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Token-Weighted Crowdsourcing

Author

Listed:
  • Gerry Tsoukalas

    (The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

  • Brett Hemenway Falk

    (Department of Computer and Information Sciences, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

Abstract

Blockchain-based platforms often rely on token-weighted voting (“ τ -weighting”) to efficiently crowdsource information from their users for a wide range of applications, including content curation and on-chain governance. We examine the effectiveness of such decentralized platforms for harnessing the wisdom and effort of the crowd. We find that τ -weighting generally discourages truthful voting and erodes the platform’s predictive power unless users are “strategic enough” to unravel the underlying aggregation mechanism. Platform accuracy decreases with the number of truthful users and the dispersion in their token holdings, and in many cases, platforms would be better off with a “flat” 1/ n mechanism. When, prior to voting, strategic users can exert effort to endogenously improve their signals, users with more tokens generally exert more effort—a feature often touted in marketing materials as a core advantage of τ -weighting—however, this feature is not attributable to the mechanism itself, and more importantly, the ensuing equilibrium fails to achieve the first-best accuracy of a centralized platform. The optimality gap decreases as the distribution of tokens across users approaches a theoretical optimum, which we derive, but tends to increase with the dispersion in users’ token holdings.

Suggested Citation

  • Gerry Tsoukalas & Brett Hemenway Falk, 2020. "Token-Weighted Crowdsourcing," Management Science, INFORMS, vol. 66(9), pages 3843-3859, September.
  • Handle: RePEc:inm:ormnsc:v:66:y:2020:i:9:p:3843-3859
    DOI: 10.287/mnsc.2019.3515
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    Cited by:

    1. Lingxiu Dong & Puping (Phil) Jiang & Fasheng Xu, 2023. "Impact of Traceability Technology Adoption in Food Supply Chain Networks," Management Science, INFORMS, vol. 69(3), pages 1518-1535, March.
    2. Lin William Cong & Yizhou Xiao, 2021. "Categories and Functions of Crypto-Tokens," Springer Books, in: Maurizio Pompella & Roman Matousek (ed.), The Palgrave Handbook of FinTech and Blockchain, edition 1, chapter 0, pages 267-284, Springer.
    3. Wenpin Tang, 2022. "Stability of shares in the Proof of Stake Protocol -- Concentration and Phase Transitions," Papers 2206.02227, arXiv.org.
    4. Jiri Chod & Nikolaos Trichakis & Gerry Tsoukalas & Henry Aspegren & Mark Weber, 2020. "On the Financing Benefits of Supply Chain Transparency and Blockchain Adoption," Management Science, INFORMS, vol. 66(10), pages 4378-4396, October.
    5. Elena Belavina & Simone Marinesi & Gerry Tsoukalas, 2020. "Rethinking Crowdfunding Platform Design: Mechanisms to Deter Misconduct and Improve Efficiency," Management Science, INFORMS, vol. 66(11), pages 4980-4997, November.
    6. Choi, Tsan-Ming & Ouyang, Xu, 2021. "Initial coin offerings for blockchain based product provenance authentication platforms," International Journal of Production Economics, Elsevier, vol. 233(C).
    7. Alon Benhaim & Brett Hemenway Falk & Gerry Tsoukalas, 2021. "Scaling Blockchains: Can Committee-Based Consensus Help?," Papers 2110.08673, arXiv.org, revised Dec 2022.
    8. Jingxing (Rowena) Gan & Gerry Tsoukalas & Serguei Netessine, 2021. "Initial Coin Offerings, Speculation, and Asset Tokenization," Management Science, INFORMS, vol. 67(2), pages 914-931, February.

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