1. Insights into the accuracy of social scientists’ forecasts of societal change
- Author
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Grossmann, Igor, Rotella, Amanda, Hutcherson, Cendri A., Sharpinskyi, Konstantyn, Varnum, Michael E. W., Achter, Sebastian, Dhami, Mandeep K., Guo, Xinqi Evie, Kara-Yakoubian, Mane, Mandel, David R., Raes, Louis, Tay, Louis, Vie, Aymeric, Wagner, Lisa, Adamkovic, Matus, Arami, Arash, Arriaga, Patrícia, Bandara, Kasun, Baník, Gabriel, Bartoš, František, Baskin, Ernest, Bergmeir, Christoph, Białek, Michał, Børsting, Caroline K., Browne, Dillon T., Caruso, Eugene M., Chen, Rong, Chie, Bin-Tzong, Chopik, William J., Collins, Robert N., Cong, Chin Wen, Conway, Lucian G., Davis, Matthew, Day, Martin V., Dhaliwal, Nathan A., Durham, Justin D., Dziekan, Martyna, Elbaek, Christian T., Shuman, Eric, Fabrykant, Marharyta, Firat, Mustafa, Fong, Geoffrey T., Frimer, Jeremy A., Gallegos, Jonathan M., Goldberg, Simon B., Gollwitzer, Anton, Goyal, Julia, Graf-Vlachy, Lorenz, Gronlund, Scott D., Hafenbrädl, Sebastian, Hartanto, Andree, Hirshberg, Matthew J., Hornsey, Matthew J., Howe, Piers D. L., Izadi, Anoosha, Jaeger, Bastian, Kačmár, Pavol, Kim, Yeun Joon, Krenzler, Ruslan, Lannin, Daniel G., Lin, Hung-Wen, Lou, Nigel Mantou, Lua, Verity Y. Q., Lukaszewski, Aaron W., Ly, Albert L., Madan, Christopher R., Maier, Maximilian, Majeed, Nadyanna M., March, David S., Marsh, Abigail A., Misiak, Michal, Myrseth, Kristian Ove R., Napan, Jaime M., Nicholas, Jonathan, Nikolopoulos, Konstantinos, O, Jiaqing, Otterbring, Tobias, Paruzel-Czachura, Mariola, Pauer, Shiva, Protzko, John, Raffaelli, Quentin, Ropovik, Ivan, Ross, Robert M., Roth, Yefim, Røysamb, Espen, Schnabel, Landon, Schütz, Astrid, Seifert, Matthias, Sevincer, A. T., Sherman, Garrick T., Simonsson, Otto, Sung, Ming-Chien, Tai, Chung-Ching, Talhelm, Thomas, Teachman, Bethany A., Tetlock, Philip E., Thomakos, Dimitrios, Tse, Dwight C. K., Twardus, Oliver J., Tybur, Joshua M., Ungar, Lyle, Vandermeulen, Daan, Vaughan Williams, Leighton, Vosgerichian, Hrag A., Wang, Qi, Wang, Ke, Whiting, Mark E., Wollbrant, Conny E., Yang, Tao, Yogeeswaran, Kumar, Yoon, Sangsuk, Alves, Ventura R., Andrews-Hanna, Jessica R., Bloom, Paul A., Boyles, Anthony, Charis, Loo, Choi, Mingyeong, Darling-Hammond, Sean, Ferguson, Z. E., Kaiser, Cheryl R., Karg, Simon T., Ortega, Alberto López, Mahoney, Lori, Marsh, Melvin S., Martinie, Marcellin F. R. C., Michaels, Eli K., Millroth, Philip, Naqvi, Jeanean B., Ng, Weiting, Rutledge, Robb B., Slattery, Peter, Smiley, Adam H., Strijbis, Oliver, Sznycer, Daniel, Tsukayama, Eli, van Loon, Austin, Voelkel, Jan G., Wienk, Margaux N. A., Wilkening, Tom, Collaborative, The Forecasting, Organizational Psychology, Social Psychology, IBBA, Research Group: Economics, Department of Economics, Department of Social Psychology, Department of Finance, and The Forecasting Collaborative [Member of the MPIB: Anton Gollwitzer]
- Subjects
Inequality, cohesion and modernization ,Behavioral Neuroscience ,Social Psychology ,SDG 5 - Gender Equality ,IMPLICIT ,Experimental and Cognitive Psychology ,Ongelijkheid, cohesie en modernisering ,ATTITUDES ,SDG 10 - Reduced Inequalities - Abstract
Item does not contain fulltext 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. 18 p.
- Published
- 2023
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