📝 SummarXiv

Abstract

Recovering and distinguishing between the strict-preference, indifference and/or indecisiveness parts of a decision maker's preferences is a challenging task but also important for testing theory and conducting welfare analysis. This paper contributes towards this goal by reporting on data from a lab experiment on riskless choice that were analyzed with novel theory-guided computational methods. The experiment included both Forced- and Free-Choice treatments. Its main novelty consisted of allowing subjects to select multiple alternatives at each menu. Based on a new non-parametric goodness-of-fit criterion that we introduce, which generalizes a widely used pre-existing method to environments of multi-valued choices, each subject's decisions were tested against three structured general choice models that feature maximization of stable but potentially weak and/or incomplete preferences. Nearly 60% of all subjects' are well-explained by one of these models, typically with a unique model-optimal preference relation per subject. Importantly, revealed preferences typically have a non-trivial indifference part that, on average, accounts for up to 19% of all possible comparisons. In addition, 22% of all subjects are best explained by models of incomplete-preference maximization and reveal preferences that typically exhibit the distinctions between indifference and indecisiveness that these models afford or predict. These distinctions are documented empirically for the first time.