To predict human choice, consider the context

Abstract

Choice prediction competitions suggest that popular models of choice, including prospect theory, have low predictive accuracy. Peterson et al. show the key problem lies in assuming each alternative is evaluated in isolation, independently of the context. This observation demonstrates how a focus on predictions can promote understanding of cognitive processes.

Keywords: BEAST; decisions under risk; decisions under uncertainty; machine learning; reliance on small samples.