Consensus in a fuzzy environment: a bibliometric study

In today’s organizations, group decision making has become a part of everyday organizational life. It involves multiple individuals interacting to reach a decision. An important question here is the level of agreement or consensus achieved among the individuals before making the decision. Traditionally, consensus has been meant to be a full and unanimous agreement. However, it is often not reachable in practice. A more reasonable approach is the use of softer consensus measures, which assess the consensus in a more flexible way, reflecting the large spectrum of possible partial agreements and guiding the discussion process until widespread agreement is achieved. As soft consensus measures are more human-consistent in the sense that they better reflect a real human perception of the essence of consensus, consensus models based on these kind of measures have been widely proposed. The aim of this contribution is to present a bibliometric study performed on the consensus approaches that have been proposed in a fuzzy environment. It gives an overview about the research products gathered in this research field. To do so, several points have been studied, among others: countries, journals, top contributing authors, most cited keywords, papers and authors. This allows us to show a quick shot of the state of the art in this research area

Group Decision Making Based on a Framework of Granular Computing for Multi-Criteria and Linguistic Contexts

The usage of linguistic information involves computing with words, a methodology assuming linguistic values as computational elements, in group decision-making environments. In recent times, a new methodology founded on a framework of granular computing has been employed to manage linguistic information. An advantage of this methodology is that the distribution and the semantics of the linguistic values, in place of being initially established, are defined by the optimization of a certain criterion. In this paper, different from the existing approaches, we present a novel approach build on the basis of a granular computing framework that is able to cope with group decision-making problems defined in multi-criteria contexts, that is, those in which different criteria are considered to evaluate the possible alternatives for solving the problem. In particular, it models group decision-making problems in a more realistic way by taking into account that each criterion has an importance weight and by considering that each decision maker has a different importance weight for each criterion. This approach makes operational the linguistic values by associating them with intervals via the optimization of an optimization criterion composed of two important aspects that must be taken into account in this kind of decision problems, that is, the consensus at the level of group of decision makers and the consistency at the level of individual decision makers.This work was supported in part by the Spanish Ministry of Economy and Competitiveness under Project DPI2016-77677-P, in part by the RoboCity2030-DIH-CM Madrid Robotics Digital Innovation Hub (``Robótica aplicada a la mejora de la calidad de vida de los ciudadanos. Fase IV''; S2018/NMT-4331), funded by the ``Programas de Actividades I+D de la Comunidad de Madrid,'' and co-funded by the Structural Funds of the EU, and in part by the research grant from the Asociación Universitaria Iberoamericana de Postgrado (AUIP) and Consejería de Economía y Conocimiento de la Junta de Andalucía

Looking Over the Research Literature on Software Engineering from 2016 to 2018

This paper carries out a bibliometric analysis to detect (i) what is the most influential research on software engineering at the moment, (ii) where is being published that relevant research, (iii) what are the most commonly researched topics, (iv) and where is being undertaken that research (i.e., in which countries and institutions). For that, 6,365 software engineering articles, published from 2016 to 2018 on a variety of conferences and journals, are examined.This work has been funded by the Spanish Ministry of Science, Innovation, and Universities under Project DPI2016-77677-P, the Community of Madrid under Grant RoboCity2030-DIH-CM P2018/NMT-4331, and grant TIN2016-75850-R from the FEDER funds

Reaching Consensus in Digital Libraries: A Linguistic Approach

This work has been developed with the financing of FEDER funds in FUZZYLING-II Project TIN2010-17876, the Andalusian Excellence Projects TIC-05299 and TIC-5991, and Proyecto de Investigación del Plan de Promoción de la Investigación UNED 2011 (2011/PUNED/0003)2nd International Conference on Information Technology and Quantitative Management, ITQM 2014Libraries are recently changing their classical role of providing stored information into new virtual communities, which involve large number of users sharing real time information. Despite of those good features, there is still a necessity of developing tools to help users to reach decisions with a high level of consensus in those new virtual environments. In this contribution we present a new consensus reaching tool with linguistic preferences designed to minimize the main problems that this kind of organization presents (low and intermittent participation rates, difficulty of establishing trust relations and so on) while incorporating the benefits that a new digital library offers (rich and diverse knowledge due to a large number of users, real-time communication and so on). The tool incorporates some delegation and feedback mechanisms to improve the speed of the process and its convergence towards a consensual solution.FEDER funds in FUZZYLING-II Project TIN2010-17876Andalusian Excellence Projects TIC-05299 and TIC-5991Proyecto de Investigación del Plan de Promoción de la Investigación UNED 2011 (2011/PUNED/0003

