The Plausibility-Informativeness Theory

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New Waves in Epistemology, Page: 164-191

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Huber, Franz
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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). We first present the general plausibility-informativeness theory of theory evaluation. In a nutshell, the message is (1) that there are two epistemic 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 evaluated; and (3) that in evaluating 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 difference in plausibility is small enough. Particular accounts of this general theory arise by inserting particular strength and truth indicators. The theory is spelt out for the Bayesian paradigm; it is then compared with incremental Bayesian confirmation theory. The first part closes by discussing a few epistemic problems in the philosophy of science 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). The second part discusses the question of justification any theory of theory evaluation has to face: Why should one stick to theories with high values rather than to any other theories? The answer given by the Bayesian version of the account presented in the first part is that one should accept theories given high values, because, in the medium run, theory evaluation almost surely takes one to the most informative among all true theories when presented separating data. The comparison between the present account and incremental Bayesian confirmation theory is continued.