Consensus under a fuzzy context: Taxonomy, analysis framework AFRYCA and experimental case of study

Consensus reaching processes play an increasingly important role in the resolution of group decision making problems: a solution acceptable to all the experts participating in a problem is necessary in many real-life contexts. A large number of consensus approaches have been proposed to support groups in such processes, each one with its own characteristics, such as the methods utilized for the fusion of information regarding the preferences of experts. Given this variety of existing approaches in the literature to support consensus reaching processes, this paper considers two main objectives. Firstly, we propose a taxonomy that provides an overview and categorization of some existing consensus models for group decision making problems defined in a fuzzy context, taking into account the main features of each model. Secondly, the paper presents AFRYCA, a simulation-based analysis framework for the resolution of group decision making problems by means of different consensus models. The framework is aimed at facilitating a study of the performance of each consensus model, as well as determining the most suitable model/s for the resolution of a specific problem. An experimental study is carried out to show the usefulness of the framework. Decision making is a common process in daily life, characterized by the existence of several alternatives and the need to decide which one/s are the best or should be chosen as the solution to a problem. Group Decision Making (GDM) problems, in which several individuals or experts with different points of view take part in a decision problem with the aim of achieving a common solution , frequently occur in many organizations nowadays [1,2]. Although decision problems may take place in different environments (certainty, risk or uncertainty), most real-life GDM problems are often defined in uncertain environments. Due to the difficulty of dealing with uncertainty of a non-probabilistic nature, which is mainly caused by the imprecision and vagueness of information, experts must express their preferences over alternatives by means of information domains that allow them to deal with such uncertainty. To do so, fuzzy modeling and linguistic information has been utilized in such situations [3–5]. Traditionally, GDM problems have been solved by applying an alternative selection process [6], in which the preferences of each expert over the alternatives are gathered and the best alternative or subset of alternatives is chosen [7]. This resolution scheme does not take into account the existing level of agreement between experts , therefore some experts …