Insights into the accuracy of social scientists’ forecasts of societal change

Igor Grossmann; Amanda Rotella; Cendri A. Hutcherson; Konstantyn Sharpinskyi; Michael E.W. Varnum; Sebastian Achter; Mandeep K. Dhami; Xinqi Evie Guo; Mane Kara-Yakoubian; David R. Mandel; Louis Raes; Louis Tay; Aymeric Vie; Lisa Wagner; Matus Adamkovic; Arash Arami; Patrícia Arriaga; Kasun Bandara; Gabriel Baník; František Bartoš; Ernest Baskin; Christoph Bergmeir; Michał Białek; Caroline K. Børsting; Dillon T. Browne; Eugene M. Caruso; Rong Chen; Bin Tzong Chie; William J. Chopik; Robert N. Collins; Chin Wen Cong; Lucian G. Conway; Matthew Davis; Martin V. Day; Nathan A. Dhaliwal; Justin D. Durham; Martyna Dziekan; Christian T. Elbaek; Eric Shuman; Marharyta Fabrykant; Mustafa Firat; Geoffrey T. Fong; Jeremy A. Frimer; Jonathan M. Gallegos; Simon B. Goldberg; Anton Gollwitzer; Julia Goyal; Lorenz Graf-Vlachy; Ruslan Krenzler; Timur Sevincer

doi:10.1038/s41562-022-01517-1

Insights into the accuracy of social scientists’ forecasts of societal change

Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet

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Insights into the accuracy of social scientists’ forecasts of societal change. / Grossmann, Igor; Rotella, Amanda; Hutcherson, Cendri A. et al.
in: Nature Human Behaviour, Jahrgang 7, Nr. 4, 04.2023, S. 484-501.

Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet

Harvard

Grossmann, I, Rotella, A, Hutcherson, CA, Sharpinskyi, K, Varnum, MEW, Achter, S, Dhami, MK, Guo, XE, Kara-Yakoubian, M, Mandel, DR, Raes, L, Tay, L, Vie, A, Wagner, L, Adamkovic, M, Arami, A, Arriaga, P, Bandara, K, Baník, G, Bartoš, F, Baskin, E, Bergmeir, C, Białek, M, Børsting, CK, Browne, DT, Caruso, EM, Chen, R, Chie, BT, Chopik, WJ, Collins, RN, Cong, CW, Conway, LG, Davis, M, Day, MV, Dhaliwal, NA, Durham, JD, Dziekan, M, Elbaek, CT, Shuman, E, Fabrykant, M, Firat, M, Fong, GT, Frimer, JA, Gallegos, JM, Goldberg, SB, Gollwitzer, A, Goyal, J, Graf-Vlachy, L, Krenzler, R & Sevincer, T 2023, 'Insights into the accuracy of social scientists’ forecasts of societal change', Nature Human Behaviour, Jg. 7, Nr. 4, S. 484-501. https://doi.org/10.1038/s41562-022-01517-1

APA

Grossmann, I., Rotella, A., Hutcherson, C. A., Sharpinskyi, K., Varnum, M. E. W., Achter, S., Dhami, M. K., Guo, X. E., Kara-Yakoubian, M., Mandel, D. R., Raes, L., Tay, L., Vie, A., Wagner, L., Adamkovic, M., Arami, A., Arriaga, P., Bandara, K., Baník, G., ... Sevincer, T. (2023). Insights into the accuracy of social scientists’ forecasts of societal change. Nature Human Behaviour, 7(4), 484-501. https://doi.org/10.1038/s41562-022-01517-1

Vancouver

Grossmann I, Rotella A, Hutcherson CA, Sharpinskyi K, Varnum MEW, Achter S et al. Insights into the accuracy of social scientists’ forecasts of societal change. Nature Human Behaviour. 2023 Apr;7(4):484-501. doi: 10.1038/s41562-022-01517-1

Bibtex

@article{ceba1c46c0ed4748be840851056a50b0,

title = "Insights into the accuracy of social scientists{\textquoteright} forecasts of societal change",

abstract = "How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing the accuracy of predictions of societal change in domains commonly studied in the social sciences: ideological preferences, political polarization, life satisfaction, sentiment on social media, and gender–career and racial bias. After we provided them with historical trend data on the relevant domain, social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N = 86 teams and 359 forecasts), with an opportunity to update forecasts on the basis of new data six months later (Tournament 2; N = 120 teams and 546 forecasts). Benchmarking forecasting accuracy revealed that social scientists{\textquoteright} forecasts were on average no more accurate than those of simple statistical models (historical means, random walks or linear regressions) or the aggregate forecasts of a sample from the general public (N = 802). However, scientists were more accurate if they had scientific expertise in a prediction domain, were interdisciplinary, used simpler models and based predictions on prior data.",

