Theory and Decision 85 (1):21-39 (2018)

Authors
Denis Bonnay
Université Paris Nanterre
Mikael Cozic
Université Paris-Est Créteil
Abstract
Jeffrey conditioning tells an agent how to update her priors so as to grant a given probability to a particular event. Weighted averaging tells an agent how to update her priors on the basis of testimonial evidence, by changing to a weighted arithmetic mean of her priors and another agent’s priors. We show that, in their respective settings, these two seemingly so different updating rules are axiomatized by essentially the same invariance condition. As a by-product, this sheds new light on the question how weighted averaging should be extended to deal with cases when the other agent reveals only parts of her probability distribution. The combination of weighted averaging and Jeffrey conditioning is a comprehensive updating rule to deal with such cases, which is again axiomatized by invariance under embedding. We conclude that, even though one may dislike Jeffrey conditioning or weighted averaging, the two make a natural pair when a policy for partial testimonial evidence is needed.
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DOI 10.1007/s11238-017-9639-3
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References found in this work BETA

Conditionalization and Observation.Paul Teller - 1973 - Synthese 26 (2):218-258.
The Logic of Decision.Henry E. Kyberg - 1968 - Philosophical Review 77 (2):250.
Updating Subjective Probability.Persi Diaconis & Sandy L. Zabell - 1982 - Journal of the American Statistical Association 77 (380):822-830.

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Citations of this work BETA

Support for Geometric Pooling.Jean Baccelli & Rush T. Stewart - forthcoming - Review of Symbolic Logic:1-40.

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