An analysis of symbolic linguistic computing models in decision making

TLDR

Experts in complex decision problems often express knowledge in natural language, which is inherently vague, prompting the use of fuzzy logic and computing‑with‑words approaches to manage uncertainty. The paper reviews the link between decision making and computing‑with‑words, examining whether symbolic linguistic computing models used in decision making fit within the CWW paradigm. It surveys symbolic linguistic computing models applied to decision making, assessing their alignment with the computing‑with‑words framework. The study highlights the role of linguistic information in computing‑with‑words.

Abstract

It is common that experts involved in complex real-world decision problems use natural language for expressing their knowledge in uncertain frameworks. The language is inherent vague, hence probabilistic decision models are not very suitable in such cases. Therefore, other tools such as fuzzy logic and fuzzy linguistic approaches have been successfully used to model and manage such vagueness. The use of linguistic information implies to operate with such a type of information, i.e. processes of computing with words (CWW). Different schemes have been proposed to deal with those processes, and diverse symbolic linguistic computing models have been introduced to accomplish the linguistic computations. In this paper, we overview the relationship between decision making and CWW, and focus on symbolic linguistic computing models that have been widely used in linguistic decision making to analyse if all of them can be considered inside of the CWW paradigm.