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Four Approaches to Supposition

Eva, Benjamin and Shear, Ted and Fitelson, Branden (2020) Four Approaches to Supposition. [Preprint]

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Abstract

Suppositions can be introduced in either the indicative and subjunctive mood. The introduction of either type of supposition initiates judgments that may be either qualitative, binary judgments about whether a given proposition is acceptable or quantitative, numerical ones about how acceptable it is. As such, accounts of qualitative/quantitative judgment under indicative/subjunctive supposition have been developed in the literature. We explore these four different types of theories by systematically explicating the relationships canonical representatives of each. Our representative qualitative accounts of indicative and subjunctive supposition are based on the belief change operations provided by AGM revision and KM update respectively; our representative quantitative ones are offered by conditionalization and imaging. This choice is motivated by the familiar approach of understanding supposition as 'provisional belief revision' wherein one temporarily treats the supposition as true and forms judgments by making appropriate changes to their other opinions. To compare the numerical judgments recommended by the quantitative theories with the binary ones recommended by the qualitative accounts, we rely on a suitably adapted version of the Lockean thesis. Ultimately, we establish a number of new results that we interpret as vindicating the often-repeated claim that conditionalization is a probabilistic version of revision, while imaging is a probabilistic version of update.


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Item Type: Preprint
Creators:
CreatorsEmailORCID
Eva, Benjaminbenedgareva@icloud.com
Shear, Tedshear.ted@tedshear.org0000-0003-0404-0942
Fitelson, Brandenbranden@fitelson.org
Keywords: imaging, conditionalization, AGM revision, KM update, belief revision, supposition, indicative, subjunctive
Subjects: Specific Sciences > Computer Science
Specific Sciences > Artificial Intelligence
General Issues > Decision Theory
Specific Sciences > Probability/Statistics
Depositing User: Dr Ted Shear
Date Deposited: 22 Nov 2020 04:04
Last Modified: 22 Nov 2020 04:04
Item ID: 18412
Subjects: Specific Sciences > Computer Science
Specific Sciences > Artificial Intelligence
General Issues > Decision Theory
Specific Sciences > Probability/Statistics
Date: 28 November 2020
URI: https://philsci-archive-dev.library.pitt.edu/id/eprint/18412

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