Hybrid choice models are advanced econometric tools used to capture the decision-making processes of individuals by integrating both observable and unobservable factors. Traditional choice models, such as discrete choice models, rely solely on observable variables and choices made by individuals to infer preferences and predict future behavior. Hybrid choice models enrich this framework by incorporating latent variables, which are psychological constructs like attitudes, perceptions, and preferences that influence decision-making but they are not directly measurable.[1] This integration allows hybrid choice models to provide a deeper understanding of choice behavior, recognizing that decisions are not only influenced by tangible variables such as price and quality but also by subjective factors like trust, satisfaction, and risk aversion.[2]
The application of hybrid choice models spans various fields, including transportation, marketing, and environmental economics, where understanding the complexity of human behavior is crucial. For instance, in transportation planning, hybrid choice models can model how travelers' attitudes toward convenience, environmental concerns, and safety affect their choice of transport modes.[3][4][5] By capturing these latent variables through surveys or inferred through observable indicators, researchers and policymakers can design more effective interventions and policies. Hybrid choice models also offer the advantage of improved prediction accuracy and policy relevance, as they can simulate how changes in both observable and latent factors might influence future choices, thus providing a comprehensive framework for understanding and influencing behavior.
Applications
Hybrid choice models are versatile tools used to integrate both observable and unobservable factors influencing decision-making. Here are some types of applications for hybrid choice models:
