Dr. León-Castro, Ernesto

The main aim of this paper is to propose a new aggregation operator to improve the evaluation of the transparency index. This new operator is called the prioritized induced ordered weighted average weighted average (PIOWAWA) operator. The main characteristics of the PIOWAWA operator are that it allows considering the degree of importance, reordering and weight factors given to the information in the same formulation by the decision maker. A mathematical application is performed using a Colombia transparency case. The findings highlight that according to the operator used, there are significant changes in the ranking. The main implications are given by using these aggregation operators for the generation of scenarios by considering the changes in the allocation of weights, the level of importance and the ordering of information simultaneously.

The objective of the paper is to present an extension of the ordered weighted average (OWA) operator, probability OWA (POWA) operator, distance measures and Pythagorean membership grades. These new operators are called the Pythagorean membership grade OWA distance (PMGOD) operator and the probabilistic Pythagorean membership grade OWA distance (PPMGOD) operator. This extension includes in one formulation the ability to measure the ideal vs. real situation of the distance operators, the weighting vector and reordering step of the OWA operator and the way in which it includes uncertainty based on the satisfaction of the criteria of the Pythagorean membership degree. These propositions are applied to new business ventures in the city of Colombia, where the decision maker can express his/her concerns about the uncertainty and probabilities of the current business environment and, thus, generate a better decision-making process.

Fuzzy systems in innovation and sustainability are important topics in literature nowadays. A lot of new formulations in fuzzy systems are being made including interesting applications in different topics. The aim of this special issue is to present different works made in this line of research that were presented in the IV International Congress of Innovation and Sustainability (ICONIS).

Estimating and forecasting volatility is an important issue for financial decision-makers. Therefore, it is important to build models that adapt to the current characteristics of the time series. The ordered weighted average (OWA) has some extensions that provide interesting ways to adapt to these characteristics. This work proposes a new application that uses the simple linear regression (LR) and OWA operators in the same formulation. We use the heavy ordered weighted average (HOWA), the prioritized ordered weighted average (PrOWA), the probabilistic ordered weighted average (POWA) and their combinations with induced cooperators. The main idea in linear regression with OWA operator is to obtain an estimate and forecast that can be adaptable to situations of uncertainty and information known to the decision maker. The work analyzes the applicability of this new approach in a problem regarding exchange rate volatility forecasting, where the operators that we can use in high or low seasons are located and thus generate ranges.