期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 116, 期 28, 页码 13903-13908出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1821028116
关键词
exploration; generalization; reinforcement learning; decision making
资金
- Harvard Data Science Initiative
- Office of Naval Research [N000141712984]
- U.S. Department of Defense (DOD) [N000141712984] Funding Source: U.S. Department of Defense (DOD)
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.
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