Discrete Choice

Discrete choice modeling aims to understand and predict how individuals select from a set of alternatives, a fundamental problem across diverse fields like transportation, marketing, and economics. Current research emphasizes improving model accuracy and interpretability using advanced machine learning techniques, such as neural networks, gradient boosting, and kernel methods, often incorporating large language models or computer vision to handle complex data types. These advancements are enhancing the predictive power and explanatory capabilities of discrete choice models, leading to more effective policy decisions and improved understanding of human behavior in various contexts.