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对贴现模型选择方法的分析:评估贴现模型的概括性
Jordan D Bailey1, Mark J Rzeszutek2, Mikhail N Koffarnus2
1Exponent, Inc.
Journal of the experimental analysis of behavior
|April 25, 2025
概括
这项研究比较了选择折扣模型的方法,发现Akaike信息标准 (AIC),贝叶斯信息标准 (BIC) 和leave-one-out交叉验证 (LOOCV) 一般确定了主观价值折扣的正确功能形式.
科学领域:
- 行为经济学是一种行为经济学.
- 决策科学 决策科学 决策科学
- 心理物理学的精神物理.
背景情况:
- 主观价值折扣,即结果价值如何随时间,概率或努力而变化,是心理学和经济学的一个关键领域.
- 这种折扣的精确数学形式一直在争论中,提出了各种模型.
- 将这些折扣模型进行比较,就方法,数据要求和评估指标提出了挑战.
研究的目的:
- 复制和扩展对折扣模型选择方法的研究.
- 模拟来自五种已建立的折扣模型的数据:马祖尔超标,拉赫林超标,迈尔森-格林超标,萨尔森指数和β-delta.
- 在不同的数据密度下评估模型性能,并使用不同的建模方法评估概括性.
主要方法:
- 来自五个函数形式的模拟折扣数据 (马祖尔的过度波,拉切林的过度波,迈尔森-格林的过度波,萨尔森的指数,β-delta).
- 操纵了数据点密度,并采用了两种建模技术.
- 评估模型概括使用未包含在配件中的数据.
- 在模型比较中使用了Akaike信息标准 (AIC),贝叶斯信息标准 (BIC) 和leave-one-out交叉验证 (LOOCV).
主要成果:
- 一般来说,AIC,BIC和LOOCV成功地选择了正确的折扣模式.
- 在应用多级建模时,Rachlin模型在LOOCV折叠中展示了最低的误差.
- 模型性能因数据密度和使用的特定建模方法而有所不同.
结论:
- 标准模型选择标准 (AIC,BIC,LOOCV) 对于识别折扣函数是有效的.
- 使用LOOCV的多级建模显示出在折扣研究中对稳健的模型评估的承诺.
- 对数据密度和建模方法的相互作用进行进一步的研究是必要的,以准确评估主观价值.
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