Measuring the predictability of life outcomes with a scientific mass collaboration

© 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...

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Formato: Artigo
Idioma:English
Publicado em: Proceedings of the National Academy of Sciences 2021
Acesso em linha:https://hdl.handle.net/1721.1/135360
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collection MIT
description © 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.
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spelling mit-1721.1/1353602022-04-01T17:47:10Z Measuring the predictability of life outcomes with a scientific mass collaboration © 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. 2021-10-27T20:23:08Z 2021-10-27T20:23:08Z 2020 2021-02-02T18:52:00Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/135360 en 10.1073/PNAS.1915006117 Proceedings of the National Academy of Sciences of the United States of America Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Proceedings of the National Academy of Sciences PNAS
spellingShingle Measuring the predictability of life outcomes with a scientific mass collaboration
title Measuring the predictability of life outcomes with a scientific mass collaboration
title_full Measuring the predictability of life outcomes with a scientific mass collaboration
title_fullStr Measuring the predictability of life outcomes with a scientific mass collaboration
title_full_unstemmed Measuring the predictability of life outcomes with a scientific mass collaboration
title_short Measuring the predictability of life outcomes with a scientific mass collaboration
title_sort measuring the predictability of life outcomes with a scientific mass collaboration
url https://hdl.handle.net/1721.1/135360