Abstract
The interest in the wisdom of crowds stems mainly from the possibility of combining independent forecasts from experts in the hope that many expert minds are better than a few. Hence the relevant subject of study nowadays is the Vox Expertorum rather than Galton’s original Vox Populi. Here we use the Federal Reserve Bank of Philadelphia’s Survey of Professional Forecasters to analyze 15455 forecasting contests to predict a variety of economic indicators. We find that the median has advantages over the mean as a method to combine the experts’ estimates: the odds that the crowd beats all participants of a forecasting contest is 0.015 when the aggregation is given by the mean and 0.026 when it is given by the median. Both aggregation methods yield a 20% error on the average, which must be contrasted with the expected error of a randomly selected forecaster, which is about 22%. We conclude that selective attention is the most likely explanation for the mysterious high accuracy of the crowd reported in the literature. This conclusion is strengthened by the rebuff of the claim that this high accuracy results from the cancellation of the participants’ errors: we find no meaningful correlation between the asymmetry of the distribution of the individuals’ estimates and the collective error.
Graphical abstract
Similar content being viewed by others
Data Availibility Statement
This manuscript has associated data in a data repository. [Authors’ comment: The economic forecasting data considered in the manuscript are from the Federal Reserve Bank of Philadelphia’s Survey of Professional Forecasters and are available at https://www.philadelphiafed.org/research-and-data/real-time-center/survey-of-professional-forecasters.]
References
F. Galton, Nature 75, 450 (1907)
K.F. Wallis, Stat. Sci. 29, 420 (2014)
J. Surowiecki, The Wisdom of Crowds: Why the Many Are Smarter than the Few and How Collective Wisdom Shapes Business, Economies, Societies, and Nations (Doubleday, New York, 2004)
C. Sunstein, Infotopia: How many minds produce knowledge (Oxford University Press, Oxford, 2006)
S.E. Page, The difference: How the power of diversity creates better groups, firms, schools, and societies (Princeton University Press, Princeton, 2007)
J. Lorenz, H. Rauhut, F. Schweitzer, D. Helbing, Proc. Natl. Acad. Sci. USA 108, 9020 (2011)
J.M. Bates, C.W.J. Granger, Oper. Res. Q. 20, 451 (1969)
U.W. Nash, PLoS ONE 9, e112386 (2014)
U.W. Nash, J. Math. Psychol. 77, 165 (2017)
S.M. Reia, J.F. Fontanari, J. Stat. Mech. 2021, 053402 (2021)
D.A. Nobre, J.F. Fontanari, Complex. Syst. 29, 861 (2020)
Federal Reserve Bank of Philadelphia. https://www.philadelphiafed.org/research-and-data/real-time-center/survey-of-professional-forecasters, (2022)
M. P. Clements, R. W. Rich, J. S. Tracy, Surveys of Professionals. Working Paper No. 22-13. Federal Reserve Bank of Cleveland, (2022). https://doi.org/10.26509/frbc-wp-2022-13
A.J. King, L. Cheng, S.D. Starke, J.P. Myatt, Biol. Lett. 8, 197 (2011)
F.H. Perry-Coste, Nature 75, 509 (1907)
P.-N. Tan, M. Steinbach, V. Kumar, Introduction to data mining (Pearson Education Limited, New York, 2014)
L. Wasserman, All of statistics: a concise course in statistical inference (Springer, New York, 2004)
E. Vul, H. Pashler, Psychol Sci. 19, 645 (2008)
P. Meehl, Clinical versus statistical prediction (University of Minnesota Press, Minneapolis, 1954)
P.E. Tetlock, D. Gardner, Superforecasting: the art and science of prediction (Crown Publishing Group, New York, 2016)
C. Chatfield, Time-series forecasting (CRC Press, Boca Raton, 2000)
R.J. Hyndman, G. Athanasopoulos, Forecasting: principles and practice (OTexts, New York, 2018)
L. Rendell, R. Boyd, D. Cownden, M. Enquist, K. Eriksson, M.W. Feldman, L. Fogarty, S. Ghirlanda, T. Lillicrap, K.N. Laland, Science 328, 208 (2010)
J.F. Fontanari, PLoS ONE 9, e110517 (2014)
S.M. Reia, A.C. Amado, J.F. Fontanari, Phys. Life Rev. 31, 320 (2019)
G. MacKay, Extraordinary delusions and the madness of crowds (Richard Bentley, London, 1841)
S.M. Stigler, Statistics on the table: the history of statistical concepts and methods: the history of statistical concepts and methods (Harvard University Press, Cambridge, 2002)
K. Donnelly, Adolphe quetelet, social physics and the average men of science, 1796–1874 (University of Pittsburgh Press, Pittsburgh, 2015)
M. Perc, Sci. Rep. 9, 16549 (2019)
Acknowledgements
The research of JFF was supported in part by Grant No. 2020/03041-3, Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) and by Grant No. 305620/2021-5, Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq).
Author information
Authors and Affiliations
Contributions
All the authors contributed equally to the paper.
Corresponding author
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Neto, N.S.S., Fontanari, J.F. On the efficacy of the wisdom of crowds to forecast economic indicators. Eur. Phys. J. B 96, 6 (2023). https://doi.org/10.1140/epjb/s10051-023-00482-6
Received:
Accepted:
Published:
DOI: https://doi.org/10.1140/epjb/s10051-023-00482-6