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Nowadays we are living the apogee of the Internet based technologies and consequently web 2.0 communities, where a large number of users interact in real time and share opinions and knowledge, is a generalized phenomenon. This type of social networks communities constitute a challenge scenario from the point of view of Group Decision Making approaches, because it involves a large number of agents coming from different backgrounds and/or with different level of knowledge and influence. In these type of scenarios there exists two main key issues that requires attention. Firstly, the large number of agents and their diverse background may lead to uncertainty and or inconsistency and so, it makes difficult to assess the quality of the information provided as well as to merge this information. Secondly, it is desirable, or even indispensable depending on the situation, to obtain a solution accepted by the majority of the members or at least to asses the existing level of agreement. In this contribution we address these two main issues by bringing together both decision Making approaches and opinion dynamics to develop a similarity-confidence-consistency based Social network that enables the agents to provide their opinions with the possibility of allocating uncertainty by means of the Intuitionistic fuzzy preference relations and at the same time interact with like-minded agents in order to achieve an agreement.
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... In general, the process of reaching consensus is dynamic and iterative, where the individuals express their opinions over a finite set of alternatives, then discuss and modify their initial positions and finally find a satisfactory solution accepted by the most of the experts (Lu et al. 2007;Gupta 2015;Dong and Xu 2016). In particular, with the development of social networks, of much interest is the large-scale group decision making based on the information (Wu et al. 2018;Dong et al. 2018;Ureña et al. 2019). On the other hand, it is worth noting that the analytic hierarchy process (AHP) is a popular decision making methodology developed by Saaty (1980), where a complex decision making problem is decomposed to a hierarchy with criteria, subcriteria and alternatives. ...
... In what follows, an illustrate example is offered to verify the proposed model. It is seen that a real case study is useful to illustrate the proposed group decision making model (D'Aniello et al. 2015;Ureña et al. 2019). Here, we consider that a professor in operations research should be chosen at the university. ...
... On the other hand, it is noted that of much significance is to apply the proposed group decision making model to a practical case (D'Aniello et al. 2015;Ureña et al. 2019). For the purpose of reaching consensus in the Situation Awareness framework (Endsley 1995), a fuzzy group decision making model is proposed in D' Aniello et al. (2015), where the interactive process between the decision makers and the moderator has been modeled. ...
... S OCIAL network group decision making (SN-GDM) refers to the process in which a group of experts connected by social relations (such as friendships or trust) selects the best solution from a set of alternatives [1]. SN-GDM is a hot research topic, and has recently attracted widespread attention [2], [3], [4]. In SN-GDM, experts have to reach a certain level of consensus on alternatives in order to obtain the final solution. ...
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... The determination of weight will have a significant influence on grading nursing care evaluation; therefore, many experts in various fields need to judge the importance of indicators in accordance with their own knowledge. To deal with the complex social relations among decision makers, the concept of social network has been gradually applied to group decision making [22,23]. ...
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Establishing a scientific and sustainable grading nursing care evaluation system is the key to realizing the rational distribution of medical and nursing resources in the combined medical and nursing care services. This study establishes a grading nursing care index system for medical and nursing institutions from both medical and nursing aspects, and proposes a grading nursing care evaluation model based on a combination of interval-valued intuitionistic fuzzy entropy and a two- stage gray synthetic clustering model for interval gray number under a social network context. Through case analysis, the proposed method can directly classify the elderly into corresponding grading nursing care grades and realize the precise allocation of medical and nursing resources, which proves the feasibility of the method.
... Capuano et al. [27] estimated missing preferences through a social influence network. Ureña et al. [28] developed a similarity-confidence-consistency-based social network to solve the problem of uncertainty of opinions in group decision-making. Dong et al. [3] reviewed incomplete preference value estimation based on social networks. ...
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