Token-weighted crowdsourcing
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Published version
Date
2020-09
Authors
Tsoukalas, Gerry
Falk, Brett Hemenway
Version
Published version
OA Version
Citation
G. Tsoukalas, B.H. Falk. 2020. "Token-Weighted Crowdsourcing." Management Science, Volume 66, Issue 9, pp. 3843 - 3859. https://doi.org/10.1287/mnsc.2019.3515
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. This paper was accepted by Gabriel Weintraub, revenue management and market analytics.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. You are free to download this work and share with others, but cannot change in any way or use commercially without permission, and you must attribute this work as “Management Science. Copyright © 2020 The Author(s). https://doi.org/10.1287/mnsc.2019.3515, used under a Creative Commons Attribution License: https://creativecommons.org/licenses/by-nc-nd/4.0/