Dan Goldstein, Microsoft Research

Abstract

We propose and test a method for out-of-population prediction termed model-assisted judgmental bootstrapping, which leverages a predictive model from one domain combined with expert judgment to generate training data and subsequently a predictive model for a new domain. In a preregistered experiment (=1440), we assessed the predictive accuracy of this method in increasingly challenging environments. We also analyzed the individual contributions of two techniques that underlie the method: model-assisted estimation and judgmental bootstrapping. Our findings revealed that both techniques significantly improved predictive accuracy. Furthermore, their impacts were complementary: model-assisted estimation provided the largest accuracy gains in the least demanding environment, while judgmental bootstrapping did so in the most challenging environment. Our results suggest that model-assisted judgmental bootstrapping is a promising technique for creating predictive models in domains in which outcome data are not available.

Bio: Daniel G. Goldstein is a Senior Principal Research Manager at Microsoft Research in New York City. Dan's research focuses on decision making in business, economics, and statistics, drawing on methods from computer science and cognitive psychology. Before joining Microsoft, he held research and professorial roles at the Max Planck Institute, Columbia University, London Business School, and Yahoo Research. Dan has authored award-winning research on forecasting and served as president of the Society for Judgment and Decision Making.