100K选择困境的计算分析:决策属性,权衡结构和基于模型的预测
Sudeep Bhatia1, Simon T van Baal2, Feiyi Wang1
1Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104.
概括
本研究引入了大量现实生活选择困境的数据集,并使用大型语言模型 (LLM) 来分析决策属性. 士学位分析准确地预测人类的选择,为复杂的决策提供了洞察力.
科学领域:
- 计算社会科学 计算社会科学
- 行为经济学是一种行为经济学.
- 心理学 心理学 心理学
背景情况:
- 现实生活中的选择困境是复杂的,很难在规模上研究.
- 现有的方法很难捕捉影响人类决策的细微属性.
研究的目的:
- 使用大语言模型 (LLM) 创建一个大规模的选择困境数据集并提取关键决策属性.
- 为选择属性开发一个代表性空间,以量化权衡和上下文变化.
- 评估LLM衍生属性的预测准确性,以建模人类决策.
主要方法:
- 从社交媒体和调查中收集了超过10万份关于选择困境的文字描述.
- 采用大型语言模型 (LLM) 来提取数百个选择属性并将它们映射到一个共同的表示空间中.
- 通过将其对人类选择的预测与已建立的决策模型和控制变量进行比较,验证了LLM管道.
主要成果:
- 开发了一个新的代表性空间,量化了生活选择中的主题和权衡.
- 证明了LLM管道,与决策模型集成,准确预测人类的选择.
- 与仅依赖于非结构化文本,人口统计数据或人格特征的模型相比,展示了优异的预测性能.
结论:
- 基于LLM的大规模结构提取可以有效地模拟复杂的人类行为和决策.
- 该研究提供了关于现实生活选择背后的属性,结果和目标的重要见解.
- 这种方法为在行为研究中将计算方法与科学理论相结合提供了一个强大的新工具.
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