Decision makers involved in complex decision making problems usually provide information about their preferences by eliciting their knowledge with different assessments. Usually, the complexity of these decision problems implies uncertainty that in many occasions has been successfully modelled by means of linguistic information, mainly based on fuzzy...

One of the most complex challenges currently faced by non-profit organizations (NPOs) is demonstrating that they manage resources with the highest levels of efficiency and excellence, and do not deviate from the accomplishment of their mission. Transparency and good governance are highly valuable issues for the survival of these organizations. However, empirical studies and models to measure these concepts are scarce and lack consensus. The objective of this article is to develop a uniform procedure for measuring the levels of transparency and good governance in NPOs, validated by experts, that integrates the most important contributions. The main proposals are supported by lists of indicators whose compliance they try to verify. Finally, we considered the experts’ preferences to obtain the indicator weights by means of the Best–Worst Method and Minimum Cost Consensus model. The result of our work is the development of a list of indicators, which integrates the existing battery of Spanish indicators. We contribute, with this work, to improving the credibility of the third sector from the perspective of donors, users, public administrations, and society. This is an essential issue for the survival of these NPOs.

After an emergency event (EE) happens, emergency decision making (EDM) is a common and effective way to deal with the emergency situation, which plays an important role in mitigating its level of harm. In the real world, it is a big challenge for an individual emergency manager (EM) to make a proper and comprehensive decision for coping with an EE. Consequently, many practical EDM problems drive group emergency decision making (GEDM) problems whose main limitations are related to the lack of flexibility in knowledge elicitation, disagreements in the group and the consideration of experts’ psychological behavior in the decision process. Hence, this paper proposes a novel GEDM approach that allows more flexibility for preference elicitation under uncertainty, provides a consensus process to avoid disagreements and considers experts’ psychological behavior by using the fuzzy TODIM method based on prospect theory. Eventually, a group decision support system (GDSS) is developed to support the whole GEDM process defined in the proposed method demonstrating its novelty, validity and feasibility.

Linguistic Group Decision-Making (LiGDM) aims at solving decision situations involving human decision-makers (DMs) whose opinions are modeled by using linguistic information. To achieve agreed solutions that increase DMs' satisfaction towards the collective solution, Linguistic Consensus Reaching Processes (LiCRPs) have been developed. These LiCRPs aim at suggesting DMs to change their original opinions to increase the group consensus degree, computed by a certain consensus measure. In recent years, these LiCRPs have been a prolific research line, and consequently numerous proposals have been introduced in the specialized literature. However, it has been pointed out the non-existence of objective metrics to compare these models and decide which one presents the best performance for each LiGDM problem. Therefore, this paper aims at introducing a metric to evaluate the performance of LiCRPs that takes into account the resulting consensus degree and the cost of modifying DMs' initial opinions. Such a metric is based on a linguistic Comprehensive Minimum Cost Consensus (CMCC) model based on ELICIT (Extended Comparative Linguistic Expressions with Symbolic Translation) information that models DMs' hesitancy and provides accurate Computing with Words processes. Additionally, the linguistic CMCC optimization model is linearized to speed up the computational model and improve its accuracy.

In recent times, to improve the interpretability and accuracy of computing with words processes, a rich linguistic representation model has been developed and referred to as Extended Comparative Linguistic Expressions with Symbolic Translation (ELICIT). This model extends the definition of the comparative linguistic expressions into a continuous domain due to the use of the symbolic translation concept related to the 2-tuple linguistic model. The aggregation of ELICIT information via a suitable rule that reflects the underlying interrelation among the aggregated information in output is the key tool to design decision-making algorithm for solving multi-attribute decision-making problems under linguistic information. In this study, we introduce three aggregation operators for aggregating ELICIT information in aim of capturing three different types of interrelationship patterns among inputs, which we refer to as ELICIT Bonferroni mean, ELICIT extended Bonferroni mean and ELICIT partitioned Bonferroni mean. Further, the key aggregation properties of these proposed operators are investigated with the proposal of weighted forms. Based on the proposed aggregation operators, an approach for solving multi-attribute decision-making problems, in which attributes are interrelated is developed. Finally, a didactic example is presented to illustrate the working of the proposal and demonstrate its feasibility.

Nowadays, sustainability is an omnipresent concept in our society, which encompasses several challenges related to poverty, inequality, climate change and so on. The United Nations adopted the Agenda 2030, a plan of action formed of universal Sustainable Developments Goals (SDGs) and targets, which countries have to face in order to shift the world toward a sustainable future. One of the most relevant SDGs since the onset of the financial crisis in 2007 has been the so-called reduced inequalities, which consists of dealing with the inequality of opportunities and wealth between and within countries. However, reducing inequalities depends on many heterogeneous aspects, making it difficult to make a proper analysis that evaluates the European Union (EU) countries performance of this goal. In this study, we introduce a novel approach to evaluate the inequalities in EU countries based on a sorting a multi-criteria decision-making method called AHPSort II. This approach allows to obtain a classification of the EU countries according to their achievements in reducing inequalities to subsequently carry out a deep performance analysis with the aim of drawing conclusions as to the evolution of inequality in them along the years. The results are consistent with the main international organizations’ reports and academic literature, as shown in the Discussion Section.