Linguistic Preference Modeling: Foundation Models and New Trends

Extended Abstract There are decision situations in which the information cannot be assessed precisely in a quantitative form but may be in a qualitative one, and thus, the use of a linguistic approach is necessary. For example, when attempting to qualify phenomena related to human perception, we are often led to use words in natural language instead of numerical values. As was pointed out in [2], this may arise for different reasons. There are some situations in which the information may be unquantifiable due to its nature, and thus, it may be stated only in linguistic terms (e.g., when evaluating the " comfort " or " design " of a car, terms like " good " , " medium " , " bad " can be used). In other cases, precise quantitative information may not be stated because either it is unavailable or the cost of its computation is too high, so an " approximate value " may be tolerated (e.g., when evaluating the speed of a car, linguistic terms like " fast " , " very fast " , " slow " may be used instead of numerical values). The linguistic approach is an approximate technique which represents qualitative aspects as linguistic values by means of linguistic variables [16], that is, variables whose values are not numbers but words or sentences in a natural or artificial language. Each linguistic value is characterized by a syntactic value or label and a semantic value or meaning. Since words are less precise than numbers, the concept of a linguistic variable serves the purpose of providing a measure of an approximate characterization of the phenomena which are too complex or ill-defined to be amenable to their description by conventional quantitative terms. Linguistic preference modeling consists in the use of the linguistic approach for solving decision making problems assuming that experts' preferences are expressed by means of linguistic preference relations. Its application in the development of the theory and methods in Decision Analysis is very beneficial because it introduces a more flexible framework which allows to represent the information in a more direct and adequate way when experts are unable to express it precisely. In this way, the burden of quantifying a qualitative concept is eliminated. In the literature, we may find many applications of linguistic preference modeling to solve real-world decision