# A Multiple Criteria Decision Analysis Framework for Dispersed Group Decision-Making Contexts
## 2 Literature Review  
The Consensus Reaching Process is an essential process in GDM events since in most cases it is impossible to reach a consensual decision after the participants' initial preferences configuration [11]. It consists of a dynamic feedback process that aims to reduce existing conflicts as the group seeks to converge towards a solution [12]. Most existing works consider that a decision becomes consensual when a certain level of Consensus Degree is reached [10; 13]. However, reaching consensus can be an extremely complex process, as the participants involved can be highly diverse (differing in their intentions, preferences, motivations, level of expertise, etc. [14; 15]). Different strategies for achieving consensus and which have been applied to many real-world problems, especially in the context of MCDA, such as financial, energy planning, and sustainable development have been proposed [16]. Several authors have addressed the topic of consensus-reaching in GDM and have presented ideas worthy of discussion. [PERSON], [PERSON] [17] studied the impact of consensus in multi-criteria GDM and proposed a consensus-reaching model that considers three main features. The first feature relates to how decision-makers hold individual and different interests regarding each alternative and define their own criteria to evaluate alternatives. In the second feature, decision-makers do not have to reach a consensus regarding the set of criteria defined to evaluate each alternative, but instead, must find the alternative supported by most decision-makers. Finally, the third feature describes the dynamic characteristics of the decision-making process and how individual preferences regarding both sets of available criteria and alternatives can change throughout the decision-making process. The authors applied the proposed model in a resolution framework which includes two processes to obtain a collective solution. In the first process, the individual evaluation matrices are normalized, and the weights of the individual attributes are measured. Each individual alternative is then ranked, and a temporal solution is finally derived. In the second process, the achieved consensus is improved by first measuring the consensus level regarding the obtained collective solution and then designing feedback adjustment rules to help decision-makers modify their individual sets of attributes, alternatives, and preferences. [PERSON] and [PERSON] [18] studied the measurement of interrater reliability in the climate context and compared it with interrater agreement in terms of consensus and consistency. The authors explained how interrater reliability referred to consistency while interrater agreement referred to interchangeability of opinion among raters (consensus). Other authors have studied and compared consensus and consistency in the context of GDM. [PERSON], [PERSON] [19] extended a decision theory proposed in [20], originally formulated to explain choice behavior for decision-making under uncertainty and presented a dynamic decision model for multi-alternative problems. The dynamic feature of their model was determined by an initial preference state (bias resulting from previous experience, previous habits, and history of choices) and a feedback matrix (contains all the interconnections and self-connection between choice alternatives). The authors compared their model with other multi-alternative models according to similarity, attraction and compromise effects, and concluded that their model could provide an explanation for each effect and the interactions between each of those effects. [PERSON] [21] compared consensus, consistency, and measurement approaches to estimate the rate of agreement between different decision-makers (in this case referred as judges or raters). The author analyzed the different advantages and disadvantages as well as the methods to compute both consensus, consistency, and measurement estimates for interrater reliability.