Reproducing and Extending Experiments in Behavioral Strategy with Large Language Models

• URL: http://arxiv.org/abs/2410.06932v1
• Date: Wed, 9 Oct 2024 14:26:20 GMT
• Title: Reproducing and Extending Experiments in Behavioral Strategy with Large Language Models
• Authors: Daniel Albert, Stephan Billinger,
• Abstract summary: We reproduce a human laboratory experiment in behavioral strategy using large language model (LLM) generated agents. Our results show that LLM agents effectively reproduce search behavior and decision-making comparable to humans.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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