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CROWDMATCH: Optimizing Crowdsourcing Matching through the Integration of Matching Theory and Coalition Games

Author

Listed:
  • Adedamola Adesokan

    (Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131-0001, USA)

  • Rowan Kinney

    (Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131-0001, USA)

  • Eirini Eleni Tsiropoulou

    (Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131-0001, USA)

Abstract

This paper tackles the challenges inherent in crowdsourcing dynamics by introducing the CROWDMATCH mechanism. Aimed at enabling crowdworkers to strategically select suitable crowdsourcers while contributing information to crowdsourcing tasks, CROWDMATCH considers incentives, information availability and cost, and the decisions of fellow crowdworkers to model the utility functions for both the crowdworkers and the crowdsourcers. Specifically, the paper presents an initial Approximate CROWDMATCH mechanism grounded in matching theory principles, eliminating externalities from crowdworkers’ decisions and enabling each entity to maximize its utility. Subsequently, the Accurate CROWDMATCH mechanism is introduced, which is initiated by the outcome of the Approximate CROWDMATCH mechanism, and coalition game-theoretic principles are employed to refine the matching process by accounting for externalities. The paper’s contributions include the introduction of the CROWDMATCH system model, the development of both Approximate and Accurate CROWDMATCH mechanisms, and a demonstration of their superior performance through comprehensive simulation results. The mechanisms’ scalability in large-scale crowdsourcing systems and operational advantages are highlighted, distinguishing them from existing methods and highlighting their efficacy in empowering crowdworkers in crowdsourcer selection.

Suggested Citation

  • Adedamola Adesokan & Rowan Kinney & Eirini Eleni Tsiropoulou, 2024. "CROWDMATCH: Optimizing Crowdsourcing Matching through the Integration of Matching Theory and Coalition Games," Future Internet, MDPI, vol. 16(2), pages 1-16, February.
    • Handle: RePEc:gam:jftint:v:16:y:2024:i:2:p:58-:d:1337507
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    References listed on IDEAS

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    1. Jingsha He & Yue Li & Nafei Zhu, 2023. "A Game Theory-Based Model for the Dissemination of Privacy Information in Online Social Networks," Future Internet, MDPI, vol. 15(3), pages 1-17, February.
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    Cited by:

    1. Adedamola Adesokan & Aisha B Rahman & Eirini Eleni Tsiropoulou, 2024. "INFLUTRUST : Trust-Based Influencer Marketing Campaigns in Online Social Networks," Future Internet, MDPI, vol. 16(7), pages 1-16, June.

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