Nonparametric Discrete Choice Experiments with Machine Learning Guided Adaptive Design
This addresses the need for scalable and robust product design for businesses, though it appears incremental as it builds on existing nonparametric methods with adaptive improvements.
The paper tackles the problem of designing products to meet consumer preferences by proposing the Gradient-based Survey (GBS), a nonparametric discrete choice experiment that adaptively constructs paired comparison questions based on previous choices, and demonstrates its advantage in accuracy and sample efficiency over existing methods in simulations.
Designing products to meet consumers' preferences is essential for a business's success. We propose the Gradient-based Survey (GBS), a discrete choice experiment for multiattribute product design. The experiment elicits consumer preferences through a sequence of paired comparisons for partial profiles. GBS adaptively constructs paired comparison questions based on the respondents' previous choices. Unlike the traditional random utility maximization paradigm, GBS is robust to model misspecification by not requiring a parametric utility model. Cross-pollinating the machine learning and experiment design, GBS is scalable to products with hundreds of attributes and can design personalized products for heterogeneous consumers. We demonstrate the advantage of GBS in accuracy and sample efficiency compared to the existing parametric and nonparametric methods in simulations.