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Abstract

Explaining prosocial behavior is a central goal in classic and contemporary behavioral science. Here, for the first time, we apply modern machine learning techniques to uncover the full predictive potential that personality traits have for prosocial behavior. We utilize a large-scale dataset (N = 2707; 81 personality traits) and state-of-the-art statistical models to predict an incentivized measure of prosocial behavior, Social Value Orientation (SVO). We conclude: (1) traits explain 13.9% of the variance in SVO; (2) linear models are sufficient to obtain good prediction; (3) trait–trait interactions do not improve prediction; (4) narrow traits improve prediction beyond basic personality (i.e., the HEXACO); (5) there is a moderate association between the univariate predictive power of a trait and its multivariate predictive power, suggesting that univariate estimates (e.g., Pearson’s correlation) can serve as a useful proxy for multivariate variable importance. We propose that the limited usefulness of nonlinear models may stem from current measurement practices in personality science, which tend to favor linearly related constructs. Overall, our study provides a benchmark for how well personality predicts SVO and charts a course toward better prediction of prosocial behavior.