A consensus model for large-scale group decision making with hesitant fuzzy information and changeable clusters

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Fuzzy Multiple Attribute Decision Making Methods And Applications

In linguistic large-scale group decision making (LSGDM), it is often necessary to achieve a consensus. Particularly, when computing with words and linguistic decision, we must keep in mind that words mean different things to different people. Therefore, to represent the specific semantics of each individual, we need to consider the personalized individual semantics (PIS) model in linguistic LSGDM. In this paper, we propose a consensus model based on PIS for LSGDM. Specifically, a PIS process to obtain the individual semantics of linguistic terms with linguistic preference relations is introduced. A consensus process based on PIS, including the consensus measure and feedback recommendation phases, is proposed to improve the willingness of decision makers who follow the suggestions to revise their preferences in order to achieve a consensus in linguistic LSGDM problems. The consensus measure defines two opposing consensus groups with respective acceptable and unacceptable consensus. In the feedback recommendation phase, a PIS-based clustering method to get decision makers with similar individual semantics is proposed. Recommendation rules design a feedback for decision makers with unacceptable consensus, finding suitable moderators from the decision makers with acceptable consensus based on cluster proximity.

A Consensus Model for Large-Scale Linguistic Group Decision Making With a Feedback Recommendation Based on Clustered Personalized Individual Semantics and Opposing Consensus Groups

A position and perspective analysis of hesitant fuzzy sets on information fusion in decision making. Towards high quality progress

Consensus efficiency in group decision making: A comprehensive comparative study and its optimal design

Aggregating opinions in non-uniform ordered qualitative scales

In this paper we consider that a group of decision makers rank a set of alternatives by means of weak orders for making a collective decision. Since decision makers could have very different opinions and it should be important to reach a consensuated decision, we have introduced indices of contribution to consensus for each decision maker for prioritizing them in order of their contributions to consensus. These indices are defined by means of a consensus measure which assigns a number between 0 and 1 to each subset of decision makers. For putting in practice this idea, we have introduced a class of consensus measures based on distances on weak orders and we have analyzed some of their properties. We have illustrated the weighted decision procedure with an example.

/pdf/some-consensus-measures-and-their-applications-in-group-9f5jej1gxw.pdf

Some consensus measures and their applications in group decision making

Metrizable ordinal proximity measures and their aggregation

In this paper, we consider that a group of agents judge a set of alternatives by means of an ordered qualitative scale. The scale is not assumed to be uniform, i.e., the psychological distance between adjacent linguistic terms is not necessarily always the same. In this setting, we propose how to measure the consensus in each subset of at least two agents over each subset of alternatives. We introduce a consensus reaching process where some agents may be invited to change their assessments over some alternatives in order to increase the consensus. All the steps are managed in a purely ordinal way through ordinal proximity measures.

/pdf/a-consensus-reaching-process-in-the-context-of-non-uniform-4q0bmcjabe.pdf

A consensus reaching process in the context of non-uniform ordered qualitative scales

In this paper, we introduce a new hierarchical agglomerative clustering process in the setting of weak orders. This process is based on consensus measures induced by weighted Kemeny distances that associate a number between 0 and 1 to each subset of weak orders. Then, clusters are sequentially formed according to the consensus among the corresponding weak orders. The process is illustrated with the data obtained in an experiment carried out during the 2006 Public Choice Society election.

/pdf/consensus-based-hierarchical-agglomerative-clustering-in-the-4zlg7abmut.pdf

David Pérez-Román

Papers

Aggregating opinions in non-uniform ordered qualitative scales

Some consensus measures and their applications in group decision making

Metrizable ordinal proximity measures and their aggregation

A consensus reaching process in the context of non-uniform ordered qualitative scales

Consensus-based hierarchical agglomerative clustering in the context of weak orders