Posterior inference of attitude-behaviour relationships using latent class choice models

2025-09-10

AI Summary

Traditional hybrid choice models face challenges including high complexity and poor convergence when analyzing attitude–behavior relationships. To address these issues, this study proposes a posterior inference framework based on the Latent Class Choice Model (LCCM), which abandons full integration in favor of class-specific attitude profiling to capture preference heterogeneity. The framework combines posterior analysis of indicator means with a score-based multinomial logit model to ensure transparent, interpretable inference. Compared to factor-analytic and full-information hybrid models, the proposed approach substantially enhances interpretability and robustness while imposing minimal computational burden. Empirical applications—examining preferences for remote work and vaccine acceptance—demonstrate that the framework delivers deep behavioral insights with low modeling complexity. Overall, it establishes a simpler, more reliable paradigm for attitude-driven behavioral modeling.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Knowledge Representation and Reasoning: Preferences
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The link between attitudes and behaviour has been a key topic in choice modelling for two decades, with the widespread application of ever more complex hybrid choice models. This paper proposes a flexible and transparent alternative framework for empirically examining the relationship between attitudes and behaviours using latent class choice models (LCCMs). Rather than embedding attitudinal constructs within the structural model, as in hybrid choice frameworks, we recover class-specific attitudinal profiles through posterior inference. This approach enables analysts to explore attitude-behaviour associations without the complexity and convergence issues often associated with integrated estimation. Two case studies are used to demonstrate the framework: one on employee preferences for working from home, and another on public acceptance of COVID-19 vaccines. Across both studies, we compare posterior profiling of indicator means, fractional multinomial logit (FMNL) models, factor-based representations, and hybrid specifications. We find that posterior inference methods provide behaviourally rich insights with minimal additional complexity, while factor-based models risk discarding key attitudinal information, and fullinformation hybrid models offer little gain in explanatory power and incur substantially greater estimation burden. Our findings suggest that when the goal is to explain preference heterogeneity, posterior inference offers a practical alternative to hybrid models, one that retains interpretability and robustness without sacrificing behavioural depth.

Problem

• Investigating attitude-behaviour relationships in choice modelling
• Proposing latent class models for posterior inference of attitudes
• Comparing alternative methods to reduce estimation complexity

Innovation

• Uses latent class choice models for posterior inference
• Recovers class-specific attitudinal profiles from data
• Avoids complexity of integrated hybrid model estimation