A consensus model to detect and manage noncooperative behaviors in large-scale group decision making

Consensus reaching processes in group decision making attempt to reach a mutual agreement among a group of decision makers before making a common decision. Different consensus models have been proposed by different authors in the literature to facilitate consensus reaching processes. Classical models focus on solving group decision making problems where few decision makers participate. However, nowadays, societal and technological trends that demand the management of larger scales of decision makers, such as e-democracy and social networks, add a new requirement to the solution of consensus-based group decision making problems. Dealing with such large groups implies the need for mechanisms to detect decision makers' noncooperative behaviors in consensus, which might bias the consensus reaching process. This paper presents a consensus model suitable to manage large scales of decision makers, which incorporates a fuzzy clustering-based scheme to detect and manage individual and subgroup noncooperative behaviors. The model is complemented with a visual analysis tool of the overall consensus reaching process based on self-organizing maps, which facilitates the monitoring of the process performance across the time. The consensus model presented is aimed to the solution of consensus processes involving large groups.