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Study of Dependency Degree and Bayesian Networks for Conflict Scenarios

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Rough Sets (IJCRS 2024)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14839))

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Abstract

The paper delves into analyzing conflict scenarios seen by attribute dependency, emphasizing the interaction between individual attributes. The paper presents two distinct methodologies: attributes dependency degree and Bayesian Networks for attributes. The study investigates the scope to which attributes influence one another within the context of conflict. Through empirical analysis of two real-world cases, the research uncovers significant attributes whose values offer crucial insights into the compatibility or divergence of numerous other attributes. Both methods illuminate attribute connections by employing legible graphical representations, facilitating a deeper understanding of the conflict situation. The findings highlight attributes that emerge as pivotal, providing valuable guidance for negotiation in complex conflict situations.

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Author information

Authors and Affiliations

  1. Institute of Computer Science, University of Silesia, ul. Bedzinska 39, Sosnowiec, Poland

    Małgorzata Przybyła-Kasperek

  2. Department of Computer Science, WSB University, ul. Cieplaka 1c, Da̧browa Górnicza, Poland

    Rafał Deja

Authors
  1. Małgorzata Przybyła-Kasperek
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  2. Rafał Deja
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Corresponding author

Correspondence to Rafał Deja .

Editor information

Editors and Affiliations

  1. Saint Mary's University, Halifax, NS, Canada

    Mengjun Hu

  2. Ghent University, Ghent, Belgium

    Chris Cornelis

  3. California State University, San Bernardino, CA, USA

    Yan Zhang

  4. Saint Mary's University, Halifax, NS, Canada

    Pawan Lingras

  5. University of Warsaw, Warsaw, Poland

    Dominik Ślęzak

  6. University of Regina, Regina, SK, Canada

    JingTao Yao

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Przybyła-Kasperek, M., Deja, R. (2024). Study of Dependency Degree and Bayesian Networks for Conflict Scenarios. In: Hu, M., Cornelis, C., Zhang, Y., Lingras, P., Ślęzak, D., Yao, J. (eds) Rough Sets. IJCRS 2024. Lecture Notes in Computer Science(), vol 14839. Springer, Cham. https://doi.org/10.1007/978-3-031-65665-1_7

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  • DOI: https://doi.org/10.1007/978-3-031-65665-1_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-65664-4

  • Online ISBN: 978-3-031-65665-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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