Using Multi-granular Fuzzy Linguistic Modelling Methods to Represent Social Networks Related Information in an Organized Way

Social networks are the preferred mean for experts to share their knowledge and provide information. Therefore, it is one of the best sources that can be used for obtaining data that can be used for a high amount of purposes. For instance, determining social needs, identifying problems, getting opinions about certain topics, ... Nevertheless, this kind of information is difficult for a computational system to interpret due to the fact that the text is presented in free form and that the information that represents is imprecise. In this paper, a novel method for extracting information from social networks and represent it in a fuzzy ontology is presented. Sentiment analysis procedures are used in order to extract information from free text. Moreover, multi-granular fuzzy linguistic modelling methods are used for converting the information into the most suitable representation mean.This work has been supported by the ’Juan de la Cierva Incorporación’ grant from the Spanish Ministry of Economy and Competitiveness and by the Grant from the FEDER funds provided by the Spanish Ministry of Economy and Competitiveness (No. TIN2016-75850-R)

Nuevos modelos de toma de decisión en grupo con información lingüística difusa

Tesis Univ. Granada. Departamento de Ciencias de la Computación e Inteligencia Artificial. Leída el 18 de julio de 200

q2-Index: Quantitative and qualitative evaluation based on the number and impact of papers in the hirsch core

Bibliometric studies at the micro level are increasingly requested by science managers and policy makers to support research decisions. Different measures and indices have been developed at this level of analysis. One type of indices, such as the h-index and g-index, describe the most productive core of the output of a researcher and inform about the number of papers in the core. Other indices, such as the a-index and m-index, depict the impact of the papers in the core. In this paper, we present a new index which relates two different dimensions in a researchers productive core: a quantitative one (number of papers) and a qualitative one (impact of papers). In such a way, we could obtain a more balanced and global view of the scientific production of researchers. This new index, called q2-index, is based on the geometric mean of h-index and the median number of citations received by papers in the h-core, i.e., the m-index, which allows us to combine the advantages of both kind of indices

Group decision making in linguistic contexts: an information granulation approach

Group decision making situations are part of today’s organizations. It is a type of decision making involving many decision makers which act collectively to choose the best alternative (or alternatives) from a set of feasible alternatives. Usually, numerical values have been used by the decision makers to express their opinions on the possible alternatives. However, as the standard representation of the concepts that humans use for communication is the natural language, words or linguistic terms instead of numerical values should be used by the decision makers to provide their preferences. In such a situation, the linguistic information has to be made operational in order to be fully utilized. In this contribution, assuming that decision makers express their opinions by using linguistic terms, we present an information granulation of such a type of information, which is formulated as an optimization problem in which consistency is maximized by a suitable mapping of the linguistic terms on information granules

A consensus model for group decision making problems with unbalanced fuzzy linguistic information

Most group decision making problems based on linguistic approaches use symmetrically and uniformly distributed linguistic term sets to express experts opinions. However, there exist problems whose assessments need to be represented by means of unbalanced linguistic term sets, i.e., using term sets which are not uniformly and symmetrically distributed. The aim of this paper is to present a consensus model for group decision making problems with unbalanced fuzzy linguistic information. This consensus model is based on both a fuzzy linguistic methodology to deal with unbalanced linguistic term sets and two consensus criteria, consensus degrees and proximity measures. To do so, we use a new fuzzy linguistic methodology improving another approach to manage unbalanced fuzzy linguistic information,1 which uses the linguistic 2-tuple model as representation base of unbalanced fuzzy linguistic information. In addition, the consensus model presents a feedback mechanism to help experts for reaching the highest degree of consensus possible. There are two main advantages provided by this consensus model. Firstly, its ability to cope with group decision making problems with unbalanced fuzzy linguistic information overcoming the problem of finding different discrimination levels in linguistic term sets. And, secondly, it supports the consensus process automatically, avoiding the possible subjectivity that the moderator can introduce in this phase