1. Measuring the predictability of life outcomes with a scientific mass collaboration
- Author
-
Salganik, Matthew J, Lundberg, Ian, Kindel, Alexander T, Ahearn, Caitlin E, Al-Ghoneim, Khaled, Almaatouq, Abdullah, Altschul, Drew M, Brand, Jennie E, Carnegie, Nicole Bohme, Compton, Ryan James, Datta, Debanjan, Davidson, Thomas, Filippova, Anna, Gilroy, Connor, Goode, Brian J, Jahani, Eaman, Kashyap, Ridhi, Kirchner, Antje, McKay, Stephen, Morgan, Allison C, Pentland, Alex, Polimis, Kivan, Raes, Louis, 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, Baer-Bositis, Livia, Büchi, Moritz, 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, Kun, Jurgens, David, Kaminski, Patrick, Karapetyan, Areg, Kim, EH, 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, Onur, Wang, Xiafei, Wang, Zhi, Wang, Julia, Wang, Flora, Weissman, Samantha, Whitaker, Kirstie, Wolters, Maria K, Woon, Wei Lee, Wu, James, Wu, Catherine, and Yang, Kengran
- Subjects
Pediatric ,Behavioral and Social Science ,Generic health relevance ,Adolescent ,Child ,Child ,Preschool ,Cohort Studies ,Family ,Female ,Humans ,Infant ,Life ,Machine Learning ,Male ,Predictive Value of Tests ,Social Sciences ,life course ,prediction ,machine learning ,mass collaboration - 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.
- Published
- 2020