Reducing Simulation Input-Model Risk via Input Model Averaging
Reducing Simulation Input-Model Risk via Input Model Averaging
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DOI：
10.1287/ijoc.2020.0994
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
B. Nelson;Alan T. K. Wan;Guohua Zou;Xinyu Zhang;Xi Jiang
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文献类型：
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作者：
B. Nelson;Alan T. K. Wan;Guohua Zou;Xinyu Zhang;Xi Jiang
输入不确定性是模拟模型风险的一个方面，当驱动输入分布是从现实世界的历史数据推导或“拟合”出来时就会产生这种风险。尽管在对输入不确定性进行量化和套期保值方面已经取得了重大进展，但还没有通过更好的输入建模直接尝试减少它。“更好”的含义取决于背景和目标：我们所说的背景是指（a）存在一个或多个合理选择的参数分布族；（b）现实世界的历史数据预计不会与其中任何一个完全相符；（c）我们的主要目标是获得更高保真度的模拟输出，而不是发现“真实”分布。在本文中，我们表明频率论模型平均可以是一种创建能更好地代表真实的、未知的输入分布的输入模型的有效方法，从而降低模型风险。输入模型平均建立在标准输入建模实践的基础上，计算负担不大，不需要改变模拟的执行方式，也不需要任何后续实验，并且可以在综合R档案网络（CRAN）上获取。我们为我们的方法提供了理论和实证支持。
Input uncertainty is an aspect of simulation model risk that arises when the driving input distributions are derived or “fit” to real-world, historical data. Although there has been significant progress on quantifying and hedging against input uncertainty, there has been no direct attempt to reduce it via better input modeling. The meaning of “better” depends on the context and the objective: Our context is when (a) there are one or more families of parametric distributions that are plausible choices; (b) the real-world historical data are not expected to perfectly conform to any of them; and (c) our primary goal is to obtain higher-fidelity simulation output rather than to discover the “true” distribution. In this paper, we show that frequentist model averaging can be an effective way to create input models that better represent the true, unknown input distribution, thereby reducing model risk. Input model averaging builds from standard input modeling practice, is not computationally burdensome, requires no change in how the simulation is executed nor any follow-up experiments, and is available on the Comprehensive R Archive Network (CRAN). We provide theoretical and empirical support for our approach.