Composite Decision Makers in the Graph Model for Conflict Resolution: Hesitant Fuzzy Preference Modeling

2020 ◽  
pp. 1-14
Author(s):  
Nannan Wu ◽  
Yejun Xu ◽  
D. Marc Kilgour ◽  
Liping Fang
Keyword(s):  
Conflict Resolution ◽  
Graph Model ◽  
Decision Makers ◽  
Preference Modeling ◽  
Fuzzy Preference

Two-Decision-Maker Conflict Resolution with Fuzzy Preferences

2014 ◽  
Vol 2 (2) ◽  
pp. 40-59
Author(s):  
Mubarak S. Al-Mutairi
Keyword(s):  
Fuzzy Logic ◽  
Conflict Resolution ◽  
Decision Maker ◽  
Decision Makers ◽  
Fuzzy Approach ◽  
Model Uncertainties ◽  
Preference Information ◽  
Fuzzy Preferences ◽  
Illustrative Application ◽  
Fuzzy Preference

A unique fuzzy approach is developed to model uncertainties in the preferences of a decision maker involved in a conflict. Human judgments, including expressing preferences over a set of feasible outcomes or states in a conflict, are usually imprecise. Situations characterized by vagueness, impreciseness, incompleteness and ambiguity, are often reflected in the decision maker's preferences. When modeling a conflict, it is assumed that the decision makers, the courses of actions available for each, and the preferences of each decision maker are known. When the preferences of the decision maker over a certain set of actions are not known with certainty, this could affect the overall equilibria which are predicted in an analysis. Hence, fuzzy logic is used to handle imprecise or vague preference information so that realistic equilibria can be found. The well-known game of Prisoner's Dilemma, in which one must decide whether or not to cooperate, is employed as an illustrative application to demonstrate how the fuzzy preference methodology works in practice.

Strategic analysis of the James Bay hydro-electric dispute in Canada

2005 ◽  
Vol 32 (5) ◽  
pp. 868-880 ◽  
Author(s):  
Jing Ma ◽  
Keith W Hipel ◽  
Mitali De
Keyword(s):  
Conflict Resolution ◽  
Decision Support ◽  
Stability Analysis ◽  
Decision Support System ◽  
Support System ◽  
Graph Model ◽  
Decision Makers ◽  
Native People ◽  
Strategic Analysis ◽  
James Bay

A strategic analysis of the James Bay conflict was carried out by using the graph model for conflict resolution. In 1971, Hydro-Québec, which is the third-largest electrical generating company of North America, began its large-scale hydroelectric projects in the James Bay area. Since the projects would significantly affect the living conditions of the native people and the environment around that region, worldwide debates were stimulated. A conflict model was developed in terms of the decision makers, their options, and their preferences for the situation existing as of January 2002, just prior to the signing of the final agreement. Subsequently, a stability analysis based on the calibrated model indicates that a possible resolution is that Hydro-Québec reduces the number of proposed power stations to appease the native people, who in turn would not initiate lawsuits. Sensitivity and hypergame analyses were also carried out to demonstrate the effects of preferences of decision makers on the final resolution. In practice, the modelling and analysis were implemented using the decision support system, GMCR II®. Key words: Hydroelectric, conflict resolution, decision support system, graph model, stability analysis, sensitivity analysis, hypergame analysis.

The graph model for conflict resolution with consensus fuzzy preferences and its application to environmental sustainable development

2020 ◽  
Vol 39 (5) ◽  
pp. 6721-6731
Author(s):  
Nannan Wu ◽  
Yejun Xu ◽  
Lizhong Xu ◽  
Huimin Wang
Keyword(s):  
Sustainable Development ◽  
Conflict Resolution ◽  
Preference Relation ◽  
Graph Model ◽  
Decision Makers ◽  
The Novel ◽  
Equilibrium Solutions ◽  
Fuzzy Preferences ◽  
Fuzzy Solution ◽  
Behavior Characteristics

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.

Conflict analysis based on the graph model for conflict resolution and grey matrix

2013 ◽  
Vol 3 (1) ◽  
pp. 95-106 ◽  
Author(s):  
Xueshan Han ◽  
Thi Dieu Linh Nguyen ◽  
Haiyan Xu
Keyword(s):  
Conflict Resolution ◽  
Prisoner's Dilemma ◽  
Pure Strategy ◽  
Prisoner’S Dilemma ◽  
Graph Model ◽  
Conflict Analysis ◽  
Decision Makers ◽  
Analysis Model ◽  
Policy Makers ◽  
Content Type

PurposeThe purpose of this paper is to propose a complete theory of grey conflict analysis model based on grey game and the graph model for conflict resolution and also, to illustrate a case of “prisoner's dilemma” in the traditional grey game as an example.Design/methodology/approachBased on the theories of grey game and graph model for conflict resolution, this paper concentrates on the model of grey conflict analysis in a case of two players under the condition of symmetrical loss information. By analyzing decision makers, strategies, states, graph model and grey potential, and the number of decision makers' steps, the pure strategy Nash equilibrium is extended to grey potential‐general metarationality, grey potential‐symmetrical metarationality, and grey potential‐sequential stability. Meanwhile, the logical relationships between solutions are discussed. A specific case study is carried out to illustrate how the proposed grey conflict analysis model is used in practice.FindingsThe results in this paper indicate that more stable solutions are found when one considers the grey potential‐general metarationality, the grey potential‐symmetrical metarationality, and the grey potential‐sequential stability, and then solve the paradox of “prisoner's dilemma”.Practical implicationsThis new grey conflict analysis model could be used to provide useful information for policy makers during existing conflicts or negotiations among parties or enterprises.Originality/valueThe paper succeeds in constructing a new grey conflict analysis model, in which the solution concepts are studied; and the two‐player grey game will be extended to n‐players in the near future.

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

2019 ◽  
Vol 11 (4) ◽  
pp. 1099 ◽  
Author(s):  
Nannan Wu ◽  
Yejun Xu ◽  
D. Kilgour
Keyword(s):  
Conflict Resolution ◽  
River Basin ◽  
Water Allocation ◽  
Common Property ◽  
Preference Relation ◽  
Real Life ◽  
Graph Model ◽  
Additive Consistency ◽  
Fuzzy Preferences ◽  
Fuzzy Preference

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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