NCode: Encoding Non-Homogeneous Information into Type-2 Fuzzy Words in Decision-Making

Existing computer-aided decision models focus on multiple aspects of a decision-making process. Most aim at modeling linguistic information input by humans, citing their inherent cognitive facets. While this is true, most humans do not perform conscious decision-making with only linguistic features. Meaning, human knowledge consists of all diverse brackets of information such as numeric, interval, and linguistic. Each such category can further diversify into complex expressions or even single entities such as words. This paper deliberates through non-homogeneity of information obtained from decision-making individuals in formal decision-making settings, by encoding such data into type-2 fuzzy sets for enhanced representation. More formally, an ideation of a linguistic encoding model wherein different modalities of information are encoded into type-2 fuzzy set based models for effective decision making is proposed for the first time in the literature. Consequently, methods of obtaining type-2 fuzzy sets for representation of input data are also compared to provide recommendations for the same.