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Model-Selection Theory: The Need for a More Nuanced Picture of Use-Novelty and Double-Counting
The British Journal for the Philosophy of Science ( IF 3.4 ) Pub Date : 2018-06-01 , DOI: 10.1093/bjps/axw024
Katie Steele 1 , Charlotte Werndl 2
Affiliation  

This article argues that common intuitions regarding (a) the specialness of ‘use-novel’ data for confirmation and (b) that this specialness implies the ‘no-double-counting rule’, which says that data used in ‘constructing’ (calibrating) a model cannot also play a role in confirming the model’s predictions, are too crude. The intuitions in question are pertinent in all the sciences, but we appeal to a climate science case study to illustrate what is at stake. Our strategy is to analyse the intuitive claims in light of prominent accounts of confirmation of model predictions. We show that on the Bayesian account of confirmation, and also on the standard classical hypothesis-testing account, claims (a) and (b) are not generally true; but for some select cases, it is possible to distinguish data used for calibration from use-novel data, where only the latter confirm. The more specialized classical model-selection methods, on the other hand, uphold a nuanced version of claim (a), but this comes apart from (b), which must be rejected in favour of a more refined account of the relationship between calibration and confirmation. Thus, depending on the framework of confirmation, either the scope or the simplicity of the intuitive position must be revised. 1 Introduction 2 A Climate Case Study 3 The Bayesian Method vis-à-vis Intuitions 4 Classical Tests vis-à-vis Intuitions 5 Classical Model-Selection Methods vis-à-vis Intuitions 5.1 Introducing classical model-selection methods 5.2 Two cases 6 Re-examining Our Case Study 7 Conclusion 1 Introduction 2 A Climate Case Study 3 The Bayesian Method vis-à-vis Intuitions 4 Classical Tests vis-à-vis Intuitions 5 Classical Model-Selection Methods vis-à-vis Intuitions 5.1 Introducing classical model-selection methods 5.2 Two cases 5.1 Introducing classical model-selection methods 5.2 Two cases 6 Re-examining Our Case Study 7 Conclusion

中文翻译:

模型选择理论:需要更细致的使用新颖性和重复计算图片

本文认为,关于 (a) 用于确认的“使用新颖”数据的特殊性以及 (b) 这种特殊性意味着“不重复计算规则”的常见直觉,即“构建”(校准)中使用的数据) 模型也不能在确认模型的预测方面发挥作用,太粗糙了。有问题的直觉在所有科学中都是相关的,但我们呼吁气候科学案例研究来说明什么是利害攸关的。我们的策略是根据模型预测确认的突出说明来分析直观的主张。我们表明,在贝叶斯确认的解释上,以及在标准的经典假设检验解释上,断言 (a) 和 (b) 通常不正确;但对于某些特定情况,可以将用于校准的数据与使用新数据区分开来,只有后者确认。另一方面,更专业的经典模型选择方法支持权利要求 (a) 的细微版本,但这与 (b) 不同,后者必须被拒绝,以支持更精细地解释校准和模型之间的关系。确认。因此,根据确认的框架,必须修改直观位置的范围或简单性。1 介绍 2 气候案例研究 3 贝叶斯方法与直觉 4 经典测试与直觉 5 经典模型选择方法与直觉 5.1 介绍经典模型选择方法 5.
更新日期:2018-06-01
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