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Probabilistic Judgement Aggregation by Opinion Update

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Modeling Decisions for Artificial Intelligence (MDAI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13408))

Abstract

We consider a situation where agents are updating their probabilistic opinions on a set of issues with respect to the confidence they have in each other’s judgements. We adapt the framework for reaching a consensus introduced in [2] and modified in [1] to our case of uncertain probabilistic judgements on logically related issues. We discuss possible alternative solutions for the instances where the requirements for reaching a consensus are not satisfied.

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Notes

  1. 1.

    In this paper we interpret likelihood as probability and we use the two terms interchangeably. Note that, however, likelihood can also be interpreted as another measure of belief, see [5].

  2. 2.

    Expressions containing all the other types of inequalities or equality can be defined as abbreviations.

  3. 3.

    To ensure that every \(\varphi ^M\) is measurable, we may take \(F=2^W.\)

References

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Author information

Authors and Affiliations

  1. BI Norwegian Business School, Oslo, Norway

    Magdalena Ivanovska

  2. University of Bergen, Bergen, Norway

    Marija Slavkovik

Authors
  1. Magdalena Ivanovska
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  2. Marija Slavkovik
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Corresponding author

Correspondence to Magdalena Ivanovska .

Editor information

Editors and Affiliations

  1. Umeå University, Umeå, Sweden

    Vicenç Torra

  2. Tamagawa University, Tokyo, Japan

    Yasuo Narukawa

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Ivanovska, M., Slavkovik, M. (2022). Probabilistic Judgement Aggregation by Opinion Update. In: Torra, V., Narukawa, Y. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2022. Lecture Notes in Computer Science(), vol 13408. Springer, Cham. https://doi.org/10.1007/978-3-031-13448-7_3

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  • DOI: https://doi.org/10.1007/978-3-031-13448-7_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13447-0

  • Online ISBN: 978-3-031-13448-7

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