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Robust and High-Accessibility Ranking Method for Crowdsourcing-Based Decision Making

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  • Phan-Anh-Huy Nguyen

    (HCMC University of Technology and Education)

  • Ping-Yu Hsu

    (National Central University)

Abstract

With the advancement of online technologies in recent years, crowdsourcing data has been used for numerous applications in many fields. The preference sequences obtained through crowdsourcing are valuable resources for ranking. However, the aggregation of incomplete and inconsistent preferences is complicated. To address these challenges, this study proposed a novel method termed robust crowd ranking (RCR) based on a consistent fuzzy c-means approach to increase the robustness and accessibility of aggregated preference sequences obtained through crowdsourcing. To verify the robustness, accessibility, and accuracy of RCR, comprehensive experiments were conducted using synthetic and real data. The simulation results validated that the RCR outperforms Borda Count, Dodgson, IRV and Tideman methods.

Suggested Citation

  • Phan-Anh-Huy Nguyen & Ping-Yu Hsu, 2023. "Robust and High-Accessibility Ranking Method for Crowdsourcing-Based Decision Making," Group Decision and Negotiation, Springer, vol. 32(5), pages 1211-1236, October.
    • Handle: RePEc:spr:grdene:v:32:y:2023:i:5:d:10.1007_s10726-023-09840-2
    DOI: 10.1007/s10726-023-09840-2
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    References listed on IDEAS

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    6. Baback Vaziri & Shaunak Dabadghao & Yuehwern Yih & Thomas L. Morin & Mark Lehto, 2020. "Crowd-Ranking: a Markov-based method for ranking alternatives," Operational Research, Springer, vol. 20(1), pages 279-295, March.
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