Dr. León-Castro, Ernesto

El proceso de toma de decisiones tiene una incidencia relevante en los resultados de las empresas, lo que ha llevado a desarrollar novedosos métodos que permitan evaluar bajo condiciones no controlables elementos subjetivos y racionales. En ese sentido, el objetivo principal de este trabajo estudia los operadores de agregación en la toma de decisiones en entornos inciertos. Se presentan dos metodologías que permiten agregar información, que se llaman operadores OWA y BON-OWA. La aplicación de estos operadores se realiza en la selección de lanzamiento de nuevos productos. La principal ventaja de estos operadores es que permiten capturar la actitud del decisor y la comparación e interrelación continua de la información. Así, se destaca el análisis holístico que ofrecen estos métodos sobre la toma de decisiones en incertidumbre, que permite integrar conceptos de la teoría administrativa y la teoría de la agregación en un caso aplicado, visualizando como la inclusión de la información genera cambios dentro de los rankings de selección de alternativas.

The present paper has the main aim to evaluate the competitive level of the regions of Mexico based on their performance on 10 main factors from 100 indicators. The methodology is based on the Multiple Criteria Hierarchy Process (MCHP) capable of analysing the performance of a subset and the comprehensive indicators, and how they impact the competitiveness of the region. An important aspect of the MCHP implemented is that it considers the interaction between criteria (indicators) and measure the performance of a large number of criteria. The main contribution to the research is with the identification of region with the worst level of competitiveness, and the factors are requiring more attention by the decision-makers.

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 the Pythagorean membership grade induced ordered weighted moving average (PMGIOWMA) operator with some particular cases and theorems. The main advantage of this new operator is that can include the knowledge, expectation, and aptitude of the decision maker into the Pythagorean membership function by using a weighting vector and induced variables. An application in financial knowledge based on a survey conducted in 13 provinces in Boyacá, Colombia, is presented.

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.

In this part of a series of two papers, we extend the theorems discussed in Part I for infinite series. We then use these theorems to develop distinct novel results involving the Hurwitz zeta function, Riemann zeta function, polylogarithms and Fibonacci numbers. In continuation, we obtain some zeros of the newly developed zeta functions and explain their behaviour using plots in complex plane. Furthermore, we provide particular cases for the theorems and corollaries that show that our results generalise the currently available functions and series such as the Riemann zeta function and the geometric series. Finally, we provide four miscellaneous examples to showcase the vast scope of the developed theorems and showcase that these two theorems can provide hundreds of new results and thus can potentially create an entirely new field under the realm of number theory and analysis.

Agricultural product prices are subject to various uncertainties, including unpredictable weather conditions, pest infestations, and market fluctuations, which can significantly impact agricultural yields and productivity. Accurately assessing and understanding price is crucial for farmers, policymakers, and stakeholders in the agricultural sector to make informed decisions and implement appropriate risk management strategies. This study used the ordered weighted average (OWA) operator and its extensions as mathematical aggregation techniques incorporating ordered weights to capture and evaluate the factors influencing price variation. By generating different vectors related to different inputs to the traditional formulation, it is possible to aggregate information to calculate and provide a new view of the outcomes. The results of this research can help enhance risk management practices in agriculture and support decision-making processes to mitigate the adverse effects of price.

The present research proposes a new method to analyze the sustainable development goals (SDGs) index using ordered weighted average (OWA) operators. To develop this method, five experts evaluated and designated the relative importance of each of the 17 SDGs defined by the United Nations (UN), and with the use of the OWA and prioritized OWA (POWA) operators, rankings were generated. With the results, it is possible to visualize that the ranking of countries can change depending on the weights related to each SDG because the OWA and POWA operator methods can capture the uncertainty of the phenomenon.

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.

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.