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Exploring variability in risk taking with large language models.
1Department of Psychology, University of Pennsylvania.
This study uses large language models (LLMs) to understand why people take risks differently. LLMs quantify risky behaviors and individual preferences, offering a new decision-theoretic basis for psychological research.
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Area of Science:
- Cognitive Psychology
- Decision Science
- Artificial Intelligence
Background:
- Individual differences in risk-taking are typically studied using psychometric methods analyzing survey data.
- Existing methods correlate behaviors and individuals but lack a deeper explanation of underlying preferences.
Purpose of the Study:
- To identify sources of individual differences in risk-taking behavior.
- To determine how these differences vary across different decision domains and situations.
- To provide a decision-theoretic foundation for understanding psychometric findings.
Main Methods:
- Utilized large language models (LLMs) to quantify everyday risky behaviors by their descriptive attributes or reasons.
- Employed decision models to link these attributes and reasons to individual participant responses.
- Analyzed correlations between behaviors and individuals based on elicited reasons and individual weighting of reasons.
Main Results:
- LLM-based decision models successfully explained correlations between different behaviors by identifying shared reasons.
- These models also explained correlations between individuals by revealing how they weigh specific reasons.
- Demonstrated accurate out-of-sample prediction for numerous everyday behaviors.
Conclusions:
- The proposed approach offers a decision-theoretic framework for psychometric findings in risk-taking research.
- LLMs can generate quantitative representations of decisions, enabling prediction and interpretation of behavioral heterogeneity.
- This methodology has significant theoretical and practical implications for studying individual differences in behavior.