Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner's ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These 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 by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships.

https://www.pnas.org/content/pnas/117/32/19061.full.pdf

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

It has become trivial to point out that algorithmic systems increasingly pervade the social sphere. Improved efficiency-the hallmark of these systems-drives their mass integration into day-to-day life. However, as a robust body of research in the area of algorithmic injustice shows, algorithmic systems, especially when used to sort and predict social outcomes, are not only inadequate but also perpetuate harm. In particular, a persistent and recurrent trend within the literature indicates that society's most vulnerable are disproportionally impacted. When algorithmic injustice and harm are brought to the fore, most of the solutions on offer (1) revolve around technical solutions and (2) do not center disproportionally impacted communities. This paper proposes 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. Outlining the idea of ethics built on the foundations of relationality, this paper calls for a rethinking of justice and ethics as a set of broad, contingent, and fluid concepts and down-to-earth practices that are best viewed as a habit and not a mere methodology for data science. As such, this paper mainly offers critical examinations and reflection and not "solutions."

Algorithmic injustice: a relational ethics approach.

This book is the classic source of empirical information on the patterns of occupational achievement in American society. Based on an unusually comprehensive set of data, it 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. The American Occupational Structure received the Sorokin Award of the American Sociological Association in recognition of its significant contribution to the social sciences.

The American Occupational Structure

Chronic and/or extreme stress in early life, often referred to as early adversity, childhood trauma, or early life stress, has been associated with a wide range of adverse effects on development. However, while early life stress has been linked to negative effects on a number of neural systems, the specific mechanisms through which early life stress influences development and individual differences in children’s outcomes are still not well understood. The current paper reviews the existing literature on the neurobiological effects of early life stress and their ties to children’s psychological and behavioral development. Early life stress has persistent and pervasive effects on prefrontal–hypothalamic–amygdala and dopaminergic circuits that are at least partially mediated by alterations in hypothalamic–pituitary–adrenal axis function. However, to date, this research has primarily utilized methods of assessment that focus solely on children’s event exposures. Incorporating 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. This can aid in further elucidating specific mechanisms through which these neurobiological changes influence development and contribute to risk for psychopathology and health disorders.

/pdf/early-life-stress-and-development-potential-mechanisms-for-3zwn6rxpjo.pdf

Early life stress and development: potential mechanisms for adverse outcomes.

Computational social science is more than just large repositories of digital data and the computational methods needed to construct and analyse them. It also represents a convergence of different fields with different ways of thinking about and doing science. The goal of this Perspective is to provide some clarity around how these approaches differ from one another and to propose how they might be productively integrated. Towards this end we make two contributions. The first is 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-and how these two priorities can complement, rather than compete with, one another. Our second contribution is to advocate that computational social scientists devote more attention to combining prediction and explanation, which we call integrative modelling, and to outline some practical suggestions for realizing this goal.

Integrating explanation and prediction in computational social science.

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.

/pdf/measuring-the-predictability-of-life-outcomes-with-a-336fzc87lx.pdf

Qiankun Niu

Papers

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