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

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 problem...

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Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS 2019-07, Vol.116 (28), p.13903-13908
Main Authors: Schulz, Eric, Bhui, Rahul, Love, Bradley C., Brier, Bastien, Todd, Michael T., Gershman, Samuel J.
Format: Article
Language:English
Subjects:
Choice Behavior - physiology
Computer applications
Computer Simulation
Consumer Behavior
Customers
Decision Making - physiology
Delivery services
Exploration
Exploratory behavior
Exploratory Behavior - physiology
Female
Generalization, Psychological
Humans
Learning - physiology
Male
Mathematical models
Reinforcement, Psychology
Social Sciences
Uncertainty
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Summary: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.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.1821028116