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Symmetry and the Brown-Freiling Refutation of the Continuum Hypothesis Bartha, Paul
Abstract
Freiling [1] and Brown [2] have put forward a probabilistic reductio argument intended to refute the Continuum Hypothesis. The argument relies heavily upon intuitions about symmetry in a particular scenario. This paper argues that the argument fails, but is still of interest for two reasons. First, the failure is unusual in that the symmetry intuitions are demonstrably coherent, even though other constraints make it impossible to find a probability model for the scenario. Second, the best probability models have properties analogous to non-conglomerability, motivating a proposed extension of that concept (and corresponding limits on Bayesian conditionalization).
Item Metadata
Title |
Symmetry and the Brown-Freiling Refutation of the Continuum Hypothesis
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Creator | |
Publisher |
Multidisciplinary Digital Publishing Institute
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Date Issued |
2011-09-06
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Description |
Freiling [1] and Brown [2] have put forward a probabilistic reductio argument intended to refute the Continuum Hypothesis. The argument relies heavily upon intuitions about symmetry in a particular scenario. This paper argues that the argument fails, but is still of interest for two reasons. First, the failure is unusual in that the symmetry intuitions are demonstrably coherent, even though other constraints make it impossible to find a probability model for the scenario. Second, the best probability models have properties analogous to non-conglomerability, motivating a proposed extension of that concept (and corresponding limits on Bayesian conditionalization).
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Subject | |
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Type | |
Language |
eng
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Date Available |
2019-04-04
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Provider |
Vancouver : University of British Columbia Library
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Rights |
CC BY 3.0
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DOI |
10.14288/1.0377812
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URI | |
Affiliation | |
Citation |
Symmetry 3 (3): 636-652 (2011)
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Publisher DOI |
10.3390/sym3030636
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Peer Review Status |
Reviewed
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Scholarly Level |
Faculty
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Rights URI | |
Aggregated Source Repository |
DSpace
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Item Media
Item Citations and Data
Rights
CC BY 3.0