Computing with complex linguistic expressions for decision making under uncertainty

Abstract

Decision making is a common process for humans to carry out real-world activities that are often defined in situations where information is imprecise. There are different approaches to deal with this uncertainty, being the fuzzy linguistic approach one of the most widely used since it provides satisfactory results by modeling uncertainty using linguistic variables. The use of linguistic information involves computing with words processes. Among the existing linguistic representation models, the extended comparative linguistic expression representation with symbolic translation stands out because it improves the accuracy of the computational process while maintaining the interpretability of the results. To apply this model to decision-making problems, it is necessary to define new aggregation operators, similarity measures, multicriteria decision-making models, consensus models, etc. These proposals introduce state-of-the-art improvements to this research report and effectively address some current challenges in decision making.