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Assessing Theories, Bayes Style.

Huber, Franz (2008) Assessing Theories, Bayes Style. Synthese, 161 (1). pp. 89-118.

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Abstract

The problem addressed in this paper is ``the main epistemic problem concerning science'', viz. ``the explication of how we compare and evaluate theories [...] in the light of the available evidence'' (van Fraassen 1983: 27).
Sections 1-3 contain the general plausibility-informativeness theory of theory assessment. In a nutshell, the message is (1) that there are two values a theory should exhibit: truth and informativeness -- measured respectively by a truth indicator and a strength indicator; (2) that these two values are conflicting in the sense that the former is a decreasing and the latter an increasing function of the logical strength of the theory to be assessed; and (3) that in assessing a given theory by the available data one should weigh between these two conflicting aspects in such a way that any surplus in informativeness succeeds, if the shortfall in plausibility is small enough.
Particular accounts of this general theory arise by inserting particular strength indicators and truth indicators. In section 4 the theory is spelt out for the Bayesian paradigm of subjective probabilities. It is then compared to incremental Bayesian confirmation theory. Section 4 closes by asking whether it is likely to be lovely. Section 5 discusses a few problems of confirmation theory in the light of the present approach. In particular, it is briefly indicated how the present account gives rise to a new analysis of Hempel's conditions of adequacy for any relation of confirmation (Hempel 1945), differing from the one Carnap gave in §87 of his (1962).
Section 6 addresses the question of justification any theory of theory assessment has to face: why should one stick to theories given high assessment values rather than to any other theories? The answer given by the Bayesian version of the account presented in section 4 is that one should accept theories given high assessment values, because, in the medium run, theory assessment almost surely takes one to the most informative among all true theories when presented separating data. The concluding section 8 continues the comparison between the present account and incremental Bayesian confirmation theory.


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Item Type: Published Article or Volume
Creators:
CreatorsEmailORCID
Huber, Franzfranz.huber@utoronto.ca
Subjects: General Issues > Confirmation/Induction
General Issues > Decision Theory
General Issues > Formal Learning Theory
Specific Sciences > Probability/Statistics
General Issues > Theory Change
Depositing User: Unnamed user with email franz.huber@utoronto.ca
Date Deposited: 09 Jul 2014 14:38
Last Modified: 09 Jul 2014 14:38
Item ID: 10853
Journal or Publication Title: Synthese
Publisher: Springer
Official URL: http://link.springer.com/article/10.1007%2Fs11229-...
Subjects: General Issues > Confirmation/Induction
General Issues > Decision Theory
General Issues > Formal Learning Theory
Specific Sciences > Probability/Statistics
General Issues > Theory Change
Date: 2008
Page Range: pp. 89-118
Volume: 161
Number: 1
URI: https://philsci-archive-dev.library.pitt.edu/id/eprint/10853

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