Abstract
This paper discusses the Bayesian updating rules of ordinary and Jeffrey conditionalisation. Their justification has been a topic of interest for the last quarter century, and several strategies proposed. None has been accepted as conclusive, and it is argued here that this is for a good reason; for by extending the domain of the probability function to include propositions describing the agent's present and future degrees of belief one can systematically generate a class of counterexamples to the rules. Dynamic Dutch Book and other arguments for them are examined critically. A concluding discussion attempts to put these results in perspective within the Bayesian approach.
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This paper was completed with assistance from a Mind Association Fellowship. I would like to thank the Mind Association, and also Paul Castell for his patient advice on previous drafts.
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Howson, C. Bayesian rules of updating. Erkenntnis 45, 195–208 (1996). https://doi.org/10.1007/BF00276790
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DOI: https://doi.org/10.1007/BF00276790