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The Meta-Inductive Justification of Induction

Sterkenburg, Tom F. (2019) The Meta-Inductive Justification of Induction. Episteme.

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Abstract

I evaluate Schurz's proposed meta-inductive justification of induction, a refinement of Reichenbach's pragmatic justification that rests on results from the machine learning branch of prediction with expert advice.

My conclusion is that the argument, suitably explicated, comes remarkably close to its grand aim: an actual justification of induction. This finding, however, is subject to two main qualifications, and still disregards one important challenge.

The first qualification concerns the empirical success of induction. Even though, I argue, Schurz's argument does not need to spell out what inductive method actually consists in, it does need to postulate that there is something like the inductive or scientific prediction strategy that has so far been *significantly* more successful than alternative approaches. The second qualification concerns the difference between having a justification for inductive method and for sticking with induction *for now*. Schurz's argument can only provide the latter. Finally, the remaining challenge concerns the pool of alternative strategies, and the relevant notion of a meta-inductivist's optimality that features in the analytical step of Schurz's argument. Building on the work done here, I will argue in a follow-up paper that the argument needs a stronger *dynamic* notion of a meta-inductivist's optimality.


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Item Type: Published Article or Volume
Creators:
CreatorsEmailORCID
Sterkenburg, Tom F.tom.sterkenburg@lmu.de0000-0002-4860-727X
Subjects: Specific Sciences > Computer Science
General Issues > Confirmation/Induction
Depositing User: Mr Tom Sterkenburg
Date Deposited: 16 May 2019 14:01
Last Modified: 16 May 2019 14:01
Item ID: 16008
Journal or Publication Title: Episteme
DOI or Unique Handle: 10.1017/epi.2018.52
Subjects: Specific Sciences > Computer Science
General Issues > Confirmation/Induction
Date: 2019
URI: http://philsci-archive.pitt.edu/id/eprint/16008

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