1. Using large-scale experiments and machine learning to discover theories of human decision-making.
- Author
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Peterson JC, Bourgin DD, Agrawal M, Reichman D, and Griffiths TL
- Subjects
- Choice Behavior, Deep Learning, Humans, Neural Networks, Computer, Probability, Decision Making, Machine Learning, Models, Psychological
- Abstract
Predicting and understanding how people make decisions has been a long-standing goal in many fields, with quantitative models of human decision-making informing research in both the social sciences and engineering. We show how progress toward this goal can be accelerated by using large datasets to power machine-learning algorithms that are constrained to produce interpretable psychological theories. Conducting the largest experiment on risky choice to date and analyzing the results using gradient-based optimization of differentiable decision theories implemented through artificial neural networks, we were able to recapitulate historical discoveries, establish that there is room to improve on existing theories, and discover a new, more accurate model of human decision-making in a form that preserves the insights from centuries of research., (Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)
- Published
- 2021
- Full Text
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