Bakshi, Kabir S. (2025) Identification and Indetermination in the Meta-inductive Approach to Induction. [Preprint]

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Abstract

The meta-inductive approach to induction justifies induction by proving its optimality. The argument for the optimality of induction proceeds in two steps. The first 'a priori' step intends to show that meta-induction is optimal and the second 'a posteriori' step intends to show that meta-induction selects object-induction in our world. I critically evaluate the second-step and raise two problems: the identification problem and the indetermination problem. In light of these problems, I assess the prospects of any meta-inductive approach to induction.

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Item Type:	Preprint
Creators:	Creators Email ORCID Bakshi, Kabir S. kabir.bakshi@pitt.edu
Keywords:	induction; optimality; meta-induction; online learning
Subjects:	Specific Sciences > Computation/Information General Issues > Confirmation/Induction General Issues > Evidence General Issues > Formal Learning Theory Specific Sciences > Artificial Intelligence > Machine Learning Specific Sciences > Probability/Statistics
Depositing User:	Kabir Bakshi
Date Deposited:	12 Aug 2025 12:54
Last Modified:	12 Aug 2025 12:54
Item ID:	26205
Subjects:	Specific Sciences > Computation/Information General Issues > Confirmation/Induction General Issues > Evidence General Issues > Formal Learning Theory Specific Sciences > Artificial Intelligence > Machine Learning Specific Sciences > Probability/Statistics
Date:	2025
URI:	https://philsci-archive.pitt.edu/id/eprint/26205

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