aiqbench analysis
This paper offers a rigorous theoretical critique of pessimistic meta-induction by contrasting it with frequentist statistics and machine learning convergence guarantees, providing essential epistemological context for the foundations of scientific inference in AI. By demonstrating that no inference method achieves almost everywhere convergence within specific problem contexts, the work addresses core questions about truth-seeking mechanisms central to advancing reliable ML systems.
Pessimistic Meta-Induction and Its Limits: Lessons from Frequentist Statistics and Machine Learning Theory
arXiv:2608.17213v1 Announce Type: new Abstract: 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.
Abstract: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.
Machine Learning (cs.LG); Methodology (stat.ME)
arXiv:2608.17213 [cs.LG]
(or arXiv:2608.17213v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.17213 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 190 words
