Causal basis for probabilistic belief change

distance vs. closeness

Seemran Mishra, Abhaya Nayak*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

1 Citation (Scopus)

Abstract

In probabilistic accounts of belief change, traditionally Bayesian conditioning is employed when the received information is consistent with the current knowledge, and imaging is used otherwise. It is well recognised that imaging can be used even if the received information is consistent with the current knowledge. Imaging assumes, inter alia, a relational measure of similarity among worlds. In a recent work, Rens and Meyer have argued that when, in light of new evidence, we no longer consider a world ω to be a serious possibility, worlds more similar to it should be considered relatively less plausible, and hence more dissimilar (distant) a world is from ω, the larger should be its share in the original probability mass of ω. In this paper we argue that this approach leads to results that revolt against our causal intuition, and propose a converse account where a larger share of ω’s mass move to worlds that are more similar (closer) to it instead.

Original languageEnglish
Title of host publicationMulti-disciplinary Trends in Artificial Intelligence - 10th International Workshop, MIWAI 2016, Proceedings
EditorsChattrakul Sombattheera, Frieder Stolzenburg, Fangzhen Lin, Abhaya Nayak
Place of PublicationCham, Switzerland
PublisherSpringer, Springer Nature
Pages112-125
Number of pages14
Volume10053 LNAI
ISBN (Print)9783319493961
DOIs
Publication statusPublished - 2016
Event10th Multi-Disciplinary International Workshop on Artificial Intelligence, MIWAI 2016 - Chiang Mai, Thailand
Duration: 7 Dec 20169 Dec 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10053 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other10th Multi-Disciplinary International Workshop on Artificial Intelligence, MIWAI 2016
CountryThailand
CityChiang Mai
Period7/12/169/12/16

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Mishra, S., & Nayak, A. (2016). Causal basis for probabilistic belief change: distance vs. closeness. In C. Sombattheera, F. Stolzenburg, F. Lin, & A. Nayak (Eds.), Multi-disciplinary Trends in Artificial Intelligence - 10th International Workshop, MIWAI 2016, Proceedings (Vol. 10053 LNAI, pp. 112-125). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10053 LNAI). Cham, Switzerland: Springer, Springer Nature. https://doi.org/10.1007/978-3-319-49397-8_10