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Measuring the predictability of life outcomes with a scientific mass collaboration.

Matthew J. Salganik, +114 more
- 14 Apr 2020 - 
- Vol. 117, Iss: 15, pp 8398-8403
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TLDR
Practical limits to the predictability of life outcomes in some settings are suggested and the value of mass collaborations in the social sciences is illustrated.
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.

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Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

Samantha Joel, +88 more
TL;DR: The findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person’s own relationship-specific experiences, and effects due to moderation byindividual differences and moderation by partner-reports may be quite small.
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Algorithmic injustice: a relational ethics approach.

TL;DR: In this article, a fundamental shift from rational to relational-in thinking about personhood, data, justice, and everything in between, and places ethics as something that goes above and beyond technical solutions is proposed.
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The American Occupational Structure

TL;DR: The American Occupational Structure (AOS) as discussed by the authors is the classic source of empirical information on the patterns of occupational achievement in American society and is renowned for its pioneering methods of statistical analysis as well as for its far-reaching conclusions about social stratification and occupational mobility in the United States.
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Early life stress and development: potential mechanisms for adverse outcomes.

TL;DR: Assessment of factors that influence children’s interpretation of stressors, along with stressful events, has the potential to provide further insight into the mechanisms contributing to individual differences in neurodevelopmental effects of early life stress.
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Integrating explanation and prediction in computational social science.

TL;DR: In this article, the authors propose a schema for thinking about research activities along two dimensions-the extent to which work is explanatory, focusing on identifying and estimating causal effects, and the degree of consideration given to testing predictions of outcomes.
References
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Book

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TL;DR: In this paper, the authors describe the important ideas in these areas in a common conceptual framework, and the emphasis is on concepts rather than mathematics, with a liberal use of color graphics.
Journal ArticleDOI

Estimating the reproducibility of psychological science

Alexander A. Aarts, +290 more
- 28 Aug 2015 - 
TL;DR: A large-scale assessment suggests that experimental reproducibility in psychology leaves a lot to be desired, and correlational tests suggest that replication success was better predicted by the strength of original evidence than by characteristics of the original and replication teams.
Book

The American occupational structure

TL;DR: The American Occupational Structure is renowned for its pioneering methods of statistical analysis as well as for its far-reaching conclusions about social stratification and occupational mobility in the United States.
Journal ArticleDOI

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

TL;DR: This Perspective clarifies the chasm between explaining black boxes and using inherently interpretable models, outlines several key reasons why explainable black boxes should be avoided in high-stakes decisions, identifies challenges to interpretable machine learning, and provides several example applications whereinterpretable models could potentially replace black box models in criminal justice, healthcare and computer vision.
Journal ArticleDOI

Clinical versus actuarial judgment

TL;DR: Research comparing these two approaches to decision-making shows the actuarial method to be superior, factors underlying the greater accuracy of actuarial methods, sources of resistance to the scientific findings, and the benefits of increased reliance on actuarial approaches are discussed.
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