Seeking 1, pages 615-631, Springer.
    2. J. Michael Harrison & Nur Sunar, 2015. "Investment Timing with Incomplete Information and Multiple Means of Learning," Operations Research, INFORMS, vol. 63(2), pages 442-457, April.
    3. George Georgiadis, 2015. "Projects and Team Dynamics," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(1), pages 187-218.
    4. Laurence Ales & Soo-Haeng Cho & Ersin Körpeoğlu, 2017. "Optimal Award Scheme in Innovation Tournaments," Operations Research, INFORMS, vol. 65(3), pages 693-702, June.
    5. Kimmy Wa Chan & Stella Yiyan Li & Jian Ni & John JianJun Zhu, 2021. "What Feedback Matters? The Role of Experience in Motivating Crowdsourcing Innovation," Production and Operations Management, Production and Operations Management Society, vol. 30(1), pages 103-126, January.
    6. Marina Halac & Navin Kartik & Qingmin Liu, 2017. "Contests for Experimentation," Journal of Political Economy, University of Chicago Press, vol. 125(5), pages 1523-1569.
    7. Jürgen Mihm & Jochen Schlapp, 2019. "Sourcing Innovation: On Feedback in Contests," Management Science, INFORMS, vol. 65(2), pages 559-576, February.
    8. Lakshminarayana Nittala & Sanjiv Erat & Vish Krishnan, 2022. "Designing internal innovation contests," Production and Operations Management, Production and Operations Management Society, vol. 31(5), pages 1963-1976, May.
    9. Shengwu Li, 2017. "Obviously Strategy-Proof Mechanisms," American Economic Review, American Economic Association, vol. 107(11), pages 3257-3287, November.
    10. Ersin Körpeoğlu & Soo-Haeng Cho, 2018. "Incentives in Contests with Heterogeneous Solvers," Management Science, INFORMS, vol. 64(6), pages 2709-2715, June.
    11. Christian Terwiesch & Yi Xu, 2008. "Innovation Contests, Open Innovation, and Multiagent Problem Solving," Management Science, INFORMS, vol. 54(9), pages 1529-1543, September.
    12. Cuihong Li & Zhixi Wan, 2017. "Supplier Competition and Cost Improvement," Management Science, INFORMS, vol. 63(8), pages 2460-2477, August.
    13. Myerson, Roger B., 1982. "Optimal coordination mechanisms in generalized principal-agent problems," Journal of Mathematical Economics, Elsevier, vol. 10(1), pages 67-81, June.
    14. Roger B. Myerson, 1981. "Optimal Auction Design," Mathematics of Operations Research, INFORMS, vol. 6(1), pages 58-73, February.
    15. Richard L. Fullerton & R. Preston McAfee, 1999. "Auctioning Entry into Tournaments," Journal of Political Economy, University of Chicago Press, vol. 107(3), pages 573-605, June.
    16. Murali Agastya & Oleksii Birulin, 2023. "Optimal Task Scheduling under Adverse Selection and Hidden Actions," American Economic Journal: Microeconomics, American Economic Association, vol. 15(2), pages 660-698, May.
    17. Taylor, Curtis R, 1995. "Digging for Golden Carrots: An Analysis of Research Tournaments," American Economic Review, American Economic Association, vol. 85(4), pages 872-890, September.
    18. Shivam Gupta & Wei Chen & Milind Dawande & Ganesh Janakiraman, 2015. "Optimal Descending Mechanisms for Constrained Procurement," Production and Operations Management, Production and Operations Management Society, vol. 24(12), pages 1955-1965, December.
    19. Chawla, Shuchi & Hartline, Jason D. & Sivan, Balasubramanian, 2019. "Optimal crowdsourcing contests," Games and Economic Behavior, Elsevier, vol. 113(C), pages 80-96.
    20. Paul Milgrom & Ilya Segal, 2020. "Clock Auctions and Radio Spectrum Reallocation," Journal of Political Economy, University of Chicago Press, vol. 128(1), pages 1-31.
    21. Nur Sunar & John R. Birge & Sinit Vitavasiri, 2019. "Optimal Dynamic Product Development and Launch for a Network of Customers," Operations Research, INFORMS, vol. 67(3), pages 770-790, May.
    22. Kostas Bimpikis & Shayan Ehsani & Mohamed Mostagir, 2019. "Designing Dynamic Contests," Operations Research, INFORMS, vol. 67(2), pages 339-356, March.
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