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Using large-scale experiments and machine learning to discover theories of human decision-making
Joshua C Peterson1, David D Bourgin2, Mayank Agrawal3,4
1Department of Computer Science, Princeton University, Princeton, NJ 08540, USA. joshuacp@princeton.edu.
Science (New York, N.Y.)
|June 11, 2021
Summary
Researchers developed a new machine-learning approach to model human decision-making. This method uses large datasets to create interpretable psychological theories, leading to a more accurate model of risky choice behavior.
Area of Science:
- Cognitive psychology
- Computational neuroscience
- Behavioral economics
Background:
- Understanding human decision-making is crucial across social sciences and engineering.
- Quantitative models are key to predicting choices, but existing theories have limitations.
Purpose of the Study:
- To accelerate progress in understanding human decision-making.
- To develop interpretable psychological theories using machine learning.
- To discover a more accurate model of risky choice behavior.
Main Methods:
- Conducted the largest experiment on risky choice to date.
- Utilized large datasets to power machine-learning algorithms.
- Employed gradient-based optimization of differentiable decision theories via artificial neural networks.
Main Results:
- Successfully recapitulated historical discoveries in decision-making research.
- Demonstrated that existing theories can be improved upon.
- Discovered a novel, more accurate model of human decision-making.
Conclusions:
- Machine learning, constrained by interpretability, can advance psychological theory.
- The new model offers improved accuracy while retaining insights from established theories.
- This approach facilitates a deeper understanding of risky choice and human behavior.
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