Iván Palomares

Consensus under a fuzzy context: Taxonomy, analysis framework AFRYCA and experimental case of study

Abstract Consensus reaching processes play an increasingly important role in the resolution of group decision making problems: a solution acceptable to all the experts participating in a problem is necessary in many real-life contexts. A large number of consensus approaches have been proposed to support groups in such processes, each one with its own characteristics, such as the methods utilized for the fusion of information regarding the preferences of experts. Given this variety of existing approaches in the literature to support consensus reaching processes, this paper considers two main objectives. Firstly, we propose a taxonomy that provides an overview and categorization of some existing consensus models for group decision making problems defined in a fuzzy context, taking into account the main features of each model. Secondly, the paper presents AFRYCA, a simulation-based analysis framework for the resolution of group decision making problems by means of different consensus models. The framework is aimed at facilitating a study of the performance of each consensus model, as well as determining the most suitable model/s for the resolution of a specific problem. An experimental study is carried out to show the usefulness of the framework.

An attitude-driven web consensus support system for heterogeneous group decision making

Consensus reaching processes are applied in group decision making problems to reach a mutual agreement among a group of decision makers before making a common decision. Different consensus models have been developed to facilitate consensus reaching processes. However, new trends bring diverse challenges in group decision making, such as the modelling of different types of information and of large groups of decision makers, together with their attitude to achieve agreements. These challenges require the capacity to deal with heterogenous frameworks, and the automation of consensus reaching processes by means of consensus support systems. In this paper, we propose a consensus model in which decision makers can express their opinions by using different types of information, capable of dealing with large groups of decision makers. The model incorporates the management of the groups attitude towards consensus by means of an extension of OWA aggregation operators aimed to optimize the overall consensus process. Eventually, a novel Web-based consensus support system that automates the proposed consensus model is presented.

A Semisupervised Multiagent System Model to Support Consensus-Reaching Processes

Consensus reaching processes as part of solving group decision-making problems attempt to reach a mutual agreement in the group before making a decision. Most consensus models and consensus support systems that are proposed in the literature present some noticeable drawbacks: the need for constant human supervision by experts to guarantee an effective process and the difficulty to manage large groups of experts, which are increasingly common in current decisions and may imply a higher cost and complexity to carry out such processes. In order to overcome these problems, this paper presents a novel consensus support system based on the multiagent system paradigm, which automates and supports consensus reaching processes by providing agents with the necessary degree of autonomy to conduct discussion processes by themselves, with a semisupervised methodology. The main novelty of such a system is the agent semisupervised autonomy approach it incorporates, which lets agents conduct most of the discussion process by themselves and allows them to interact with their corresponding human experts under certain circumstances in which human supervision might be convenient and necessary.

COMAS: A Multi-agent System for Performing Consensus Processes

The need for achieving consensus in group decision making problems is a common and sometimes necessary task in a myriad of social and business environments. Different consensus reaching processes have been proposed in the literature to achieve agreement among a group of experts. Initially, such processes were guided by a human moderator, but afterwards, some proposals to facilitate such a process arose by automating the moderator tasks. However, not many consensus support systems have been developed so far, due to the difficulty to manage intelligent tasks and cope with the negotiation process involved in consensus. This paper aims to present an initial prototype of an automatic consensus support system, developed by using the multi-agent paradigm that provides intelligent tools and capacities to tackle the inherent complexity found in this problem. To do so, we focus on the consensus model considered, the multi-agent architecture designed to develop such a system, and the ontology used for reasoning and communication tasks.

A COMPARATIVE STUDY OF HETEROGENEOUS DECISION ANALYSIS APPROACHES APPLIED TO SUSTAINABLE ENERGY EVALUATION

The evaluation of sustainable energy policies supports the selection of the best policy to put it in practice. In this evaluation, stakeholders may express their preferences in different domains, considering their diverse background and the imprecision and uncertainty of the related information, as well as the nature of assessed criteria. Therefore, these evaluation problems require the selection of an adequate approach to manage such a heterogeneous framework. In this paper, we review three approaches with different strategies to deal with heterogeneous information and apply them to the evaluation of sustainable energy policies, with the view of analyzing their influence in a complex evaluation process, mainly in terms of interpretability and understandability.

