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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Wisdom of crowds: much ado about nothing

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Author(s):
Reia, Sandro M. [1] ; Fontanari, Jose F. [1]
Total Authors: 2
Affiliation:
[1] Univ Sao Paulo, Inst Fis Sao Carlos, Caixa Postal 369, BR-13560970 Sao Carlos, SP - Brazil
Total Affiliations: 1
Document type: Journal article
Source: JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT; v. 2021, n. 5 MAY 2021.
Web of Science Citations: 0
Abstract

The puzzling idea that the combination of independent estimates of the magnitude of a quantity results in a very accurate prediction, which is superior to any or, at least, to most of the individual estimates is known as the wisdom of crowds. Here we use the federal reserve bank of Philadelphia's survey of professional forecasters database to confront the statistical and psychophysical explanations of this phenomenon. Overall we find that the data do not support any of the proposed explanations of the wisdom of crowds. In particular, we find a positive correlation between the variance (or diversity) of the estimates and the crowd error in disagreement with some interpretations of the diversity prediction theorem. In addition, in opposition to the predictions of the psychophysical augmented quincunx model, we find that the skew of the estimates offers no information about the crowd error. More importantly, we find that the crowd beats all individuals in less than 2% of the forecasts and beats most individuals in less than 70% of the forecasts, which means that there is fair chance that an individual selected at random will perform better than the crowd. These results contrast starkly with the performance of non-natural crowds composed of unbiased forecasters which beat most individuals in practically all forecasts. The moderate statistical advantage of a real-world crowd over its members does not justify the ado about its wisdom, which is most likely a product of the selective attention fallacy. (AU)

FAPESP's process: 20/03041-3 - Mathematical and computational modeling in Social Sciences
Grantee:José Fernando Fontanari
Support Opportunities: Regular Research Grants