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

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Main Author: Gerasimou, Georgios
Format: Preprint
Published: 2021
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author Gerasimou, Georgios
author_facet Gerasimou, Georgios
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2111_14431
institution arXiv
publishDate 2021
record_format arxiv
spellingShingle Eliciting and Distinguishing Between Weak and Incomplete Preferences: Theory, Experiment and Computation
Gerasimou, Georgios
Theoretical Economics
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.
title Eliciting and Distinguishing Between Weak and Incomplete Preferences: Theory, Experiment and Computation
topic Theoretical Economics
url https://arxiv.org/abs/2111.14431