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Toward Zero-Determinant Strategies for Optimal Decision Making in Crowdsourcing Systems

Author

Listed:
  • Jiali Wang

    (College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua 321004, China
    The Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China)

  • Changbing Tang

    (College of Physics and Electronics Information Engineering, Zhejiang Normal University, Jinhua 321004, China)

  • Jianquan Lu

    (School of Mathematics, Southeast University, Nanjing 210096, China)

  • Guanrong Chen

    (Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR, China)

Abstract

The crowdsourcing system is an internet-based distributed problem-solving and production organization model, which has been applied in human–computer interaction, databases, natural language processing, machine learning and other fields. It guides the public to complete some tasks through specific strategies and methods. However, rational and selfish workers in crowdsourcing systems will submit solutions of different qualities in order to maximize their own benefits. Therefore, how to choose optimal strategies for selfish workers to maximize their benefits is important and crucial in such a scenario. In this paper, we propose a decision optimization method with incomplete information in a crowdsourcing system based on zero-determinant (ZD) strategies to help workers make optimal decisions. We first formulate the crowdsourcing problem, where workers have “winner-takes-all” rules as an iterated game with incomplete information. Subsequently, we analyze the optimal decision of workers in crowdsourcing systems in terms of ZD strategies, for which we find conditions to reach the maximum payoff of a focused worker. In addition, the analysis helps understand what solutions selfish workers will submit under the condition of having incomplete information. Finally, numerical simulations illustrate the performances of different strategies and the effects of the parameters on the payoffs of the focused worker.

Suggested Citation

  • Jiali Wang & Changbing Tang & Jianquan Lu & Guanrong Chen, 2023. "Toward Zero-Determinant Strategies for Optimal Decision Making in Crowdsourcing Systems," Mathematics, MDPI, vol. 11(5), pages 1-21, February.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:5:p:1153-:d:1080937
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    References listed on IDEAS

    as
    1. Taha, Mohammad A. & Ghoneim, Ayman, 2020. "Zero-determinant strategies in repeated asymmetric games," Applied Mathematics and Computation, Elsevier, vol. 369(C).
    2. Jonathan Dortheimer, 2022. "Collective Intelligence in Design Crowdsourcing," Mathematics, MDPI, vol. 10(4), pages 1-24, February.
    Full references (including those not matched with items on IDEAS)

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