Dr. León-Castro, Ernesto

The aim of this paper is to present the application of the OWA operator and some of its extensions in the calculation of continent and global tax revenues. The idea is to present how the analysis of an important economic indicator can vary depending on how the information is aggregated. An example was employed based on the Organization for Economic Co-operation and Development (OECD) database using 111 countries that were divided by continent, and then the global tax revenue was calculated using different aggregation operators. Different analyses can be carried out by governments and enterprises to improve decision making and fiscal politics.

The paper analyses the strategic planning in the industrial context of Bogota, Colombia during COVID-19. Because of the different perspectives and subjectivity of the topic, the objective is to use an innovative methodology based on different multi-criteria decision-making (MCDM) characteristics and fuzzy logic to analyse the problem. The methodologies used were the Bonferroni ordered weighted average (OWA) operator and the Pitchat algorithm. The study was based on five sectors with seven strategic scopes. Among the main results was possible to visualise that all the sectors find the most important scope in marketing and the less important scope in Formalisation. Finally, some specific analyses of the results for each sector are presented to visualise how the scopes of the strategic planning can change depending on the sector.

The main purpose is to propose a new method for approximating the average interest rate using an ordered weighted average. The ordered weighted average (OWA), generalized GOWA operator and interval numbers are studied to develop this method. Using average interest rate formulation combined with aggregations operators and interval numbers presented above is the proposed weighted average interest rate (OWAIR) operator. For mathematical application, the deposit interest rates of the countries with 2020 data on the World Bank website are carried out. Finally, it should be noted that changes in the maximum, minimum, and average values are considered in the use of training for each region, which can be helpful for investors’ decision-making.

The manuscript is a digest, which puts forward findings from previous research papers, combined with new proposals. Approaches comprise two full models' derivation for photovoltaic (PV) systems energy conversion predictability. It brings in several models for key physical observables formulated as functions of the operating conditions. The proposals encompass mean spectral reflectance, coefficient for reflections and spatial geometry, incident angular losses factor, angular losses, and fill factor along with its coefficient of temperature. Applying the superposition principle, these models are integrated into two full approaches for performance predictability. The underlying physics description is mathematically consistent with experimental measurements of the physical observables involved, reported in other studies. To the authors' knowledge, these full models have been reported previously nowhere. Simulation results from the more inaccurate of two full models show good agreement of these findings with the experimental evidence, reported of its performance. The resulting key performance indicators (KPIs), after simulating a grid-connected PV system located in Cuba, yield 1.61%, 13.10%, −1.61%, 2.02%, and 0.81 of MAE, MAPE, MBE, RMSE, and R2, respectively, which they confirm the model's good behavior. Approaches formulations, as functions of solar irradiance and module temperature, its derivations, applications, and model's simulation results are considered the main manuscript novelties.

This paper introduces the multiple linear regression heavy ordered weighted average (MLR-HOWA) operator. On the MLR-HOWA operator, the beta values are obtained with the use of the HOWA means. In that sense, it provides a new range of possibilities by under or overestimating the result based on the decision maker’s expectations and knowledge. Therefore, the MLR-HOWA provides a forecasting tool that can analyze multiple scenarios from minimum to maximum. The main properties and two extensions using induced and generalized variables are also presented. An application in exchange rate forecasting based on inflation and interest rate as independent variables for five Latin American countries is submitted. Among the main results, it is possible to identify that the forecasting error is reduced when different combinations of MLR with OWA operators are done.

This article presents the process to build a regional employability indicator based on the ordered weighted average operator. The objective is to generate an indicator that can include different weighting vectors to analyze the information based on an analysis made from States to Regions to Nation. The main advantage of doing this type of indicators is that it is possible to made different politics for each State/Region instead of just analyze the whole Nation information. Also, based on the reordering step of the OWA operator is possible to obtain the maximum and minimum results. An example based on the National Occupation and Employment Survey (ENOE) 2020 survey conducted by the National Institute of Statistics and Geography (INEGI) in Mexico was done. Among the main results is possible to visualize the main characteristics of each Region.

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.

Interval numbers are widely used in many fields to provide information about different scenarios. This paper presents several new uncertain average formulations using the ordered weighted average, prioritized, probabilistic and induced operators. First, the work introduces the uncertain prioritized induced probabilistic ordered weighted average (UPIPOWA) operator that its main applicability is in complex group decision making problems. Also, a wide range of special cases and extensions using quasi-arithmetic means are presented, such is the case of the quasi-arithmetic UPIPOWA (QUPIPOWA) operator. The study analyzes the applicability of this new approach in economic variables, specifically are imports and exports. Particularly, the paper focuses on measuring the imports and exports for Latin America for 2017

This paper presents applications of computational intelligence tools in investment decision making processes. The aim is to build effective stock portfolios, for diverse risk-oriented individuals, including both, objective and subjective inputs. The proposed methods include the analysis of 5 different stock shares from companies that are in the Nasdaq 100 index, namely: Microsoft, Amazon.com Inc, MercadoLibre Inc, Intel Corporation and Facebook Inc using the ordered weighted average distance (OWAD) operator, the ordered weighted averaging adequacy coefficient (OWAAC) operator and the ordered weighted averaging index of maximum and minimum level (OWAIMAM) operator. Results show that a risk seeking investor would prefer stocks that display the behavior of the analyzed dataset of Amazon and a conservative investor would prefer Intel. The main advantage of the proposed methods is introducing multiple objective and subjective data in a single formulation that includes risk aversion, aptitude, expectations, and investment inputs, providing a wider representation of options and scenarios.

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.