keywords = "Psychology",

author = "Igor Grossmann and Amanda Rotella and Hutcherson, {Cendri A.} and Konstantyn Sharpinskyi and Varnum, {Michael E.W.} and Sebastian Achter and Dhami, {Mandeep K.} and Guo, {Xinqi Evie} and Mane Kara-Yakoubian and Mandel, {David R.} and Louis Raes and Louis Tay and Aymeric Vie and Lisa Wagner and Matus Adamkovic and Arash Arami and Patr{\'i}cia Arriaga and Kasun Bandara and Gabriel Ban{\'i}k and Franti{\v s}ek Barto{\v s} and Ernest Baskin and Christoph Bergmeir and Micha{\l} Bia{\l}ek and B{\o}rsting, {Caroline K.} and Browne, {Dillon T.} and Caruso, {Eugene M.} and Rong Chen and Chie, {Bin Tzong} and Chopik, {William J.} and Collins, {Robert N.} and Cong, {Chin Wen} and Conway, {Lucian G.} and Matthew Davis and Day, {Martin V.} and Dhaliwal, {Nathan A.} and Durham, {Justin D.} and Martyna Dziekan and Elbaek, {Christian T.} and Eric Shuman and Marharyta Fabrykant and Mustafa Firat and Fong, {Geoffrey T.} and Frimer, {Jeremy A.} and Gallegos, {Jonathan M.} and Goldberg, {Simon B.} and Anton Gollwitzer and Julia Goyal and Lorenz Graf-Vlachy and Ruslan Krenzler and Timur Sevincer",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive licence to Springer Nature Limited.",

year = "2023",

month = apr,

doi = "10.1038/s41562-022-01517-1",

language = "English",

volume = "7",

pages = "484--501",

journal = "Nature Human Behaviour",

issn = "2397-3374",

publisher = "Nature Publishing Group",

number = "4",

}

RIS

TY - JOUR

T1 - Insights into the accuracy of social scientists’ forecasts of societal change

AU - Grossmann, Igor

AU - Rotella, Amanda

AU - Hutcherson, Cendri A.

AU - Sharpinskyi, Konstantyn

AU - Varnum, Michael E.W.

AU - Achter, Sebastian

AU - Dhami, Mandeep K.

AU - Guo, Xinqi Evie

AU - Kara-Yakoubian, Mane

AU - Mandel, David R.

AU - Raes, Louis

AU - Tay, Louis

AU - Vie, Aymeric

AU - Wagner, Lisa

AU - Adamkovic, Matus

AU - Arami, Arash

AU - Arriaga, Patrícia

AU - Bandara, Kasun

AU - Baník, Gabriel

AU - Bartoš, František

AU - Baskin, Ernest

AU - Bergmeir, Christoph

AU - Białek, Michał

AU - Børsting, Caroline K.

AU - Browne, Dillon T.

AU - Caruso, Eugene M.

AU - Chen, Rong

AU - Chie, Bin Tzong

AU - Chopik, William J.

AU - Collins, Robert N.

AU - Cong, Chin Wen

AU - Conway, Lucian G.

AU - Davis, Matthew

AU - Day, Martin V.

AU - Dhaliwal, Nathan A.

AU - Durham, Justin D.

AU - Dziekan, Martyna

AU - Elbaek, Christian T.

AU - Shuman, Eric

AU - Fabrykant, Marharyta

AU - Firat, Mustafa

AU - Fong, Geoffrey T.

AU - Frimer, Jeremy A.

AU - Gallegos, Jonathan M.

AU - Goldberg, Simon B.

AU - Gollwitzer, Anton

AU - Goyal, Julia

AU - Graf-Vlachy, Lorenz

AU - Krenzler, Ruslan

AU - Sevincer, Timur

PY - 2023/4

Y1 - 2023/4

N2 - How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing the accuracy of predictions of societal change in domains commonly studied in the social sciences: ideological preferences, political polarization, life satisfaction, sentiment on social media, and gender–career and racial bias. After we provided them with historical trend data on the relevant domain, social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N = 86 teams and 359 forecasts), with an opportunity to update forecasts on the basis of new data six months later (Tournament 2; N = 120 teams and 546 forecasts). Benchmarking forecasting accuracy revealed that social scientists’ forecasts were on average no more accurate than those of simple statistical models (historical means, random walks or linear regressions) or the aggregate forecasts of a sample from the general public (N = 802). However, scientists were more accurate if they had scientific expertise in a prediction domain, were interdisciplinary, used simpler models and based predictions on prior data.

AB - How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing the accuracy of predictions of societal change in domains commonly studied in the social sciences: ideological preferences, political polarization, life satisfaction, sentiment on social media, and gender–career and racial bias. After we provided them with historical trend data on the relevant domain, social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N = 86 teams and 359 forecasts), with an opportunity to update forecasts on the basis of new data six months later (Tournament 2; N = 120 teams and 546 forecasts). Benchmarking forecasting accuracy revealed that social scientists’ forecasts were on average no more accurate than those of simple statistical models (historical means, random walks or linear regressions) or the aggregate forecasts of a sample from the general public (N = 802). However, scientists were more accurate if they had scientific expertise in a prediction domain, were interdisciplinary, used simpler models and based predictions on prior data.

KW - Psychology

UR - http://www.scopus.com/inward/record.url?scp=85147648888&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/4dc17226-e243-3733-837b-65d5ed75c63d/

U2 - 10.1038/s41562-022-01517-1

DO - 10.1038/s41562-022-01517-1

M3 - Journal articles

C2 - 36759585

AN - SCOPUS:85147648888

VL - 7

SP - 484

EP - 501

JO - Nature Human Behaviour

JF - Nature Human Behaviour

SN - 2397-3374

IS - 4

ER -

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DOI

https://doi.org/10.1038/s41562-022-01517-1
Endgültige, publizierte Fassung

Insights into the accuracy of social scientists’ forecasts of societal change

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In der gleichen Zeitschrift

How young children integrate information sources to infer the meaning of words

A cultural theory of regimes

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