Learning What to Want: Data-Driven Microfoundations

Abstract

We have developed a method for directly learning relative preferences from histories of comparison information without an intermediate utility computation. Our method infers preferences that are rational in a psychological sense, where agent choices result from Bayesian inference of what to do from observable inputs. We further characterize conditions wherein it is appropriate for modelers to describe relative preferences as scalar utilities, and illustrate the importance of option availability in supplying auxiliary information by explaining all major categories of context effects, as well as predicting novel context effects. Applying our theory to predicting choices under uncertainty leads to good fits with empirical data and endogenous explanations for a number of economic behaviors. By retrieving economic rationality as a special case of psychologically rational preference formation, this work clarifies theoretical connections between economic and psychological definitions of rationality.