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Resolving the Bayesian Problem of Idealization

Jones, Nicholaos (2006) Resolving the Bayesian Problem of Idealization. In: [2007] LSE-Pitt Conference: Confirmation, Induction and Science (London, 8 - 10 March, 2007).

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    Abstract

    In "Bayesian Confirmation of Theories that Incorporate Idealizations", Michael Shaffer argues that, in order to show how idealized hypotheses can be confirmed, Bayesians must develop a coherent proposal for how to assign prior probabilities to counterfactual conditionals. This paper develops a Bayesian reply to Shaffer's challenge that avoids the issue of how to assign prior probabilities to counterfactuals by treating idealized hypotheses as abstract descriptions. The reply allows Bayesians to assign non-zero degrees of confirmation to idealized hypotheses and to capture the intuition that less idealized hypotheses tend to be better confirmed than their more idealized counterparts.


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    Item Type: Conference or Workshop Item (UNSPECIFIED)
    Keywords: abstraction, Bayesian, confirmation, idealization
    Subjects: General Issues > Confirmation/Induction
    General Issues > Models and Idealization
    Conferences and Volumes: [2007] LSE-Pitt Conference: Confirmation, Induction and Science (London, 8 - 10 March, 2007)
    Depositing User: Nicholaos Jones
    Date Deposited: 18 Dec 2006
    Last Modified: 07 Oct 2010 11:14
    Item ID: 3101
    URI: http://philsci-archive.pitt.edu/id/eprint/3101

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