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National Academy of Sciences, Proceedings of the National Academy of Sciences, 15(117), p. 8398-8403, 2020

DOI: 10.1073/pnas.1915006117

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Measuring the predictability of life outcomes with a scientific mass collaboration

Journal article published in 2020 by Matthew J. Salganik, Ian Lundberg ORCID, Alexander T. Kindel, Caitlin E. Ahearn, Khaled Al-Ghoneim, Abdullah Almaatouq ORCID, Drew M. Altschul ORCID, Jennie E. Brand, Nicole Bohme Carnegie ORCID, Ryan James Compton, Debanjan Datta, Thomas Davidson, Anna Filippova, Connor Gilroy, Brian J. Goode and other authors.
This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

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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.