发表机构
University of California, Davis（加利福尼亚大学戴维斯分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对反对科学实在论的悲观元归纳论证，通过动摇其归纳步骤提出新挑战，基于频率统计学等领域的认识论，论证普通枚举归纳可处处收敛而元归纳无法，且该情境下无推理方法能实现几乎处处收敛。
AI 中文摘要
本文通过动摇悲观元归纳论证的归纳步骤而非其历史前提，对反对科学实在论的悲观元归纳论证发起挑战。尽管已有相关挑战存在，本文仍提出一种新的挑战。基于频率统计学、机器学习及形式认识论中发展出的通用科学推理认识论，本文从向真理收敛的角度评估归纳。本文主张，普通枚举归纳可实现处处收敛，而元归纳甚至无法实现几乎处处收敛。实际上，在元归纳产生的问题情境中，失败更为严重：任何推理方法都无法实现几乎处处收敛。
英文摘要
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.
