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dc.contributor.authorAlmaatouq, Abdullah
dc.contributor.authorPentland, Alex
dc.contributor.authorSuhara, Yoshihiko
dc.contributor.authorJahani, Eaman
dc.contributor.authorAlhajri, Abdulla Abdulaziz
dc.date.accessioned2022-09-08T20:21:41Z
dc.date.available2021-10-27T20:23:08Z
dc.date.available2022-09-08T20:21:41Z
dc.date.issued2020
dc.identifier.urihttps://hdl.handle.net/1721.1/135360.2
dc.description.abstract© This open access article is distributed under Creative Commons Attribution-NonCommercialNoDerivatives License 4.0 (CC BY-NC-ND). How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences.en_US
dc.language.isoen
dc.publisherProceedings of the National Academy of Sciencesen_US
dc.relation.isversionof10.1073/PNAS.1915006117en_US
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivs Licenseen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.sourcePNASen_US
dc.titleMeasuring the predictability of life outcomes with a scientific mass collaborationen_US
dc.typeArticleen_US
dc.contributor.departmentSloan School of Managementen_US
dc.contributor.departmentMassachusetts Institute of Technology. Media Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Data, Systems, and Societyen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Nuclear Science and Engineeringen_US
dc.relation.journalProceedings of the National Academy of Sciences of the United States of Americaen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2021-02-02T18:52:00Z
dspace.orderedauthorsSalganik, MJ; Lundberg, I; Kindel, AT; Ahearn, CE; Al-Ghoneim, K; Almaatouq, A; Altschul, DM; Brand, JE; Carnegie, NB; Compton, RJ; Datta, D; Davidson, T; Filippova, A; Gilroy, C; Goode, BJ; Jahani, E; Kashyap, R; Kirchner, A; McKay, S; Morgan, AC; Pentland, A; Polimis, K; Raes, L; Rigobon, DE; Roberts, CV; Stanescu, DM; Suhara, Y; Usmani, A; Wang, EH; Adem, M; Alhajri, A; AlShebli, B; Amin, R; Amos, RB; Argyle, LP; Baer-Bositis, L; Büchi, M; Chung, B-R; Eggert, W; Faletto, G; Fan, Z; Freese, J; Gadgil, T; Gagné, J; Gao, Y; Halpern-Manners, A; Hashim, SP; Hausen, S; He, G; Higuera, K; Hogan, B; Horwitz, IM; Hummel, LM; Jain, N; Jin, K; Jurgens, D; Kaminski, P; Karapetyan, A; Kim, EH; Leizman, B; Liu, N; Möser, M; Mack, AE; Mahajan, M; Mandell, N; Marahrens, H; Mercado-Garcia, D; Mocz, V; Mueller-Gastell, K; Musse, A; Niu, Q; Nowak, W; Omidvar, H; Or, A; Ouyang, K; Pinto, KM; Porter, E; Porter, KE; Qian, C; Rauf, T; Sargsyan, A; Schaffner, T; Schnabel, L; Schonfeld, B; Sender, B; Tang, JD; Tsurkov, E; van Loon, A; Varol, O; Wang, X; Wang, Z; Wang, J; Wang, F; Weissman, S; Whitaker, K; Wolters, MK; Woon, WL; Wu, J; Wu, C; Yang, K; Yin, J; Zhao, B; Zhu, C; Brooks-Gunn, J; Engelhardt, BE; Hardt, M; Knox, D; Levy, K; Narayanan, A; Stewart, BM; Watts, DJ; McLanahan, Sen_US
dspace.date.submission2021-02-02T18:52:02Z
mit.journal.volume117en_US
mit.journal.issue15en_US
mit.licensePUBLISHER_CC
mit.metadata.statusPublication Information Neededen_US


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