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This paper critiques the pessimistic meta-inductive argument against scientific realism by focusing on the limitations of its inductive reasoning rather than its historical context. By leveraging insights from frequentist statistics and machine learning, the author demonstrates that while ordinary enumerative induction can achieve convergence to the truth, meta-induction does not even reach almost everywhere convergence. The findings reveal a fundamental limitation in meta-induction, suggesting that no inference method can guarantee almost everywhere convergence in the contexts where meta-induction is typically applied.
Meta-induction fails to achieve even almost everywhere convergence, challenging its validity in scientific inference.
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.
