The achievement of a 'consensual' solution in a group decision making problem depends on experts' ideas, principles, knowledge, experience, etc. The measurement of consensus has been widely studied from the point of view of different research areas, and consequently different consensus measures have been formulated, although a common characteristic of most of them is that they are driven by the implementation of either distance or similarity functions. In the present work though, and within the framework of experts' opinions modelled via reciprocal preference relations, a different approach to the measurement of consensus based on the Pearson correlation coefficient is studied. The new correlation consensus degree measures the concordance between the intensities of preference for pairs of alternatives as expressed by the experts. Although a detailed study of the formal properties of the new correlation consensus degree shows that it verifies important properties that are common either to distance or to similarity functions between intensities of preferences, it is also proved that it is different to traditional consensus measures. In order to emphasise novelty, two applications of the proposed methodology are also included. The first one is used to illustrate the computation process and discussion of the results, while the second one covers a real life application that makes use of data from Clinical Decision-Making.

We introduce a novel methodology for ranking hesitant fuzzy sets. It builds on a recent, theoretically sound contribution in Social Choice. In order to justify the applicability of such analysis, we develop two real implementations: (i) new metarankings of world academic institutions that build on real data from three reputed agencies, and (ii) a new procedure for improving teaching performance assessments which we illustrate with real data collected by ourselves. These applications provide new grounds for the theoretical model by hesitant fuzzy sets.

The measurement of the degree of agreement in a group has recently attracted considerable attention by researchers from various fields. In this paper we consider situations where each member of a population classifies a list of options as either "acceptable" or "non-acceptable" (as in job committees or elections by approval voting); either "agree" or "disagree" (as in polls or surveys); either "guilty" or "not guilty" (as in jury courts); etc. In order to measure the cohesiveness that the expression of such dichotomous opinions conveys, we propose the novel concept of approval consensus measure (ACM), which does not refer to any priors of the agents like preferences or other decision-making processes. Then we give axiomatic characterizations of two generic classes of ACMs. Finally, we focus on the 2012 presidential elections in USA as a real scenario to put in practice these two proposals.

We investigate from a global point of view the existence of cohesiveness among experts' opinions. We address this general issue from three basic essentials: the management of experts' opinions when they are expressed by ordinal information; the measurement of the degree of dissensus among such opinions; and the achievement of a group solution that conveys the minimum dissensus to the experts' group. Accordingly, we propose and characterize a new procedure to codify ordinal information. We also define a new measurement of the degree of dissensus among individual preferences based on the Mahalanobis distance. It is especially designed for the case of possibly correlated alternatives. Finally, we investigate a procedure to obtain a social consensus solution that also includes the possibility of alternatives that are correlated. In addition, we examine the main traits of the dissensus measurement as well as the social solution proposed. The operational character and intuitive interpretation of our approaches are illustrated by an explanatory example.

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In this paper we address the problem of measuring the degree of consensus/dissensus in a context where experts or agents express their opinions on alternatives or issues by means of cardinal evaluations. To this end we propose a new class of distance-based consensus model, the family of the Mahalanobis dissensus measures for profiles of cardinal values. We set forth some meaningful properties of the Mahalanobis dissensus measures. Finally, an application over a real empirical example is presented and discussed.

In this paper we are concerned with assessing the cohesiveness of a society whose individual preferences are known. We analyse the axiomatic properties of a general proposal to measure aggregate satisfaction, that relies on the consensus with reference to a select social preference.