Modeling preference heterogeneity using model-based decision trees. (March 2023)

• Record Type: Journal Article
• Authors: Gutiérrez-Vargas, Álvaro A.; Meulders, Michel; Vandebroek, Martina
• Is Part Of: Journal of choice modelling. Volume 46(2023)
• Publication Date: 2023-03
• Subjects: Discrete choice models -- Mixed logit -- Decision tree -- Preference heterogeneity -- Recursive partitioning -- Machine learning -- Simulated maximum likelihood -- Latent class models -- Allocation model; Decision making -- Periodicals; Social choice -- Periodicals; Decision making; Social choice; Periodicals; 302.13
• DOI: 10.1016/j.jocm.2022.100393
• Languages: English
• ISSNs: 1755-5345

Abstract

Abstract: This article investigates the usage of a general model-based recursive partitioning algorithm to model preference heterogeneity. We use the algorithm to grow a decision tree based on statistical tests of the stability of individuals' preference parameters. In particular, we used a Mixed Logit (MIXL) model with alternative-specific attributes at the end leaves of the tree while using individual characteristics as partition variables. This configuration allows us to search for instabilities of the taste parameters across individuals' characteristics. We conduct a simulation study to investigate the algorithm's ability to recover different data generating processes with structural breaks in the taste parameters. The results show that the algorithm can correctly recover diverse tree-like data generating processes. Additionally, we applied the algorithm to stated choice data of the preferences for the environmental impact of (hypothetical) energy generation plans in Chile. The results show that the model-based decision tree fits the data better than MIXL in terms of information criteria. Moreover, we show that the derived tree structure depends on the assumptions on the parameters' distributions. Additionally, we compare the model-based decision tree model with Latent Class (LC) models with and without within-class heterogeneity. Finally, we show that the recursive partitioning algorithm can inform the selection of variables to be included in the LC allocation models. Highlights: We apply the Model-Based recursive (MOB) algorithm to model preference heterogeneity. We grow a Decision Tree using a Mixed Logit model (MOB-MIXL) at the end leaves. Simulations show that the MOB-MIXL algorithm can recover different tree structures. We use the MOB algorithm to identify relevant variables for Latent Class models. We analyze preferences for environmental impact using the MOB algorithm.