scholarly journals Fuzzy Preferences in the Graph Model for Conflict Resolution

2012 ◽  
Vol 20 (4) ◽  
pp. 760-770 ◽  
Author(s):  
M. Abul Bashar ◽  
D. Marc Kilgour ◽  
Keith W. Hipel
2017 ◽  
Vol 17 (3) ◽  
pp. 287-315 ◽  
Author(s):  
M. Abul Bashar ◽  
Keith W. Hipel ◽  
D. Marc Kilgour ◽  
Amer Obeidi

2020 ◽  
Vol 39 (5) ◽  
pp. 6721-6731
Author(s):  
Nannan Wu ◽  
Yejun Xu ◽  
Lizhong Xu ◽  
Huimin Wang

Conflict of environmental sustainable development as a common phenomenon can be seen everywhere in life. To capture consensus problems of decision makers (DMs) in conflict, a consensus and non-consensus fuzzy preference relation (FPR) matrix is proposed to the framework of the Graph Model for Conflict Resolution (GMCR). Concentrating on the case of two DMs within GMCR paradigm, four standard fuzzy solution concepts are developed into eight fuzzy stability definitions which can fully represent DMs’ behavior characteristics of win-win and self-interested. To demonstrate how the novel GMCR methodology proposed in this paper can be conveniently utilized in practice, it is then applied to an environmental sustainable development conflict with two DMs. The results show that the general fuzzy equilibrium solutions are the intersection of consensus fuzzy equilibrium and non-consensus fuzzy equilibrium. Therefore, the GMCR technique considering DMs’ consensus can effectively predict the various possible solutions of conflict development under different DMs’ behavior preferences and provide new insights for analysts into a conflict.


2019 ◽  
Vol 11 (4) ◽  
pp. 1099 ◽  
Author(s):  
Nannan Wu ◽  
Yejun Xu ◽  
D. Kilgour

An incomplete fuzzy preference framework for the Graph Model for Conflict Resolution (GMCR) is proposed to handle both complete and incomplete fuzzy preference information. Usually, decision makers’ (DMs’) fuzzy preferences are assumed to be complete fuzzy preference relations (FPRs). However, in real-life situations, due to lack of information or limited expertise in the problem domain, any DM’s preference may be an incomplete fuzzy preference relation (IFPR). An inherent advantage of the proposed framework for GMCR is that it can complete the IFPRs based on additive consistency, which is a special form of transitivity, a common property of preferences. After introducing the concepts of FPR, IFPR, and transitivity, we propose an algorithm to supplement IFPR, that is, to find an FPR that is a good approximation. To illustrate the usefulness of the incomplete fuzzy preference framework for GMCR, we demonstrate it using to a real-world conflict over water allocation that took place in the Zhanghe River basin of China.


2015 ◽  
Vol 24 (2) ◽  
pp. 117-145 ◽  
Author(s):  
Rami A. Kinsara ◽  
Oskar Petersons ◽  
Keith W. Hipel ◽  
D. Marc Kilgour

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