
Abstract
It is well known that aggregating the degree-of-belief functions of different subjects by linear pooling or averaging is subject to a commutativity dilemma: other than in trivial cases, conditionalizing the individual degree-of-belief functions on a piece of evidence E followed by linearly aggregating them does not yield the same result as rst aggregating them linearly and then conditionalizing the resulting social degree- of-belief function on E. In the present paper we suggest a novel way out of this dilemma: adapting the method of update or learning such that linear pooling com- mutes with it. As it turns out, the resulting update scheme – (general) imaging on the evidence – is well-known from areas such as the study of conditionals and cau- sal decision theory, and a formal result from which the required commutativity property is derivable was supplied already by Gärdenfors (1982) in a different con- text. We end up determining under which conditions imaging would seem to be right method of update, and under which conditions, therefore, group update would not be affected by the commutativity dilemma.
Item Type: | Journal article |
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EU Funded Grant Agreement Number: | 675415 |
EU Projects: | Horizon 2020 > Marie Skłodowska Curie Actions > Marie Skłodowska-Curie Innovative Training Networks > 675415: Diaphora: Philosophical Problems, Resilience and Persistent Disagreement |
Form of publication: | Postprint |
Faculties: | Philosophy, Philosophy of Science and Religious Science Philosophy, Philosophy of Science and Religious Science > Munich Center for Mathematical Philosophy (MCMP) Philosophy, Philosophy of Science and Religious Science > Munich Center for Mathematical Philosophy (MCMP) > Diaphora: Logic and Paradox |
Subjects: | 100 Philosophy and Psychology > 100 Philosophy |
URN: | urn:nbn:de:bvb:19-epub-40481-0 |
ISSN: | 1750-0117 |
Language: | English |
Item ID: | 40481 |
Date Deposited: | 19. Sep 2017, 06:09 |
Last Modified: | 04. Nov 2020, 13:17 |