A social network analysis trust-consensus based approach to group decision-making problems with interval-valued fuzzy reciprocal preference relations

The file attached to this record is a pre print version of the article. The publishers final vestion can be found by following the DOI link.A social network analysis (SNA) trust-consensus based group decision making model with intervalvalued fuzzy reciprocal preference relation (IFRPR) is investigated. The main novelty of this model is that it determines the importance degree of experts by combining two reliable resources: trust degree (TD) and consensus level (CL). To do that, an interval-valued fuzzy SNA methodology to represent and model trust relationship between experts and to compute the trust degree of each expert is developed. The multiplicative consistency property of IFRPR is also investigated, and the consistency indexes for the three dierent levels of an IFRPR are dened. Additionally, similarity indexes of IFRPR are dened to measure the level of agreement among the group of experts. The consensus level is derived by combining both the consistency index and similarity index, and it is used to guide a feedback mechanism to support experts in changing their opinions to achieve a consensus solution with a high degree of consistency. Finally, a quantier guided non-dominance possibility degree (QGNDPD) based prioritisation method to derive the nal consensus-trust based solution is proposed

Online learning the consensus of multiple correspondences between sets.

When several subjects solve the assignment problem of two sets, differences on the correspondences computed by these subjects may occur. These differences appear due to several factors. For example, one of the subjects may give more importance to some of the elements’ attributes than another subject. Another factor could be that the assignment problem is computed through a suboptimal algorithm and different non-optimal correspondences can appear. In this paper, we present a consensus methodology to deduct the consensus of several correspondences between two sets. Moreover, we also present an online learning algorithm to deduct some weights that gauge the impact of each initial correspondence on the consensus. In the experimental section, we show the evolution of these parameters together with the evolution of the consensus accuracy. We observe that there is a clear dependence of the learned weights with respect to the quality of the initial correspondences. Moreover, we also observe that in the first iterations of the learning algorithm, the consensus accuracy drastically increases and then stabilises

How to assess stakeholders' influence in project management? A proposal based on the Analytic Network Process

[EN] In this paper we present a methodology to measure stakeholders' influences within a project from the point of view of the Project Manager. It is a novel proposal for the definition of "influence" among stakeholders based on a multiperspective approach. The concept of influence is broken down into criteria, evaluating different aspects that together define an index which measures the influence of each stakeholder with respect to the rest of the project team. This index is calculated with the Analytic Network Process. The methodology has been applied to a maintenance project for the Spanish National Railway Infrastructure company. Results show that the most influential stakeholders are the Contractor and the Signaling systems provider accounting for 40% of the total influence. These results have helped the Project Manager to be aware of the two most influential stakeholders and set the guidelines for the stakeholder management in the future.Aragonés-Beltrán, P.; García-Melón, M.; Montesinos-Valera, J. (2017). How to assess stakeholders' influence in project management? A proposal based on the Analytic Network Process. International Journal of Project Management. 35(3):451-462. https://doi.org/10.1016/j.ijproman.2017.01.001S45146235

Strategic weight manipulation in multiple attribute decision making

In some real-world multiple attribute decision making (MADM) problems, a decision maker can strategically set attribute weights to obtain her/his desired ranking of alternatives, which is called the strategic weight manipulation of the MADM. In this paper, we define the concept of the ranking range of an alternative in the MADM, and propose a series of mixed 0-1 linear programming models (MLPMs) to show the process of designing a strategic attribute weight vector. Then, we reveal the conditions to manipulate a strategic attribute weight based on the ranking range and the proposed MLPMs. Finally, a numerical example with real background is used to demonstrate the validity of our models, and simulation experiments are presented to show the better performance of the ordered weighted averaging operator than the weighted averaging operator in defending against the strategic weight manipulation of the MADM problems

A chi-square method for priority derivation in group decision making with incomplete reciprocal preference relations

This paper proposes a chi-square method (CSM) to obtain a priority vector for group decision making (GDM) problems where decision-makers’ (DMs’) assessment on alternatives is furnished as incomplete reciprocal preference relations with missing values. Relevant theorems and an iterative algorithm about CSM are proposed. Saaty’s consistency ratio concept is adapted to judge whether an incomplete reciprocal preference relation provided by a DM is of acceptable consistency. If its consistency is unacceptable, an algorithm is proposed to repair it until its consistency ratio reaches a satisfactory threshold. The repairing algorithm aims to rectify an inconsistent incomplete reciprocal preference relation to one with acceptable consistency in addition to preserving the initial preference information as much as possible. Finally, four examples are examined to illustrate the applicability and validity of the proposed method, and comparative analyses are provided to show its advantages over existing approaches

Social network decision making with linguistic trustworthiness based induced OWA operators

The file attached to this record is the authors final peer reviewed version. The publisher's version of record can be found by following the DOI link.Classic aggregation operators in group decision making such as the OWA, IOWA, C-IOWA, P-IOWA and I-IOWA have shown to be successful tools in order to provide flexibility in the aggregation of preferences. However, these operators do not take advantage of information related to the interaction between experts. Experts involved in a group decision making problem may have developed opinions about the reliability of other experts' judgements, either because they have previous history of interaction with each other or because they have knowledge that informs them on the reliability of other colleagues in the group in solving decision making problems in the past. In this paper, and within the framework of social network decision making, we present three new social network analysis based IOWA operators that take advantage of the linguistic trustworthiness information gathered from the experts' social network to aggregate the social group preferences. Their use is analysed with simple but illustrative examples

Fuzzy Rankings for Preferences Modeling in Group Decision Making

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.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

Average-case consistency measurement and analysis of interval-valued reciprocal preference relations

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Measuring consistency of preferences is very important in decision-making. This paper addresses this key issue for interval-valued reciprocal preference relations. Existing studies implement one of two di erent measures: the "classical" consistency measure, and the "boundary" consistency measure. The classical consistency degree of an interval-valued reciprocal preference relation is determined by its associated reciprocal preference relation with highest consistency degree, while the boundary consistency degree is determined by its two associated boundary reciprocal preference relations. However, the consistency index of an interval-valued reciprocal preference relation should be determined by taking into account all its associated reciprocal preference relations. Motivated by this, a new consistency measure for interval-valued reciprocal preference relations, the average-case consistency measure, is suggested and introduced. The new average-case consistency measure of an interval-valued reciprocal preference relation is determined as the average consistency degree of all reciprocal preference relations associated to the interval-valued reciprocal preference relation. Furthermore, the analysis and comparison of the di erent consistency measure internal mechanisms is used to justify the validity of the average-case consistency measure. Finally, an average-case consistency improving method which aims to obtain a modi ed interval-valued reciprocal preference relation with a required average consistency degree is developed

A new measure of consensus with reciprocal preference relations: The correlation consensus degree

Producción CientíficaThe 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.Ministerio de Economía, Industria y Competitividad (Project ECO2012-32178