Synthese 193 (7):2013-2034 (2016)

Simon D'Alfonso
University of Melbourne (PhD)
The merging/fusion of belief/data collections in propositional logic form is a topic that has received due attention within the domains of database and AI research. A distinction can be made between two types of scenarios to which the process of merging can be applied. In the first type, the collections represent preferences, such as the voting choices of a group of people, that need to be aggregated so as to give a consistent result that in some way best represents the collective judgement of the group. In the second type, the collections represent factual data that is to be aggregated with an aim of obtaining a result that maximises factual correctness. After introducing a general framework for belief merging via some prominent literature on the topic, this paper then introduces and considers a method for belief merging with the second type of scenario in mind. Its suitability is corroborated by demonstrating how it can be seen as a special case of a merging procedure that combines aggregation of probabilities and maximisation of expected truthlikeness
Keywords Belief merging  Information fusion  Truthlikeness  Judgment aggregation
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Reprint years 2016
DOI 10.1007/s11229-015-0825-y
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References found in this work BETA

Truthlikeness.Graham Oddie - 2014 - Stanford Encyclopedia.
Truthlikeness.David Pearce - 1989 - Journal of Symbolic Logic 54 (1):297-300.
Verisimilitude: The Third Period.Ilkka Niiniluoto - 1998 - British Journal for the Philosophy of Science 49 (1):1-29.

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Citations of this work BETA

A Scientific Metaphysical Naturalisation of Information.Bruce Long - 2018 - Dissertation, University of Sydney

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