# A Multiple Criteria Decision Analysis Framework for Dispersed Group Decision-Making Contexts
## 1 Introduction  
Group Decision-Making (GDM) is typically regarded as a process involving the opinion and interests of a group of people (also known as decision-makers) to collectively achieve a consensual decision for a certain problem [1]. In this context, solving a problem often requires the analysis and comparison of different alternatives as possible solutions through their pros and cons related to different criteria [2]. There are several advantages associated with GDM processes, such as to improve the quality of the decision, to share workloads, to get support among stakeholders, and to train less experienced group members [3; 4; 5]. In addition, when the decision-making process is performed in group, the chance to detect a problem is higher, and subsequently, the decision-makers can work together to find a solution for that problem. This turns GDM into a more effective and fast process [6]. In order to benefit from these advantages, conditions for the decision-makers to interact, exchange ideas, to understand the reasons behind different preferences, among other important activities, need to be created [7; 8], especially when decision-makers are geographically dispersed. The number of approaches that intend to support decision-making processes anytime and anywhere is increasing [2; 9; 10]. However, the big majority is still mainly focused on the outcomes and does not allow decision-makers to benefit from the advantages associated with face-to-face GDM. To propose solutions without explaining the motives can be a source of entropy because it makes the decision-making process less transparent, making it difficult for the decision-makers to understand the recommendations/proposals and the current decision context. Consequently, the decision quality is prejudiced. In this paper, we propose a Multiple Criteria Decision Analysis (MCDA) Framework that intends to help researchers and developers in the development of new MCDA methods mainly designed to operate in GDM contexts with dispersed decision-makers. The proposed framework offers a set of functionalities that helps groups attaining consensual decisions, promoting and facilitating at the same time the access to intelligence, leveraging the creation of knowledge. The proposed framework was defined to allow the deduction of the reasons behind the different recommendations/proposals. Several important aspects, such as expertise level, credibility, behavior styles, and prediction of decision quality were considered. The framework includes a consistency method to calculate the consistency of different alternatives and all the necessary data to predict the decision quality of the different solutions. In addition, it allows to predict the criteria importance from the decision-makers' perspective and for each alternative from a global view of the decision problem. In order to make the proposed Framework available to different clients, we developed a microservice that exposes the Framework's functionalities using a REST API and included it on an existing Consensus-based Group Decision Support System. To evaluate the proposed MCDA framework, a set of experiments was conducted. The experiments showed that the framework has the necessary material to be extended by MCDA methods and to perform important tasks such as to recommend an alternative(s) as a solution, to recommend an alternative to reject (in order to decrease the decision problem complexity), to explain the reasons behind the different recommendations, to present data about the other decision-makers' preferences and the possible reasons for their preferences, and to alert decision-makers to think twice about inconsistencies in the evaluation of their preferences regarding alternatives and criteria. In addition, the utility of the framework was demonstrated through an experimental consensus reaching process, namely by autonomous software agents, to make personalized recommendations and to provide personalized information.