Authors
Jon Williamson
University of Kent
Abstract
Objective Bayesianism has been criticised for not allowing learning from experience: it is claimed that an agent must give degree of belief ½ to the next raven being black, however many other black ravens have been observed. I argue that this objection can be overcome by appealing to objective Bayesian nets, a formalism for representing objective Bayesian degrees of belief. Under this account, previous observations exert an inductive influence on the next observation. I show how this approach can be used to capture the Johnson-Carnap continuum of inductive methods, as well as the Nix-Paris continuum, and show how inductive influence can be measured
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DOI 10.1093/bjps/axm032
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References found in this work BETA

Bayes or Bust?John Earman - 1992 - Bradford.
The Continuum of Inductive Methods.Rudolf Carnap - 1952 - Chicago, IL, USA: University of Chicago Press.

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Atom Exchangeability and Instantial Relevance.J. B. Paris & P. Waterhouse - 2009 - Journal of Philosophical Logic 38 (3):313-332.

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