Inferring Hidden Motives: A Utility Bayesian Model of learning the values of others

Cooperation depends on judging whom to trust: identifying the strengths of other people's morally relevant social motives, called social preferences, and refining those beliefs after observing their behavior over time.

We model this process with a Utility Bayesian Model (UBM): a continuous probability distribution over the parameters of another person's utility function, reweighted after each observed choice.

In repeated binary dictator games with randomized payoffs, the UBM captured participants' predictions of preprogrammed agents and of one another better than non-Bayesian alternatives and over 15,000 models that represent others as discrete types: people track motives as graded positions in a continuous space, not as categories.

Because participants alternated between choosing and predicting, the framework separately estimates preferences and beliefs about preferences.

Choosers' own utilities, fit with a seven-parameter function selected from 526 candidate forms, showed that most people place reliably positive weight on a stranger's payoff but weight their own roughly nine times more; that aversion to being ahead exceeds aversion to being behind, reversing the canonical ordering; that sensitivity to others' payoffs and to inequalities grows more than proportionally with their size; and that antisocial preferences are more common than prior estimates suggest.

Predictors' initial beliefs captured the dominance of self-interest but expected aversion to being behind to outweigh aversion to being ahead.

Together, these parameter distributions locate any individual's social preferences, and beliefs about them, in a common seven-dimensional space, enabling commensurable cross-study comparisons, meta-analyses, and empirically informed priors for future Bayesian models of social cognition.