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首页> 外文期刊>The Open Statistics & Probability Journal >A Model Must Be Wrong to be Useful: The Role of Linear Modeling and False Assumptions in Theoretical Explanation
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A Model Must Be Wrong to be Useful: The Role of Linear Modeling and False Assumptions in Theoretical Explanation

机译：必须有用的模型才对：线性建模和错误假设在理论解释中的作用

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It is true that many times relationships in the real world do not fall into a linear pattern. Nevertheless, even if the true causal structure of the phenomenon under study is not linear, it does not mean that the causal relationship cannot be detected using linear modeling. With the advanced use of non-linear modeling, especially in the field of business data mining, researchers feel the tension of choosing between linear and nonlinear models. It is the conviction of the author that the appropriateness of non-linear modeling and linear modeling depends on specific research purposes (prediction vs. explanation). While nonlinear models are suitable to illustrate a physical or mechanical process under a natural interpretation, researchers occasionally have to go beyond the natural interpretation to look for theoretical explanations of the relationships between attributes. Judging the efficacy of statistical modeling, which is essentially a scientific method, should be based upon the criteria developed throughout the history of science, rather than through observations from the business market and political events within one or two decades. Examples from the history of science, including Dalton's atomic model, Galileo's law of uniform acceleration, and the Titius-Bode Law, will be cited to illustrate the usefulness of linear models in terms of providing explanation with theoretical depth. It is not the case that explanatory models are still useful in spite of the fact that they are wrong to some degree. On the contrary, they are useful because they are wrong.
机译：的确，现实世界中的许多次关系并没有陷入线性模式。但是，即使所研究现象的真正因果结构不是线性的，也并不意味着不能使用线性建模来检测因果关系。随着非线性建模的高级应用，特别是在业务数据挖掘领域，研究人员感到在线性模型和非线性模型之间进行选择的压力。作者相信，非线性建模和线性建模的适当性取决于特定的研究目的（预测与解释）。尽管非线性模型适合于用自然解释来说明物理或机械过程，但研究人员有时不得不超越自然解释来寻找属性之间关系的理论解释。从本质上说，从本质上讲是一种科学方法，对统计建模的有效性的判断应该基于整个科学史上制定的标准，而不是通过一，二十年内对商业市场和政治事件的观察得出。将引用科学史上的例子，包括道尔顿的原子模型，伽利略均匀加速定律和蒂蒂斯-鲍德定律，以说明线性模型在提供理论深度的解释方面的有用性。尽管说明模型在某种程度上是错误的，但并非并非如此。相反，它们是有用的，因为它们是错误的。

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