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Measuring the Predictability of Life Outcomes with a Scientific Mass Collaboration

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Bibliographic Details
Title: Measuring the Predictability of Life Outcomes with a Scientific Mass Collaboration
Language: English
Authors: Matthew J. Salganik, Ian Lundberg (ORCID 0000-0002-1909-2270), Alexander T. Kindel, Caitlin E. Ahearn, Khaled Al-Ghoneim, Abdullah Almaatouq (ORCID 0000-0002-8467-9123), Drew M. Altschul (ORCID 0000-0001-7053-4209), Jennie E. Brand, Nicole Bohme Carnegie (ORCID 0000-0001-7664-6682), Ryan James Compton, Debanjan Datta, Thomas Davidson, Anna Filippova, Connor Gilroy, Brian J. Goode, Eaman Jahani, Ridhi Kashyap (ORCID 0000-0003-0615-2868), Antje Kirchner, Stephen McKay (ORCID 0000-0002-5080-8417), Allison C. Morgan (ORCID 0000-0003-2926-2162), Alex Pentland, Kivan Polimis, Louis Raes (ORCID 0000-0003-2640-7493), Daniel E. Rigobon, Claudia V. Roberts, Diana M. Stanescu, Yoshihiko Suhara, Adaner Usmani, Erik H. Wang, Muna Adem, Abdulla Alhajri, Bedoor AlShebli, Redwane Amin, Ryan B. Amos, Lisa P. Argyle (ORCID 0000-0003-3109-2537), Livia Baer-Bositis, Moritz Büchi (ORCID 0000-0002-9202-889X), Bo-Ryehn Chung, William Eggert, Gregory Faletto, Zhilin Fan, Jeremy Freese, Tejomay Gadgil, Josh Gagné, Yue Gao, Andrew Halpern-Manners, Sonia P. Hashim, Sonia Hausen, Guanhua He, Kimberly Higuera, Bernie Hogan, Ilana M. Horwitz, Lisa M. Hummel, Naman Jain, Kun Jin (ORCID 0000-0002-0118-1021), David Jurgens, Patrick Kaminski, Areg Karapetyan, E. H. Kim, Ben Leizman, Naijia Liu, Malte Möser, Andrew E. Mack, Mayank Mahajan, Noah Mandell, Helge Marahrens, Diana Mercado-Garcia, Viola Mocz, Katariina Mueller-Gastell, Ahmed Musse, Qiankun Niu, William Nowak, Hamidreza Omidvar, Andrew Or, Karen Ouyang, Katy M. Pinto, Ethan Porter, Kristin E. Porter, Crystal Qian, Tamkinat Rauf, Anahit Sargsyan, Thomas Schaffner, Landon Schnabel, Bryan Schonfeld, Ben Sender, Jonathan D. Tang, Emma Tsurkov, Austin van Loon, Onur Varol (ORCID 0000-0002-3994-6106), Xiafei Wang, Zhi Wang, Julia Wang, Flora Wang, Samantha Weissman, Kirstie Whitaker, Maria K. Wolters, Wei Lee Woon, James Wu, Catherine Wu, Kengran Yang, Jingwen Yin, Bingyu Zhao, Chenyun Zhu, Jeanne Brooks-Gunn, Barbara E. Engelhardt, Moritz Hardt, Dean Knox, Karen Levy, Arvind Narayanan, Brandon M. Stewart, Duncan J. Watts (ORCID 0000-0001-5005-4961), Sara McLanahan
Source: Grantee Submission. 2020 117(15).
Peer Reviewed: Y
Page Count: 7
Publication Date: 2020
Sponsoring Agency: Institute of Education Sciences (ED)
National Science Foundation (NSF)
Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) (DHHS/NIH)
Contract Number: R305B140009
1760052
Document Type: Journal Articles
Reports - Research
Descriptors: Life Satisfaction, Family Life, Quality of Life, Disadvantaged, Child Welfare, Predictive Measurement, Predictive Validity, Predictor Variables, Error of Measurement, Artificial Intelligence, Social Science Research, Educational Cooperation, At Risk Persons, Partnerships in Education, Test Validity
DOI: 10.1073/pnas.1915006117
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.
Abstractor: As Provided
IES Funded: Yes
Entry Date: 2025
Accession Number: ED669697
Database: ERIC
Description
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.
DOI:10.1073/pnas.1915006117