直接推理和概率论的归纳法。

Jon Williamson
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引用次数: 0

摘要

舒尔茨(2019,第4章)认为归纳法的概率论论述是失败的。他特别批评了那些诉诸直接推理原则的概率归纳法,包括主观贝叶斯方法(如豪森,2000)和客观贝叶斯方法(如威廉姆森,2017)。在本文中,我认为舒尔茨偏好的直接推理原则,即莱辛巴赫的最窄参考类原则,在标准概率论环境中面临着巨大的问题。此外,主要的替代直接推理原则,即刘易斯的主要原则,也很难与标准概率论相协调。因此,我认为,标准概率论方法不能诉诸直接推论来解释归纳逻辑。然而,我接着为一种非标准的客观贝叶斯归纳法辩护:我认为,这种方法既能容纳直接推论,又能为归纳逻辑提供可行的解释。然后,我将针对舒尔茨的批评为这一解释进行辩护。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Direct Inference and Probabilistic Accounts of Induction.

Direct Inference and Probabilistic Accounts of Induction.

Schurz (2019, ch. 4) argues that probabilistic accounts of induction fail. In particular, he criticises probabilistic accounts of induction that appeal to direct inference principles, including subjective Bayesian approaches (e.g., Howson 2000) and objective Bayesian approaches (see, e.g., Williamson 2017). In this paper, I argue that Schurz' preferred direct inference principle, namely Reichenbach's Principle of the Narrowest Reference Class, faces formidable problems in a standard probabilistic setting. Furthermore, the main alternative direct inference principle, Lewis' Principal Principle, is also hard to reconcile with standard probabilism. So, I argue, standard probabilistic approaches cannot appeal to direct inference to explicate the logic of induction. However, I go on to defend a non-standard objective Bayesian account of induction: I argue that this approach can both accommodate direct inference and provide a viable account of the logic of induction. I then defend this account against Schurz' criticisms.

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