在GPT的经济理性的出现
Yiting Chen1, Tracy Xiao Liu2, You Shan3
1Department of Economics, Lingnan University, Hong Kong, China HKG.
概括
大型语言模型 (LLM) 在预算决策中表现出显著的经济理性,表现优于人类受试者. 它们的决策一致性表明了超越语言处理的复杂任务能力的潜力.
科学领域:
- 行为经济学是一种行为经济学.
- 人工智能的人工智能
- 认知科学 认知科学
背景情况:
- 大型语言模型 (LLM) 越来越多地集成到各种应用中.
- 评估超越自然语言处理的LLM能力至关重要.
- 了解人工智能的经济决策是一个新兴的研究领域.
研究的目的:
- 评估大型语言模型 (GPT) 的经济合理性.
- 为了比较LLM与人类受试者的决策一致性.
- 探索影响LLM经济理性的因素.
主要方法:
- 总理被指示在风险,时间,社会和食物偏好领域做出预算决策.
- 经济理性是使用显露偏好理论和效用最大化原则来衡量的.
- 在并行实验和现有文献中,GPT的性能与人类受试者进行了比较.
主要成果:
- 在所有测试领域中,GPT表现出了实质性的经济理性.
- 与人类参与者相比,GPT获得了更高的理性得分.
- 对GPT的估计偏好参数与人类略有不同,异质性较低.
- 理性评分对随机性和人口因素具有强大影响,但对上下文框架敏感.
结论:
- 在经济背景下,LLM显示出合理决策的潜力.
- GPT的决策与效用最大化原则保持一致.
- 需要进一步的研究来了解LLM的能力,局限性和决策机制.
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