The Good, the Bad, and the Unflinchingly Selfish

The human willingness to pay costs to benefit anonymous others is often explained by social preferences: rather than only valuing their own material payoff, people also include the payoffs of others in their utility function. But how successful is this concept of outcome-based social preferences for actually predicting out-of-sample behavior? We investigate this question by having 1,067 participants each make 20 Dictator Game decisions with randomized parameters (e.g., outcomes for the self, for the other, benefit/cost ratio of pro-sociality). We then use machine learning to try to predict behavior by each participant in each decision. A representative agent model (a small, shared, set of parameters) predicts better than random but still quite poorly (AUC = 0.69). Allowing for full heterogeneity across individuals in the mapping from decision-parameters to outcome yields good predictive performance (AUC = 0.89). However, this heterogeneous model is complicated and unwieldy, thus we also investigate whether a simpler model can yield similar performance. We find that the vast majority of the predictive power (AUC = 0.88) is achieved by a model that allows for three behavioral types. Finally, we show that cannot be well proxied for by other measures in psychology. This final analysis adds further evidence to the literature that human “cooperative phenotypes” are indeed meaningful, relatively orthogonal person-level traits.