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Updated: Sep 9, 2025
03:14
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
Published on: December 6, 2024
681
Using large language models to categorize strategic situations and decipher motivations behind human behaviors
Yutong Xie1, Qiaozhu Mei1, Walter Yuan2
1School of Information, University of Michigan, Ann Arbor, MI 48109.
Summary
Researchers used large language models to understand human behavior in economic games. Analyzing prompts reveals motivations and categorizes behavioral differences across populations.
Area of Science:
- Behavioral Economics
- Artificial Intelligence
- Computational Social Science
Background:
- Understanding human decision-making in economic games is crucial.
- Traditional methods often rely on self-reported data or observed actions.
- Large language models (LLMs) offer a novel approach to simulating and analyzing behavior.
Purpose of the Study:
- To explore the use of LLMs for eliciting and analyzing human behaviors in economic games.
- To develop a method for inferring motivations behind human actions using prompt engineering.
- To categorize strategic situations and compare behavioral tendencies across different populations.
Main Methods:
- Systematically varying prompts given to a large language model.
- Analyzing the elicited behaviors within the context of classic economic games.
- Developing a framework for categorizing strategic scenarios based on prompt-response patterns.
Main Results:
- LLMs can simulate a wide spectrum of human behaviors in economic games based on prompt variations.
- Prompt analysis provides insights into the cognitive processes and potential motivations driving behavior.
- The method allows for the classification of distinct strategic situations and population-level behavioral differences.
Conclusions:
- Prompt engineering with LLMs offers a nonstandard yet effective method for deciphering human motivations.
- This approach facilitates a deeper understanding of behavioral economics and strategic decision-making.
- LLMs can serve as a valuable tool for comparative analysis of behavioral tendencies in diverse groups.
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