David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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Erkenntnis 45 (2-3):229 - 252 (1996)
An important problem in inductive probability theory is the design of exchangeable analogical methods, i.e., of exchangeable inductive methods that take into account certain considerations of analogy by similarity for predictive inferences. Here a precise reformulation of the problem of predictive analogy is given and a new family of exchangeable analogical methods is introduced.Firstly, it is proved that the exchangeable analogical method introduced by Skyrms (1993) does not satisfy the best known general principles of predictive analogy. Secondly, Skyrms's approach — consisting of the usage of particular hyper-Carnapian methods, i.e., mixtures of Carnapian inductive methods — is adopted in the design of a new family of exchangeable analogical methods. Lastly, it is proved that such methods satisfy an interesting general principle of predictive analogy.
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Citations of this work BETA
J. B. Paris & P. Waterhouse (2009). Atom Exchangeability and Instantial Relevance. Journal of Philosophical Logic 38 (3):313 - 332.
Alexandra Hill & Jeffrey Bruce Paris (2013). The Counterpart Principle of Analogical Support by Structural Similarity. Erkenntnis:1-16.
A. Hill & J. B. Paris (2013). An Analogy Principle in Inductive Logic. Annals of Pure and Applied Logic 164 (12):1293-1321.
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