使用大型语言模型来分类战略情况和解读人类行为的动机
Yutong Xie1, Qiaozhu Mei1, Walter Yuan2
1School of Information, University of Michigan, Ann Arbor, MI 48109.
概括
研究人员使用大型语言模型来了解经济游戏中的人类行为. 分析提示揭示了动机,并对不同人群的行为差异进行了分类.
科学领域:
- 行为经济学
- 人工智能
- 计算社会科学
背景情况:
- 了解人类在经济游戏中的决策至关重要.
- 传统方法通常依赖于自我报告的数据或观察到的行为.
- 大型语言模型 (LLM) 为模拟和分析行为提供了一种新的方法.
研究的目的:
- 探索LLM在经济游戏中诱导和分析人类行为的使用.
- 使用快速工程推断人类行为背后的动机的方法.
- 将战略情况分类并比较不同人群的行为倾向.
主要方法:
- 对一个大型语言模型的系统变化提示.
- 在经典经济游戏的背景下分析引起的行为.
- 根据即时响应模式对战略情景进行分类的框架.
主要成果:
- 基于快速变化的经济游戏中,LLM可以模拟广泛的人类行为.
- 快速分析提供了对认知过程和潜在的驱动行为的洞察.
- 该方法允许分类不同的战略情况和人口层面的行为差异.
结论:
- 使用LLM即时工程提供了一种非标准但有效的解读人类动机的方法.
- 这种方法有助于更深入地了解行为经济学和战略决策.
- 对于不同群体的行为倾向进行比较分析,LLM可以作为一种有价值的工具.
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