The fusion process with heterogeneous preference structures in group decision making: A survey

2015 ◽  
Vol 24 ◽  
pp. 72-83 ◽  
Author(s):  
Xia Chen ◽  
Hengjie Zhang ◽  
Yucheng Dong
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Fusion Process ◽  
Heterogeneous Preference ◽  
Preference Structures

CONSENSUS MODELLING IN GROUP DECISION MAKING: DYNAMICAL APPROACH BASED ON FUZZY PREFERENCES

2007 ◽  
Vol 03 (02) ◽  
pp. 219-237 ◽  
Author(s):  
MARIO FEDRIZZI ◽  
MICHELE FEDRIZZI ◽  
R. A. MARQUES PEREIRA
Keyword(s):  
Decision Making ◽  
Cost Function ◽  
Group Decision Making ◽  
Group Decision ◽  
Decision Makers ◽  
Weighted Averaging ◽  
Dynamical Process ◽  
Single Individual ◽  
Preference Structure ◽  
Preference Structures

The. notion of consensus plays an important role in group decision making, particularly when the collective preference structure is generated by a dynamical aggregation process of the single individual preference structures. In this dynamical process of aggregation each single decision maker gradually transforms his/her preference structure by combining it, through iterative weighted averaging, with the preference structures of the remaining decision makers. In this way, the collective decision emerges dynamically as a result of the consensual interaction among the various decision makers in the group. From the point of view of applied mathematics, the models of consensual dynamics stand in the context of multi-agent complex systems, with interactive and nonlinear dynamics. The consensual interaction among the various agents (decision makers) acts on their state variables (the preferences) in order to optimize an appropriate measure of consensus, which can be of type 'hard' (unanimous agreement within the group of decision makers) or 'soft' (partial agreement within the group of decision makers). In this paper, we study the modelling of consensus reaching when the individual testimonies are assumed to be expressed as fuzzy preference relations. Here consensus is meant as the degree to which most of the experts agree on the preferences associated to the most relevant alternatives. First of all we derive a degree of dissensus based on linguistic quantifiers and then we introduce a form of network dynamics in which the quantifiers are represented by scaling functions. Finally, assuming that the decision makers can express their preferences in a more flexible way, i.e. by using triangular fuzzy numbers, we describe the iterative process of opinion transformation towards consensus via the gradient dynamics of a cost function expressed as a linear combination of a dissensus cost function and an inertial cost function.

scholarly journals A Similarity Measure-based Optimization Model for Group Decision Making with Multiplicative and Fuzzy Preference Relations

2016 ◽  
Vol 12 (1) ◽  
pp. 26 ◽  
Author(s):  
Xiangrui Chao ◽  
Yi Peng ◽  
Gang Kou
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Optimization Methods ◽  
Decision Makers ◽  
Preference Relations ◽  
Priority Vector ◽  
Proposed Model ◽  
Fuzzy Preference Relations ◽  
Preference Structures

Group decision making (GDM) problem based on different preference relations aims to obtain a collective opinion based on various preference structures provided by a group of decision makers (DMs) or experts, those who have varying backgrounds and interests in real world. The decision process in proposed question includes three steps: integrating varying preference structures, reaching consensus opinion, selecting the best alternative. Two major approaches: preference transformation and optimization methods have been developed to deal with the issue in first step. However, the transformation processes causes information lose and existing optimization methods are so computationally complex that it is not easy to be used by management practice. This study proposes a new consistency-based method to integrate multiplicative and fuzzy preference relations, which is based on a cosine similarity measure to derive a collective priority vector. The basic idea is that a collective priority vector should be as similar per column as possible to a pairwise comparative matrix (PCM) in order to assure the group preference has highest consistency for each decision makers. The model is computationally simple, because it can be solved using a Lagrangian approach and obtain a collective priority vector following four simple steps. The proposed method can further used to derive priority vector of fuzzy AHP. Using three illustrative examples, the effectiveness and simpleness of the proposed model is demonstrated by comparison with other methods. The results show that the proposed model achieves the largest cosine values in all three examples, indicating the solution is the nearest theoretical perfectly consistent opinion for each decision makers.

scholarly journals Group decision-making based on heterogeneous preference relations with self-confidence

2016 ◽  
Vol 16 (4) ◽  
pp. 429-447 ◽  
Author(s):  
Wenqi Liu ◽  
Yucheng Dong ◽  
Francisco Chiclana ◽  
Francisco Javier Cabrerizo ◽  
Enrique Herrera-Viedma
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Preference Relations ◽  
Self Confidence ◽  
Heterogeneous Preference
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