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Identifying resource-rational heuristics for risky choice
Paul M Krueger1, Frederick Callaway2, Sayan Gul3
1Department of Computer Science, Princeton University.
Psychological Review
|April 18, 2024
Summary
People often use simple decision-making strategies (heuristics) due to limited cognitive resources. Machine learning can discover optimal heuristics, improving understanding of human decision-making in complex environments.
Area of Science:
- Cognitive Science
- Machine Learning
- Behavioral Economics
Background:
- Human decision-making is constrained by limited computational resources.
- Reliance on heuristics is common, but discovering and predicting their use is challenging.
Purpose of the Study:
- To develop a machine learning framework for automatically deriving optimal decision heuristics.
- To investigate how people adapt their strategies in complex choice environments.
Main Methods:
- Utilized machine learning to derive heuristics considering cognitive resource limitations.
- Conducted a large-scale behavioral experiment comparing discovered heuristics with human choices across diverse environments.
Main Results:
- The method rediscovered known heuristics and identified novel ones.
- Human decision strategies adapt to environmental structures but don't always fully exploit them.
Conclusions:
- Machine learning offers a powerful approach to discovering optimal heuristics for bounded rationality.
- People generally employ effective, resource-conscious strategies, though optimization can be improved.
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