报告地点：腾讯会议 ID: 882 4461 9458
Guangzhi Shang is Jim Moran Associate Professor of Operations Management in the Department of Business Analytics, Information Systems, and Supply Chain at Florida State University. His research has been published in Production and Operations Management (POM), Journal of Operations Management (JOM), and Decision Sciences (DS), among others, and recognized by best paper awards at POM, JOM, and POM Society’s College of Operational Excellence. He serves as the co-Department-Editor for the Empirical Research Methods Department at JOM and for the Retail Operations Department at DS. His review service is recognized by the 2019 outstanding reviewer award of DS and the 2018 best reviewer award of the Journal of Operations Management. He was also nominated for the best reviewer for POM and best associate editor for JOM. He co-produces a column together with Mike Galbreth and Mark Ferguson in the Reverse Logistics Magazine named “View from Academia,” aimed at disseminating fresh-off-the-press academic knowledge among industry professionals dealing with consumer returns.
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.