用大模型代理复现人类决策实验，发现其思维模式影响策略选择。
Reproducing and Extending Experiments in Behavioral Strategy with Large Language Models
- 用大模型生成代理复现人类实验室实验行为
- 代理决策与人类高度相似，且前瞻思维促进收益最大化
- 适合研究认知过程与策略演化的人类行为学者
本研究提出将大语言模型（LLM）代理作为行为策略研究的新方法，补充仿真与实验室实验，以深化对决策中认知过程的理解。我们利用LLM生成的代理复现了一项人类实验室实验，并分析其行为与真实人类的对比。结果表明，LLM代理能有效复现搜索行为和决策模式，与人类表现相当。进一步分析代理的模拟“思考”过程发现，更具前瞻性的思考更倾向于选择收益最大化策略，即更偏好利用而非探索。该方法为行为策略研究提供了新范式，并揭示了其潜在局限。
原文摘要 · Abstract (English)
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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