Structured, uncertainty-driven exploration in real-world consumer choice

Significance We study how people make choices among a large number of options when they have limited experience. In a large dataset of online food delivery purchases, we find evidence for sophisticated exploration strategies predicted by contemporary theories. People actively seek to reduce their uncertainty about restaurants and use similarity-based generalization to guide their selections. Our findings suggest that theories of exploratory choice have real-world validity. Making good decisions requires people to appropriately explore their available options and generalize what they have learned. While computational models can explain exploratory behavior in constrained laboratory tasks, it is unclear to what extent these models generalize to real-world choice problems. We investigate the factors guiding exploratory behavior in a dataset consisting of 195,333 customers placing 1,613,967 orders from a large online food delivery service. We find important hallmarks of adaptive exploration and generalization, which we analyze using computational models. In particular, customers seem to engage in uncertainty-directed exploration and use feature-based generalization to guide their exploration. Our results provide evidence that people use sophisticated strategies to explore complex, real-world environments.