Linguistic decision making with probabilistic information and induced aggregation operators

We develop a new model for linguistic decision making by using probabilistic information and induced aggregation operators. We introduce the induced linguistic probabilistic ordered weighted average (ILPOWA). It is a new aggregation operator that uses probabilities and OWA operators in the same formulation considering the degree of importance that each concept has in the formulation. Moreover, it uses complex attitudinal characters that can be assessed by using order inducing variables. Furthermore, we analyze an uncertain environment where the information can not be studied in a numerical scale but it is possible to use linguistic variables. We study this approach in a group decision making context where we use multi-person aggregation operators in order to deal with the opinion of several experts in the analysis. We focus on a problem regarding importation strategies in the administration of a country.