Gradient-Based Surveys for Nonparametric Discrete Choice Experiments

This paper introduces Gradient-based Survey (GBS), a novel method for designing products based on consumer preferences. Unlike traditional approaches, GBS adaptively generates paired comparison questions for consumers using gradient-based machine learning, eliminating the need for a predefined utility model. This allows GBS to effectively handle products with numerous attributes and to personalize designs for diverse consumers. Simulations demonstrate that GBS offers improved accuracy and efficiency compared to existing parametric and nonparametric techniques. The methodology bridges machine learning and experiment design, offering a scalable and robust solution for product optimization and individualized policy learning.