PhilSci Archive

Learning from Conditionals

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

This is the latest version of this item.

[img]
Preview
Text
LFC Mind 20_03_19.pdf

Download (420kB) | Preview

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 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.


Export/Citation: EndNote | BibTeX | Dublin Core | ASCII/Text Citation (Chicago) | HTML Citation | OpenURL
Social Networking:
Share |

Item Type: Preprint
Creators:
CreatorsEmailORCID
Eva, Benjaminbenedgareva@icloud.com
Hartmann, Stephans.hartmann@lmu.de0000-0001-8676-2177
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: 20 Mar 2019 15:30
Last Modified: 12 Jul 2024 16:59
Item ID: 15835
Subjects: General Issues > Confirmation/Induction
Specific Sciences > Probability/Statistics
Date: 20 March 2019
URI: https://philsci-archive.pitt.edu/id/eprint/15835

Available Versions of this Item

Monthly Views for the past 3 years

Monthly Downloads for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item