Consensus in Preference‐Based Conflict Situations

ABSTRACT This article follows a preference‐based conflict situations model that extends the rough set approach to agents' preferences on issues gathered via pair‐wise comparisons. The study focuses on defining and examining various types of consensus within this model, addressing strict and more relaxed disagreements. The consensus problem is then explored at the coalition level. The key properties and quantitative measures for evaluating consensus quality are discussed. Finally, the negotiation process is investigated. The first approach is based on the expanding range of preferences derived from different definitions of consensus, and the second is focusing on issues position adjustment in order‐based conflict situation. Using examples, including a medical case with multiple specialists, the paper demonstrates the practical utility of the methods. In general, this work improves the understanding of preference‐based conflicts and the possibility of consensus, contributing to more effective decision‐making processes.

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Publication Details

Journal
International Journal for Numerical Methods in Engineering
Published
2025-09-18
DOI
https://doi.org/10.1002/nme.70134
Citations
1
Primary Topic
Bayesian Modeling and Causal Inference
Type
article
Field-Weighted Citation Impact
2.48
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article

Consensus in Preference‐Based Conflict Situations

Małgorzata Przybyła‐Kasperek, Rafał Deja
1 citations
International Journal for Numerical Methods in Engineering
Bayesian Modeling and Causal Inference
2.48
article

Consensus in Preference‐Based Conflict Situations

Małgorzata Przybyła‐Kasperek, Rafał Deja
article en
1 citations

Abstract

ABSTRACT This article follows a preference‐based conflict situations model that extends the rough set approach to agents' preferences on issues gathered via pair‐wise comparisons. The study focuses on defining and examining various types of consensus within this model, addressing strict and more relaxed disagreements. The consensus problem is then explored at the coalition level. The key properties and quantitative measures for evaluating consensus quality are discussed. Finally, the negotiation process is investigated. The first approach is based on the expanding range of preferences derived from different definitions of consensus, and the second is focusing on issues position adjustment in order‐based conflict situation. Using examples, including a medical case with multiple specialists, the paper demonstrates the practical utility of the methods. In general, this work improves the understanding of preference‐based conflicts and the possibility of consensus, contributing to more effective decision‐making processes.

International Journal for Numerical Methods in EngineeringVol. 126(18)
Constantine the Philosopher University in Nitra (SK), WSB University (PL), Institute of Occupational Medicine and Environmental Health (PL)
Openalex Percentile: Top 8%
Bayesian Modeling and Causal Inference
2.48
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