Can the Best-Alternative Justification Solve Hume's Problem? On the Limits of a Promising Approach

Arnold, Eckhart

doi:10.1086/656010

Cited by 9 publications

(7 citation statements)

References 3 publications

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“…Again, it would seem that there is an obvious pool in the context of Schurz's argument, namely the pool of the scientific strategy and its proposed alternatives. This necessarily being a finite pool of strategies, it would also render Schurz's argument immune against the charge of Arnold (2010) that it is not generally possible to have optimal strategies for infinite pools. However, as I will argue in a subsequent paper (Sterkenburg 2019), it is not clear that the relevant optimality suffices for the analytic component (A) of the argument.…”

Section: Resultsmentioning

confidence: 99%

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The Meta-Inductive Justification of Induction

Sterkenburg¹

2019

Episteme

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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 beensignificantlymore successful than alternative approaches. The second qualification concerns the difference between having a justification for inductive method and for sticking with inductionfor 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 analytic step of Schurz's argument. Building on the work done here, I will argue in a follow-up paper that the argument needs a strongerdynamicnotion of a meta-inductivist's optimality.

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Section: Resultsmentioning

confidence: 99%

“…But, of course, there is the concern that this framework of prediction falls short of capturing the essence of inductive inference, and so a justification within this framework falls short of the real goal: to justify inductive or scientific reasoning (cf. Arnold 2010: 591).…”

Section: The Framework Of Predictionmentioning

confidence: 99%

The Meta-Inductive Justification of Induction

Sterkenburg¹

2019

Episteme

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“…The account of meta-induction was developed within the domain of epistemology as a means of addressing Hume's problem of induction (Schurz 2008(Schurz , 2009Arnold 2010;Vickers 2010, §6.3), utilizing results from the area of machine learning (CesaBianchi and Lugosi 2006). Meta-inductive methods have also been applied in the area of social epistemology (Schurz 2012;Thorn and Schurz 2012;Feldbacher 2012).…”

Section: Are There Optimal Selection Strategies? a Challenge For Ecolmentioning

confidence: 99%

The Revenge of Ecological Rationality: Strategy-Selection by Meta-Induction Within Changing Environments

Schurz

Thorn

2015

Minds & Machines

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According to the paradigm of adaptive rationality, successful inference and prediction methods tend to be local and frugal. As a complement to work within this paradigm, we investigate the problem of selecting an optimal combination of prediction methods from a given toolbox of such local methods, in the context of changing environments. These selection methods are called meta-inductive (MI) strategies, if they are based on the success-records of the toolbox-methods. No absolutely optimal MI strategy exists-a fact that we call the ''revenge of ecological rationality''. Nevertheless one can show that a certain MI strategy exists, called ''AW'', which is universally long-run optimal, with provably small short-run losses, in comparison to any set of prediction methods that it can use as input. We call this property universal access-optimality. Local and short-run improvements over AW are possible, but only at the cost of forfeiting universal access-optimality. The last part of the paper includes an empirical study of MI strategies in application to an 8-year-long data set from the Monash University Footy Tipping Competition.

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“…I start with the objection due to Arnold (2010) that the optimality results that Schurz relies on are restricted to finite pools of strategies (sect. 4).…”

Section: Introductionmentioning

confidence: 99%