Learning from Conditionals

Mind 129 (514):461-508 (2020)
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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 identify a normatively privileged updating procedure for this kind of learning. Along the way, we justify a privileged formalization of the notion of ‘epistemic conservativity’, offer a new analysis of the Judy Benjamin problem, and emphasize the distinction between interpreting the content of new evidence and updating one’s beliefs on the basis of that content.

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Author Profiles

Stephan Hartmann
Ludwig Maximilians Universität, München
Soroush Rafiee Rad
Tilburg University
Benjamin Eva
Duke University

Citations of this work

Bayesian Philosophy of Science.Jan Sprenger & Stephan Hartmann - 2019 - Oxford and New York: Oxford University Press.
Conditional Degree of Belief and Bayesian Inference.Jan Sprenger - 2020 - Philosophy of Science 87 (2):319-335.
A New Approach to Testimonial Conditionals.Stephan Hartmann & Ulrike Hahn - 2020 - In CogSci 2020 Proceedings. Toronto, Ontario, Kanada: pp. 981–986.

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References found in this work

Bayesian Epistemology.Luc Bovens & Stephan Hartmann - 2003 - Oxford: Oxford University Press. Edited by Stephan Hartmann.
Accuracy and the Laws of Credence.Richard Pettigrew - 2016 - New York, NY.: Oxford University Press UK.
Logic and Conversation.H. Paul Grice - 1975 - In Maite Ezcurdia & Robert J. Stainton (eds.), The Semantics-Pragmatics Boundary in Philosophy. Broadview Press. pp. 47.

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