Grey-Based Preference in a Graph Model for Conflict Resolution With Multiple Decision Makers

Kuang, Hanbin; Bashar, M. Abul; Hipel, Keith W.; Kilgour, D. Marc

doi:10.1109/tsmc.2014.2387096

Cited by 105 publications

(8 citation statements)

References 37 publications

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“…Moreover, a GUI from any state except for s 12 of any DM can be sanctioned by the other two DMs through moving the conflict to state s 12 . Hence, all states for all the DMs are grey symmetric metarational and grey general metarational (Kuang et al 2015a). Thus, these two types of stability definitions are meaningless in the conflict analysis and are not discussed in this case study.…”

Section: Grey Unilateral Improvementmentioning

confidence: 99%

“…Moreover, because three DMs are involved in the conflict, coordinated unilateral improvements for multiple DMs must be taken into account. The definition of GUI list for n ðn [ 1Þ DMs from a given state is also provided (Kuang et al 2015a).…”

Section: Grey Unilateral Improvementmentioning

confidence: 99%

“…In related research, Kuang et al (2013) incorporated grey numbers into GMCR to express DMs' uncertain preferences. Kuang et al (2015a) utilized this approach to study a sustainable development conflict involving two DMs. Within the current papers, a grey-based preference structure is used to analyse the strategic conflict when multiple DMs are proposed.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Strategic analysis of a brownfield revitalization conflict using the grey-based graph model for conflict resolution

Kuang

Bashar

Kilgour

et al. 2015

EURO Journal on Decision Processes

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Section: Grey Unilateral Improvementmentioning

confidence: 99%

Section: Grey Unilateral Improvementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Strategic analysis of a brownfield revitalization conflict using the grey-based graph model for conflict resolution

Kuang

Bashar

Kilgour

et al. 2015

EURO Journal on Decision Processes

Self Cite

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“…The solid framework of GMCR is based upon several solution concepts that describe human behavior [13][14][15][16][17][18][19]. When GMCR is employed to study a strategic conflict, there are usually two stages (see Figure 1): modeling and analysis [9].…”

Section: Introductionmentioning

confidence: 99%

Water allocation analysis of the Zhanghe River basin using the Graph Model for Conflict Resolution with incomplete fuzzy preferences

Kilgour

2019

Sustainability

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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.

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“…Yu and Yu researched fuzzy preference further, constructing a framework for calculating fuzzy preferences of DMs and applying it to practical conflict [9]. Kuang et al [10] determined the preference sequence by using a gray-based preference structure and redefined gray stabilities. They then applied the improved model to a brownfield conflict in Ontario, Canada.…”

Section: Introductionmentioning

confidence: 99%

An Improved Graph Model for Conflict Resolution Based on Option Prioritization and Its Application

Yin

2017

IJERPH

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In order to quantitatively depict differences regarding the preferences of decision makers for different states, a score function is proposed. As a foundation, coalition motivation and real-coalition analysis are discussed when external circumstance or opportunity costs are considering. On the basis of a confidence-level function, we establish the score function using a “preference tree”. We not only measure the preference for each state, but we also build a collation improvement function to measure coalition motivation and to construct a coordinate system in which to analyze real-coalition stability. All of these developments enhance the applicability of the graph model for conflict resolution (GMCR). Finally, an improved GMCR is applied in the “Changzhou Conflict” to demonstrate how it can be conveniently utilized in practice.

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Grey-Based Preference in a Graph Model for Conflict Resolution With Multiple Decision Makers

Cited by 105 publications

References 37 publications

Strategic analysis of a brownfield revitalization conflict using the grey-based graph model for conflict resolution

Strategic analysis of a brownfield revitalization conflict using the grey-based graph model for conflict resolution

Water allocation analysis of the Zhanghe River basin using the Graph Model for Conflict Resolution with incomplete fuzzy preferences

An Improved Graph Model for Conflict Resolution Based on Option Prioritization and Its Application

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