A Bayesian INLA Framework for Modeling Dependence in Best-Worst Discrete Choice Experiments

Thomas M. Menino Convention & Exhibition Center 

Analyzing highly dependent best-worst (BW) choice pairs in discrete choice experiments presents a significant challenge in complex, context-dependent settings, particularly when comparing alternative strategies and latent utilities over time. We develop a Bayesian framework for modeling dependent BW choice data in which outcomes are represented as directed transitions between latent preference states. Transition counts are modeled using a Poisson log-linear specification with pair-specific random effects to capture heterogeneity and cross-alternative dependence. Bayesian inference is conducted using Integrated Nested Laplace Approximation (INLA), enabling scalable and computationally efficient estimation without reliance on Markov chain Monte Carlo. The approach is evaluated through simulation studies and a quality-of-life case study inspired by Flynn et al., and is bench-marked against copula-based transition models. Results demonstrate improved model fit and stability, as assessed by DIC and WAIC, while providing robust and interpretable estimates of latent utilities across a range of sample sizes.

Best-Worst Scaling

Discrete Choice Experiments

Bayesian INLA

Utility Modeling 

Section on Bayesian Statistical Science