Experts appear no better at predicting future social trends than the rest of us

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We often turn to experts to help us predict what might happen in the future, but it turns out experts might be no better than the rest of us at predicting trends in social changes.  Australian and international researchers challenged social scientists in two "forecasting tournaments" to submit monthly forecasts for a year on a variety of social trends such as political polarisation, racial bias or well-being. They found that social scientists’ forecasts were on average no more accurate than those of simple statistical models or the combined forecasts of a sample from the general public. However, scientists were more accurate if they had scientific expertise in the specific area. The authors say that despite common beliefs that social science experts are better equipped to accurately predict these trends than non-experts, the current findings suggest that social and behavioural scientists have a lot of room for growth. 

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conference:
Nature Human Behaviour
Research:Paper
Organisation/s: The University of Melbourne, Monash University, The University of Queensland, Macquarie University, University of Waterloo, Canada
Funder: This programme of research was supported by the Basic Research Program at the National Research University Higher School of Economics (M. Fabrykant), John Templeton Foundation grant no. 62260 (I.G. and P.E.T.), Kega 079UK-4/2021 (P.K.), Ministerio de Ciencia e Innovación España grants no. PID2019-111512RB-I00-HMDM and no. HDL-HS-280218 (A.A.), the National Center for Complementary & Integrative Health of the National Institutes of Health under award no. K23AT010879 (S.B.G.), National Science Foundation RAPID grant no. 2026854 (M.E.W.V.), PID2019-111512RB-I00 (M.S.), NPO Systemic Risk Institute grant no. LX22NPO5101 (I.R.), the Slovak Research and Development Agency under contract no. APVV-20-0319 (M.A.), Social Sciences and Humanities Research Council of Canada Insight grant no. 435-2014-0685 (I.G.), Social Sciences and Humanities Research Council of Canada Connection grant no. 611-2020-0190 (I.G.), and Swiss National Science Foundation grant no. PP00P1_170463 (O. Strijbis).
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