, Volume 193, Issue 7, pp 2013–2034

Belief merging with the aim of truthlikeness


DOI: 10.1007/s11229-015-0825-y

Cite this article as:
D’Alfonso, S. Synthese (2016) 193: 2013. doi:10.1007/s11229-015-0825-y


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.


Belief merging Information fusion Truthlikeness  Judgment aggregation 

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.MelbourneAustralia

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