Dr. León-Castro, Ernesto

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.

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.

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 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.

Financial markets have been characterized in recent years by their uncertainty and volatility. The price of assets is always changing so that the decisions made by consumers, producers, and governments about different products is not still accurate. In this situation, it is necessary to generate models that allow the incorporation of the knowledge and expectations of the markets and thus include in the results obtained not only the historical information, but also the present and future information. The present article introduces a new extension of the ordered weighted averaging (OWA) operator called the Bonferroni probabilistic ordered weighted average (B-POWA) operator. This operator is designed to unify in a single formulation the interrelation of the values given in a data set by the Bonferroni means and a weighted and probabilistic vector that models the attitudinal character, expectations, and knowledge of the decision-maker of a problem. The paper also studies the main characteristics and some families of the B-POWA operator. An illustrative example is also proposed to analyze the mathematical process of the operator. Finally, an application to corn price estimation designed to calculate the error between the price of an agricultural commodity using the B-POWA operator and a leading global market company is presented. The results show that the proposed operator exhibits a better general performance than the traditional methods.

Purpose: The purpose of this paper is to identify new formulations for evaluating corporate social responsibility using different aggregation information operators. Design/methodology/approach: The ordered weighted average (OWA) operator and its extensions, the induced OWA (IOWA) and prioritized OWA (POWA) operators are used to generate a new score for a Mexican enterprise with the corporate social responsibility (CSR) distinction. Findings: The use of these operators allows for generation of different scenarios highlighting the relative importance of the elements. This information is useful for the government and companies to generate different evaluations depending on the specific characteristics of the region, state or municipality. Originality/value: The use of aggregation operators in the traditional CSR formulation is presented. Likewise, the application of these new strategies to evaluate CSR is presented in a Mexican enterprise case to understand the steps that should be followed if the OWA operator and its extensions are to be used.

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.

This paper presents the prioritized induced heavy ordered weighted average (PIHOWA) operator. This operator combines an unbounded weighting vector, an induced vector and a prioritized vector and can be applied to the group decision-making process where the information provided by each decision maker does not have the same importance. An application of this operator is done in governmental transparency in Mexico based on the Open Government Metric (OGM). Among the main results it is possible to visualize how the relative importance of each component can generate important change in the top 10 ranking.

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.