Nonparametric Discrete Choice Experiments with Machine Learning Guided
  Adaptive Design
Mingzhang Yin Ruijiang Gao Weiran Lin Steven M. Shugan
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.