Synthese 200 (1):1-26 (2022)

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Abstract
This paper proposes a framework for representing in Bayesian terms the idea that analogical arguments of various degrees of strength may provide inductive support to yet untested scientific hypotheses. On this account, contextual information plays a crucial role in determining whether, and to what extent, a given similarity or dissimilarity between source and target may confirm an empirical hypothesis over a rival one. In addition to showing confirmation by analogy compatible with the adoption of a Bayesian standpoint, the proposal outlined in this paper reveals a close agreement between the fulfillment of Hesse’s criteria for analogical arguments capable of inductive support and the attribution of confirmatory power by the lights of Bayesian confirmation theory. In this sense, the Bayesian representation not only enriches a framework, Hesse’s, of enduring relevance for understanding scientific activity, but may offer something akin to a proof of concept of it.
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DOI 10.1007/s11229-022-03545-w
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References found in this work BETA

Logical Foundations of Probability.Rudolf Carnap - 1950 - Chicago, IL, USA: Chicago University of Chicago Press.
Theory and Evidence.Clark N. Glymour - 1980 - Princeton University Press.
Models and Analogies in Science.Mary B. Hesse - 1963 - University of Notre Dame Press.
Bayesian Epistemology.Luc Bovens & Stephan Hartmann - 2003 - Oxford: Oxford University Press.
Models and Analogies in Science.Mary Hesse - 1965 - British Journal for the Philosophy of Science 16 (62):161-163.

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