Reducing model complexity for explanation and prediction
Reducing model complexity for explanation and prediction
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DOI：
10.1016/j.geomorph.2006.10.020
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发表时间：
2007-10-15
期刊：
影响因子：
3.9
通讯作者：
Murray, A. Brad
中科院分区：
文献类型：
--
作者：
Murray, A. Brad
数值模型可用于解释不甚了解的现象或进行可靠的定量预测。在对多尺度系统建模时，“自上而下”的方法——基于紧急变量和相互作用的模型，而不是明确地基于产生它们的更快、更小的尺度过程——有利于实现这两个目标。表示紧急交互的参数化范围从高度简化和抽象到更精确的定量。基于经验的大尺度参数化比小尺度过程的参数化更可靠地得到精确的大尺度行为。相反，有目的地简化模型交互的表示可以增强模型的解释效用，澄清导致神秘行为的关键反馈。为了使这些潜在的见解具有相关性，模型中的交互需要以某种直接的方式与“真实”系统中的交互相对应。这种对应关系通常适用于为预测目的而构建的模型，尽管这不是必需的。激励建模努力的目标有助于确定最合适的建模策略，以及判断模型有用性的最合适标准。(c) 2007 Elsevier B.V.版权所有
Numerical models can be useful for explaining poorly understood phenomena or for reliable quantitative predictions. When modeling a multi-scale system, a 'top-down' approach-basing models on emergent variables and interactions, rather than explicitly on the much faster and smaller scale processes that give rise to them-facilitates both goals. Parameterizations representing emergent interactions range from highly simplified and abstracted to more quantitatively accurate. Empirically based large-scale parameterizations lead more reliably to accurate large-scale behavior than do parameterizations of much smaller scale processes. Conversely, purposefully simplified representations of model interactions can enhance a model's utility for explanation, clarifying the key feedbacks leading to an enigmatic behavior. For such potential insights to be relevant, the interactions in the model need to correspond to those in the 'real' system in some straightforward way. Such a correspondence usually holds for models constructed for predictive purposes, although this is not a requirement. The goals motivating a modeling endeavor help determine the most appropriate modeling strategies, as well as the most appropriate criteria for judging model usefulness. (c) 2007 Elsevier B.V. All rights reserved.