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With the incorporation of web 2.0 frameworks the complexity of decision making situations has exponentially increased, involving in many cases many experts, and a potentially huge number of different alternatives, leading the experts to present uncertainty with the preferences provided. In this context, intuitionistic fuzzy preference relations play a key role as they provide the experts with means to allocate the uncertainty inherent in their proposed opinions. However, in many occasions the experts are unable to give a preference due to different reasons, therefore effective mechanisms to cope with missing informations are more than necessary. In this contribution, we present a new group decision making (GDM) approach able to estimate the missing information and at the same time implements a mechanism to bring the experts’ opinions closer in an iterative process in which the experts’ confidence plays a key role.

Confidence Based Consensus in Environments with High Uncertainty and Incomplete Information