在经济决策的机器学习理论中的建模数据集偏差
Tobias Thomas1,2, Dominik Straub3, Fabian Tatai3
1Centre for Cognitive Science and Institute of Psychology, Technical University of Darmstadt, Darmstadt, Germany. tobias.thomas@tu-darmstadt.de.
Nature human behaviour
|January 12, 2024
概括
研究人员在人类决策模型中发现了数据集偏差. 在实验室数据上训练的结构化决策噪音模型表现最好,这表明仔细的数据分析是理解风险选择的关键.
科学领域:
- 认知科学 认知科学
- 计算神经科学是一种神经科学.
- 行为经济学是一种行为经济学.
背景情况:
- 规范和描述模型传统上解释了人类在风险下做出决策.
- 最近的一项研究提出了一个新的,准确的模型,用于人类决策使用神经网络和一个大数据集 (选择13k).
研究的目的:
- 使用机器学习系统地分析决策模型和数据集之间的关系.
- 调查潜在的数据集偏差影响模型准确性预测人类风险选择.
主要方法:
- 采用机器学习技术分析多个模型和数据集.
- 通过检查在选择13k数据集中的随机主导博中的选择来调查数据集偏差.
- 开发并测试了一种包含结构化决策噪声的概率生成模型.
主要成果:
- 发现了数据集偏差的证据,特别是博的选择13k数据集中对设备引用的偏差.
- 一个概率生成模型,增强了结构化决策噪声,并对实验室数据进行了训练,证明了卓越的性能.
- 这种噪声增强模型的表现优于所有其他模型,除了那些直接在choices13k数据集上训练的模型.
结论:
- 选择13k数据集表现出偏差,可能是由于决策噪音增加.
- 理论见解和严格的数据分析的结合对于理解人类的风险选择至关重要.
- 在开发和评估决策的计算模型时,需要仔细考虑数据集特征.
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