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Learning from Conditionals

Eva, Benjamin and Hartmann, Stephan and Rafiee Rad, Soroush (2019) Learning from Conditionals. [Preprint]

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Abstract

In this article, we address a major outstanding question of probabilistic Bayesian epistemology: `How should a rational Bayesian agent update their beliefs upon learning an indicative conditional?'. A number of authors have recently contended that this question is fundamentally underdetermined by Bayesian norms, and hence that there is no single update procedure that rational agents are obliged to follow upon learning an indicative conditional. Here, we resist this trend and argue that a core set of widely accepted Bayesian norms is sufficient to uniquely identify a single rational updating procedure for this kind of learning. Along the way, we justify a privileged formalisation of the notion of `epistemic conservativity', offer a new analysis of the Judy Benjamin problem and emphasise the distinction between interpreting the content of new evidence and updating one's beliefs on the basis of that content.


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Item Type: Preprint
Creators:
CreatorsEmailORCID
Eva, Benjaminbenedgareva@icloud.com
Hartmann, StephanS.Hartmann@lmu.de
Rafiee Rad, Soroushsoroush.r.rad@gmail.com
Keywords: Bayesian Epistemology, Conditionals, Probability
Subjects: General Issues > Confirmation/Induction
Specific Sciences > Probability/Statistics
Depositing User: Dr Benjamin Eva
Date Deposited: 02 Mar 2019 01:14
Last Modified: 02 Mar 2019 01:14
Item ID: 15781
Subjects: General Issues > Confirmation/Induction
Specific Sciences > Probability/Statistics
Date: 1 March 2019
URI: http://philsci-archive.pitt.edu/id/eprint/15781

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