Low-dimensional Visualization of Experts’ Preferences in Urgent Group Decision Making under Uncertainty

Abstract Urgent or critical situations, such as natural disasters, often require that stakeholders make crucial decisions, by analyzing the information provided by a group of experts about the different elements of interest in these contexts, with the aid of decision support tools. This framework can be modeled as a Group Decision Making problem defined under uncertain environments, which are characterized by the imprecision and vagueness of information about the problem tackled. One of the main aspects to consider when solving Group Decision Making problems in urgent situations, is the fact that decisions should be taken under the highest level of possible agreement amongst all participating experts, in order to avoid serious mistakes or undesired responsibilities by some experts. Due to the complexity of urgent situations and the great amount of information about experts’ preferences that must be managed by stakeholders, it would be difficult to reach consensus in the group within a reasonable time period, since it requires an adequate analysis of experts’ preferences. Such an analysis might become a difficult task in these contexts, due to the inherent complexity, time pressure, etc. In order to help making consensual decisions in urgent situations and urgent computing environments, we propose a visual decision support tool for Group Decision Making problems defined under uncertainty. Such a tool provides stakeholders with a two-dimensional visual representation of experts’ preferences based on the similarities between them, and it enables the analysis of easily interpretable information about the state of the decision problem, as well as the detection of agreement/disagreement positions between experts. The tool is based on Self-Organizing Maps, an unsupervised learning technique aimed at the visual projection of information related to preferences of experts into a low-dimensional space.

An approach based on computing with words to manage experts behavior in consensus reaching processes with large groups

Group decision making problems are characterized by the participation of multiple experts with different points of view, who attempt to find a common solution to a problem composed by a set of alternatives. Such problems are often defined in environments of uncertainty caused by the imprecision and vagueness of information, therefore experts must utilize appropriate information domains to deal with such uncertainty when expressing their preferences, e.g. linguistic information. Usually, in group decision making problems it is necessary to apply a consensus reaching process, in which experts discuss and make their opinions closer to each other, in order to achieve a high level of agreement before making the decision. Nevertheless, in large-scale group decision making problems, where a large group of individuals take part, it is more frequent the existence of certain subgroups with a non-cooperative behavior towards consensus reaching. For this reason, it would be convenient to identify such subgroups and deal with them, so that their behavior does not affect the consensus reaching process negatively. In this contribution, we present an approach based on computing with words and fuzzy set theory, to study the behavior of experts in consensus reaching processes, with the aim of identifying and penalizing the importance weights of those experts whose behavior does not contribute to reach a collective agreement.

COMAS: A consensus multi-agent based system

In group decision making problems is common the necessity of achieving a consensus before making a decision. Many consensus reaching processes have been introduced in the literature but not many intelligent systems have finally been implemented to deal with such processes. In this contribution an initial prototype of a consensus support system supported on a multi-agent paradigm is presented, showing the system architecture (set of agents, behaviours and relationships) and a preliminary ontology to represent the problem knowledge and used for the agent communication.

Octogenarian Donors in Liver Transplantation

INTRODUCTION Due to the disparity between the number of patients on the list for liver transplantation and the availability of organs, the use of older donors has become necessary. The aim of this study was to investigate the outcomes of liver transplantation using octogenarian donors. METHODS From December 2003 to February 2016, 777 liver transplantations were performed at our institution, 33 of them (4.2%) with donors 80 years old and above. Our policy for the acceptance of these donors is based on preoperative liver function tests, donor hemodynamic stability, and intraoperative normal gross aspect. Octogenarian grafts were deliberately not assigned to retransplantations or to recipients with multiple previous surgical procedures or extensive portal thrombosis. RESULTS Mean donor age was 82.7 ± 2.1 years, with a range between 80 and 88. Only 12.1% suffered hemodynamic instability during the intensive care unit stay. Three donors (9.1%) had a history of diabetes mellitus. The mean Model for End-Stage Liver Disease score among recipients was 14.7 ± 5.6. Mean cold ischemia time was 302 ± 61 minutes. After a median follow-up of 18.5 months (range 7.5 to 47.5), no graft developed primary nonfunction. We observed hepatic artery thrombosis in 1 patient (3%) and biliary complications in 4 patients (12.5%). There was 1 case of ischemic-type biliary lesion, although it was related to hepatic artery thrombosis. Patient survival at 1 and 3 years was 90.3%, whereas graft survival was 92.6% and 86.4%, respectively. CONCLUSIONS Excellent mid-term results can be obtained after liver transplantation with octogenarian donors with strict donor selection and adequate graft allocation.

Attitude-Based Consensus Model for Heterogeneous Group Decision Making

Usually, human beings make decisions in their daily life providing their preferences according to their knowledge area and background. Therefore, when a high number of decision makers take part in a group decision-making problem, it is usual that they use different information domains to express their preferences. Besides, it might occur that several subgroups of decision makers have different interests, which may lead to situations of disagreement amongst them. Therefore, the integration of the group’s attitude toward consensus might help optimizing the consensus reaching process according to the needs of decision makers. In this contribution, we propose an attitude-based consensus model for heterogeneous group decision-making problems with large groups of decision makers.