Measuring the predictability of life outcomes with a scientific mass collaboration

Author:

Salganik Matthew J.,Lundberg IanORCID,Kindel Alexander T.,Ahearn Caitlin E.,Al-Ghoneim Khaled,Almaatouq AbdullahORCID,Altschul Drew M.ORCID,Brand Jennie E.,Carnegie Nicole BohmeORCID,Compton Ryan James,Datta Debanjan,Davidson Thomas,Filippova Anna,Gilroy Connor,Goode Brian J.,Jahani Eaman,Kashyap RidhiORCID,Kirchner Antje,McKay StephenORCID,Morgan Allison C.ORCID,Pentland Alex,Polimis Kivan,Raes LouisORCID,Rigobon Daniel E.,Roberts Claudia V.,Stanescu Diana M.,Suhara Yoshihiko,Usmani Adaner,Wang Erik H.,Adem Muna,Alhajri Abdulla,AlShebli Bedoor,Amin Redwane,Amos Ryan B.,Argyle Lisa P.ORCID,Baer-Bositis Livia,Büchi MoritzORCID,Chung Bo-Ryehn,Eggert William,Faletto Gregory,Fan Zhilin,Freese Jeremy,Gadgil Tejomay,Gagné Josh,Gao Yue,Halpern-Manners Andrew,Hashim Sonia P.,Hausen Sonia,He Guanhua,Higuera Kimberly,Hogan Bernie,Horwitz Ilana M.,Hummel Lisa M.,Jain Naman,Jin KunORCID,Jurgens David,Kaminski Patrick,Karapetyan Areg,Kim E. H.,Leizman Ben,Liu Naijia,Möser Malte,Mack Andrew E.,Mahajan Mayank,Mandell Noah,Marahrens Helge,Mercado-Garcia Diana,Mocz Viola,Mueller-Gastell Katariina,Musse Ahmed,Niu Qiankun,Nowak William,Omidvar Hamidreza,Or Andrew,Ouyang Karen,Pinto Katy M.,Porter Ethan,Porter Kristin E.,Qian Crystal,Rauf Tamkinat,Sargsyan Anahit,Schaffner Thomas,Schnabel Landon,Schonfeld Bryan,Sender Ben,Tang Jonathan D.,Tsurkov Emma,van Loon Austin,Varol OnurORCID,Wang Xiafei,Wang Zhi,Wang Julia,Wang Flora,Weissman Samantha,Whitaker Kirstie,Wolters Maria K.,Woon Wei Lee,Wu James,Wu Catherine,Yang Kengran,Yin Jingwen,Zhao Bingyu,Zhu Chenyun,Brooks-Gunn Jeanne,Engelhardt Barbara E.,Hardt Moritz,Knox Dean,Levy Karen,Narayanan Arvind,Stewart Brandon M.,Watts Duncan J.ORCID,McLanahan Sara

Abstract

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.

Funder

Russell Sage Foundation

National Science Foundation

HHS | NIH | Eunice Kennedy Shriver National Institute of Child Health and Human Development

HHS | National Institutes of Health

MRC Mental Health Data Pathfinder Award

DOD | Defense Advanced Research Projects Agency

ED | Institute of Education Sciences

Chicano Studies Research Center, UCLA

RCUK | Engineering and Physical Sciences Research Council

Publisher

Proceedings of the National Academy of Sciences

Subject

Multidisciplinary

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