Heterogeneity in choice experiment data: A Bayesian investigation

Abstract

Discrete mixture (DM) models recognize the presence of heterogeneity across individuals in a given population. In the context of a public land use discrete choice experiment, we use DM models to allow for respondent behavior to probabilistically mix over multiple competing process heuristics. We pairwise combine the Random Utility Model (RUM), Contextual Concavity Model (CCM), and Random Regret Minimization (RRM) heuristic into three DM models, in which the probability of an individual adhering to a particular heuristic is modeled as a function of sociodemographic characteristics. We present a comprehensive Bayesian analysis for which we explicitly describe prior selection, inferential procedures, and model comparison metrics. We use a fully Bayesian information criterion to rank the models. We find evidence that responses are best modeled using random regret. After accounting for preference heterogeneity, the DM models estimate two latent groups of decision makers. For the DM models, we develop a novel algorithm to calculate posterior-weighted willingness to pay estimates for improvements in different public park amenities in Polk County, Iowa.