Choose for others as you would choose for yourself? A layered analysis of probabilistic preferential choice across social distances

The present study examines the effect of social distance on choice behavior through the lens of a probabilistic modeling framework. In an experiment, participants made incentive-compatible choices between lotteries in three different social distance conditions: self, friend, and stranger. We conduct a layered, within-subjects analysis that considers four properties of preferential choice. These properties vary in their granularity. At the coarsest level, we test whether choices are consistent with transitive underlying preferences. At a finer level of granularity, we evaluate whether each participant is best described as having fixed preferences with random errors or probabilistic preferences with error-free choices. In the latter case, we further distinguish three different bounds on response error rates. At the finest level, we identify the specific transitive preference ranking of the choice options that best describes a person's choices. At each level of the analysis, we find that the stability between the self and friend conditions exceeds that between the self and stranger conditions. Stability increases with the coarseness of the analysis: Nearly all people are consistent with transitive preferences regardless of the social distance condition, but only for very few do we infer the same preference ranking in every social distance condition. Overall, while it matters whether one makes a choice on behalf of a friend versus for a stranger, the differences are most apparent when analyzing the data at a detailed level of granularity.