Developing improved metamodels by combining phenomenological reasoning with statistical methods
Developing improved metamodels by combining phenomenological reasoning with statistical methods
复制标题
通过将现象学推理与统计方法相结合来开发改进的元模型
DOI：
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发表时间：
2002
期刊：
影响因子：
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通讯作者：
P. Davis
中科院分区：
文献类型：
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作者：
J. Bigelow;P. Davis
元模型是相对较小的简单模型，它近似于大型复杂模型的行为。开发元模型的一种常见且表面上有吸引力的方法是从大量大型模型运行中生成数据，然后使用现成的统计方法，而无需尝试了解模型的内部工作原理。这篇文章描述了为什么在某些问题上，通过使用各种现象学知识来提高这种元模型的质量是重要的和富有成效的。这些好处有时在数学上是微妙的，但在战略上很重要，比如当你处理的系统可能会在几个关键组件中的任何一个发生故障时发生故障。朴素的元模型可能无法反映这些组成部分的个别关键程度，因此，如果用于政策分析，可能会非常具有误导性。朴素的元模型也可能在投入的相对重要性上给出极具误导性的结果，从而扭曲资源分配决策。然而，通过加入适当剂量的理论，这样的问题可以大大减轻。我们的工作旨在促进对多分辨率、多视角建模(MRMPM)的新理解，以及对将统计方法的优点与更多基于理论的工作的优点相结合的跨学科工作的贡献。虽然我们提出的分析是基于一个特定的大型和复杂模型的特定实验，但我们认为其洞察力更一般。
A metamodel is relatively small, simple model that approximates the behavior of a large, complex model. A common and superficially attractive way to develop a metamodel is to generate data from a number of large-model runs and to then use off-the-shelf statistical methods without attempting to understand the models internal workings. This paper describes research illuminating why it is important and fruitful, in some problems, to improve the quality of such metamodels by using various types of phenomenological knowledge. The benefits are sometimes mathematically subtle, but strategically important, as when one is dealing with a system that could fail if any of several critical components fail. Naive metamodels may fail to reflect the individual criticality of such components and may therefore be quite misleading if used for policy analysis. Na*ve metamodeling may also give very misleading results on the relative importance of inputs, thereby skewing resource-allocation decisions. By inserting an appropriate dose of theory, however, such problems can be greatly mitigated. Our work is intended to be a contribution to the emerging understanding of multiresolution, multiperspective modeling (MRMPM), as well as a contribution to interdisciplinary work combining virtues of statistical methodology with virtues of more theory-based work. Although the analysis we present is based on a particular experiment with a particular large and complex model, we believe that the insights are more general.