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Preprint
Reproducing and Extending Experiments in Behavioral Strategy with Large Language Models
ArXiv.org
09 Oct 2024
Abstract
In this study, we propose LLM agents as a novel approach in behavioral strategy research, complementing simulations and laboratory experiments to advance our understanding of cognitive processes in decision-making. Specifically, we reproduce a human laboratory experiment in behavioral strategy using large language model (LLM) generated agents and investigate how LLM agents compare to observed human behavior. Our results show that LLM agents effectively reproduce search behavior and decision-making comparable to humans. Extending our experiment, we analyze LLM agents' simulated "thoughts," discovering that more forward-looking thoughts correlate with favoring exploitation over exploration to maximize wealth. We show how this new approach can be leveraged in behavioral strategy research and address limitations.
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Details
- Title
- Reproducing and Extending Experiments in Behavioral Strategy with Large Language Models
- Creators
- Daniel Albert - Drexel UniversityStephan Billinger - University of Southern Denmark
- Publication Details
- ArXiv.org
- Number of pages
- 38
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Management
- Other Identifier
- 991021910584804721