Description: Linguistic variables are concepts that represent values in the form of words or phrases instead of numbers, and they are fundamental in the field of fuzzy logic and decision-making. These variables allow modeling situations where information is not precise or is subject to subjective interpretations. Unlike traditional variables, which are expressed numerically, linguistic variables use terms that describe qualities or states, such as ‘tall’, ‘short’, ‘hot’, or ‘cold’. This characteristic makes them especially useful in contexts where uncertainty and ambiguity are common, such as risk assessment, strategic planning, and qualitative data analysis. Linguistic variables are structured in fuzzy sets, where each term can have a degree of membership that varies between 0 and 1, allowing for a more nuanced representation of reality. Their relevance has increased with the advancement of explainable artificial intelligence, as they facilitate the interpretation of complex models by translating numerical results into understandable human language. In summary, linguistic variables are powerful tools for modeling and decision-making in uncertain environments, providing a bridge between mathematical logic and human understanding.
