Confidence Based Consensus in Environments with High Uncertainty and Incomplete Information
Raquel Ureña, Francisco Chiclana, Hamido Fujita, Enrique Herrera‐Viedma · Frontiers in artificial intelligence and applications · 2017
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, is where Intuitionist fuzzy preference relations plays a key roll 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 mechanism to cope with missing informations are more than necessary. In this contribution, we present a new GDM approach able to estimate the missing information and at the same time provide a mechanism to bring closer the experts opinions in a iterative process in which the experts confidence plays a key role.