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Eliciting and Distinguishing Between Weak and Incomplete Preferences: Theory, Experiment and Computation

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 primary novelty consisted of allowing all subjects to select multiple alternatives at each menu. Based on a non-parametric goodness-of-fit criterion that we introduce, which generalizes intuitively a widely used pre-existing method to environments of multi-valued choices, each subjects' decision data 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, preferences usually (80%) had a non-trivial indifference part and, where applicable, a clearly distinct indecisiveness part. The achieved uncoupling of revealed indifference and indecisiveness is documented empirically for the first time.

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