Wisdom of crowds: much ado about nothing
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 nonnatural crowds composed of unbiased forecasters which beat most individuals in practically all forecasts. The moderate statistical advantage of a realworld crowd over its members does not justify the ado about its wisdom, which is most likely a product of the selective attention fallacy.
 Publication:

Journal of Statistical Mechanics: Theory and Experiment
 Pub Date:
 May 2021
 DOI:
 10.1088/17425468/abfa1f
 arXiv:
 arXiv:2008.01485
 Bibcode:
 2021JSMTE2021e3402R
 Keywords:

 inference in socioeconomic system;
 data mining;
 agentbased models;
 Statistics  Applications;
 Computer Science  Multiagent Systems;
 Physics  Data Analysis;
 Statistics and Probability
 EPrint:
 doi:10.1088/17425468/abfa1f