挑战科学实在论的元归纳悲观论，证明其推理失效。
Pessimistic Meta-Induction and Its Limits: Lessons from Frequentist Statistics and Machine Learning Theory
- 用频率统计与机器学习框架评估归纳有效性
- 普通归纳可处处收敛，元归纳连几乎处处收敛都达不到
- 在特定问题中，任何方法都难实现几乎处处收敛
本文通过削弱元归纳悲观论的归纳步骤而非历史前提，对其提出挑战。尽管已有相关质疑，本文发展出新的论证路径。基于频率统计、机器学习与形式认识论构建的一般性科学推断框架，本文以收敛到真理为标准评估归纳推理。论证指出，普通枚举归纳可实现处处收敛，而元归纳甚至无法达到几乎处处收敛。尤其在元归纳出现的问题情境下，更深层的失败在于：没有任何推理方法能够实现几乎处处收敛。
原文摘要 · Abstract (English)
This paper challenges the pessimistic meta-inductive argument against scientific realism by undermining its inductive step rather than its historical premise. Although related challenges already exist, I develop a new one. Drawing on a general epistemology of scientific inference developed in frequentist statistics, machine learning, and formal epistemology, I evaluate induction in terms of convergence to the truth. I argue that ordinary enumerative induction can achieve everywhere convergence, whereas meta-induction fails even to achieve almost everywhere convergence. Indeed, in the problem context where meta-induction arises, the failure is deeper: no inference method whatsoever achieves almost everywhere convergence.
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