A comparative study on consensus measures in group decision making

Moral, M. J. del; Chiclana, Francisco; Tapia, José Miguel Rodríguez; Herrera‐Viedma, Enrique

doi:10.1002/int.21954

Cited by 121 publications

(57 citation statements)

References 27 publications

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“…Other algorithms suppose that the reliability of the sensors is known beforehand through an offline training phase where the ground truth is available during that phase or computed based on the physical properties of the sensors. Given the knowledge of the reliability of a sensor, a multitude of conventional fusing approaches can be deployed such as Ordered Weighted Averaging (OWA) method, Bayesian approaches, Dempster Shafer theory and Kalman filters [9], [10), [11), [12). However, in many real-life applications accessing the ground truth is practically impossible especially in harsh environment [13).…”

Section: Introductionmentioning

confidence: 99%

Game-Theoretic Learning for Sensor Reliability Evaluation Without Knowledge of the Ground Truth

Yazidi

Hammer

Samouylov

2021

IEEE Trans. Cybern.

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Sensor fusion has attracted a lot of research atten tion during the last years. Recently, a new research direction has emerged dealing with sensor fusion without knowledge of the ground truth. In this paper, we present a novel solution to the latter pertinent problem. In contrast to the first reported solutions to this problem, we present a solution that does not involve any assumption on the group average reliability which makes our 1·esults more general than previous works. We devise a strategic game where we show that a perfect partitioning of the sensors into reliable and unreliable groups corresponds to a Nash equilibrium of the game. Furthermore, we give sound theoretical results that prove that those equilibria are indeed the 1111ique Nash equilibria of the game. We then propose a solution involving a team of Learning Automata (LA) to unveil the identity of each sensor, whether it is reliable or unreliable, using game-theoretic learning. Experimental results show the accuracy of our solution and its ability to deal with settings that are unsolvable by legacy works.

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Section: Introductionmentioning

confidence: 99%

Game-Theoretic Learning for Sensor Reliability Evaluation Without Knowledge of the Ground Truth

Yazidi

Hammer

Samouylov

2021

IEEE Trans. Cybern.

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“…In decision-making processes, it has been observed that the pairwise comparison of alternatives is one of the most effective methods of expressing opinions because it allows the evaluation of only two alternatives at a time. 29,[32][33][34] This comparison may result in three different outputs: the preference of one alternative, the state of indifference between them, or the inability to compare them. These three different states have been merged into one unique concept of fuzzy PR 35 defined as follows:…”

Section: Introductionmentioning

confidence: 99%

Dealing with incomplete information in linguistic group decision making by means of Interval Type‐2 Fuzzy Sets

Ureña

Kou

et al. 2019

Int J Intell Syst

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Nowadays, in the social network–based decision‐making processes, like the ones involved in e‐commerce and e‐democracy, multiple users with different backgrounds may take part and diverse alternatives might be involved. This diversity enriches the process, but at the same time, increases the uncertainty of opinions. This uncertainty can be considered from two different perspectives: (i) the uncertainty in the meaning of the words given as preferences, that is, motivated by the heterogeneity of the decision makers; and (ii) the uncertainty inherent to any decision‐making process that may lead to an expert not being able to provide all their judgments. The main objective of this study is to address these two types of uncertainty. To do so, the following approaches are proposed: First, to capture, process, and keep the uncertainty in the meaning of the linguistic assumption, the Interval Type‐2 Fuzzy Sets are introduced as a way to model the experts' linguistic judgments. Second, a measure of the coherence of the information provided by each decision maker is proposed. Finally, a consistency‐based completion approach is introduced to deal with the uncertainty presented in the expert judgments. The proposed approach is tested in an e‐democracy decision‐making scenario.

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“…A GDM process consists in the evaluation of the alternatives and the choice of the most satisfactory one, taking into account all the factors and contradictory requirements and according to the preferences of the majority of involved experts. GDM has been widely studied since it has applications in many fields and several approaches have been proposed so far for the representation of experts’ preferences, for their aggregation, for the selection of the best alternative and for consensus reaching …”

Section: Introduction and Related Workmentioning

confidence: 99%

“…GDM has been widely studied since it has applications in many fields and several approaches have been proposed so far for the representation of experts' preferences, for their aggregation, for the selection of the best alternative and for consensus reaching. [1][2][3][4][5][6] By looking at the representation of experts' preferences (that is the focus of this paper), we see that many preference models already exist. With ordinal rankings 7 experts are asked to order the alternatives from the best to the worst; with utility vectors, 8 they must assign an utility value to each alternative; with fuzzy estimates, 9 they must assign them a linguistic evaluation that is subsequently translated into a fuzzy number; with preference relations, 10 they must select, for every pair of alternatives, the preferred one; with fuzzy preference relations (FPRs), 11 they must assign a degree of preference for each alternative over any other.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Fuzzy rankings for preferences modeling in group decision making

Capuano

Chiclana

Herrera‐Viedma

et al. 2018

Int. J. Intell. Syst.

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Although fuzzy preference relations (FPRs) are among the most commonly used preference models in group decision making (GDM), they are not free from drawbacks. First of all, especially when dealing with many alternatives, the definition of FPRs becomes complex and time consuming. Moreover, they allow to focus on only two options at a time. This facilitates the expression of preferences but let experts lose the global perception of the problem with the risk of introducing inconsistencies that impact negatively on the whole decision process. For these reasons, different preference models are often adopted in real GDM settings and, if necessary, transformation functions are applied to obtain equivalent FPRs. In this paper, we propose fuzzy rankings, a new approximate preference model that offers a higher level of user‐friendliness with respect to FPRs while trying to maintain an adequate level of expressiveness. Fuzzy rankings allow experts to focus on two alternatives at a time without losing the global picture so reducing inconsistencies. Conversion algorithms from fuzzy rankings to FPRs and backward are defined as well as similarity measures, useful when evaluating the concordance between experts’ opinion. A comparison of the proposed model with related works is reported as well as several explicative examples.

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A comparative study on consensus measures in group decision making

Cited by 121 publications

References 27 publications

Game-Theoretic Learning for Sensor Reliability Evaluation Without Knowledge of the Ground Truth

Game-Theoretic Learning for Sensor Reliability Evaluation Without Knowledge of the Ground Truth

Dealing with incomplete information in linguistic group decision making by means of Interval Type‐2 Fuzzy Sets

Fuzzy rankings for preferences modeling in group decision making

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