The decision support system GMCR in environmental conflict management

Hipel, Keith W.; Kilgour, D. Marc; Fang, Liping; Peng, Xiaoyong

doi:10.1016/s0096-3003(96)00170-1

Cited by 149 publications

(89 citation statements)

References 21 publications

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“…Nash Stability (R), General Metarationality (GMR), Symmetric Matarationality (SMR), and Sequential Stability (SEQ)). Each concept imitate different way of performance [6] that include carefulness and enthusiasm of a DM to make deliberate recognitions, risk behavior, and awareness of others DMs preferences [8]. Table 1 shows more details about the stability definition.…”

Section: Stability Analysismentioning

confidence: 99%

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Decision support system analysis with the graph model on non-cooperative generic water resource conflicts

Al-Juaidi

2017

IJET

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This paper aims to resolve four non-cooperative generic water disputes using the Graph Model approach for conflict resolution. Therefore, a Decision Support System (DSS) has been utilized integrating multiple-criteria decision analysis, stability analysis, and uncertainty analysis using the info-gap technique. The DSS has been applied to four different non-cooperative water conflict including: (1) groundwater common pool; (2) environmental problem between two countries; (3) river conflict dispute between two countries; and (4) sustainable development game. The DSS demonstrates four stability concepts, including Nash Stability (R), General Metarationality (GMR), Symmetric Metarationality (SMR), Sequential stability (SEQ) to illustrate how results differ with regard to the stability concepts. After classifying the preferences of stakeholders, the DSS recognized the most stable solution, considering the potential actions and counteractions of all stakeholders. Consequently, solution robustness was tested under the uncertainty related to stakeholders' perspective, under non-cooperative attitudes. When there are no sufficient details about the conflict and what decision to be prepared, the DSS proved to be useful in modeling complex disputes, determining most robust solution, and examining the effect of uncertainty.

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Section: Stability Analysismentioning

confidence: 99%

“…Table 1 shows more details about the stability definition. These stability concept definitions are elaborated in [8]. No other decisions bring a better payoff.…”

Section: Stability Analysismentioning

confidence: 99%

Decision support system analysis with the graph model on non-cooperative generic water resource conflicts

Al-Juaidi

2017

IJET

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“…So a new preference structure is proposed by some scholars, which is {>i, ～ i, Ui}, the meaning of >I and ～I is consistent with simple preference structure, "sUiq" represents decision makers i might tend to state s, also might tend to state q. sUiq reflects that decision makers i is lack of the preference information of state s, q, or decision makers i don't want to make a comparison between two states. Preference uncertainty theory expands the application range of the existing method of graph model [10] . Currently, MRCR system has realized the uncertain preference input, which makes the conflict decision analysis system more accurate on the analysis of the case model.…”

Section: A Graph Modelmentioning

confidence: 99%

Research on the Conflict Analysis of Supply Chain Management based on Graph Model with MRCR

Chen¹,

Song²,

Xuesong³

et al. 2014

Proceedings of the 2nd International Conference on Software Engineering, Knowledge Engineering and Information Engineering (SEK

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Abstract-In this research, the conflict problem in supply chain management is studied. The method of graph model is applied to solve the conflict problem in supply chain management, then based on the matrix form, the decision support system(MRCR) is used to make auxiliary decision, considering the uncertainty to select the strategy of decision makers in supply chain management. And an empirical analysis about the conflict problem is taken in YJ Nanjing Company's supply chain management. On the basis of the characteristics of YJ Company supply chain management, model is established, then, equilibrium point is obtained by MRCR, finally, make a decision to resolve the conflict after analysis.

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“…When preference uncertainty is incurred in a multiple participant-multiple objective decision problem, preference statements, as modeled for the crisp option prioritization technique, developed in Peng, 1999;Fang et al, 2003) and described in Subsection 2.2.7, may not be assessed to be precisely "true" or "false" at some feasible states. Note that an earlier version of crisp option prioritization was first introduced by Fraser and Hipel (1988), discussed by Fraser (1993Fraser ( , 1994, and then generalized for use within GMCR by Peng et al (1997);Peng (1999);Hipel et al (1997);Fang et al (2003).…”

Section: Fuzzy Truth Values and Fuzzy Scoresmentioning

confidence: 99%