Ramón Cristhian Uzeta-Obregon

An artificial intelligence procedure (AIP) model is presented to generate a robust multicriteria ranking for decision-making in problems with a large number of criteria, which jointly uses genetic algorithms, the OWA method, and SMAA. The main contribution of this AIP model is to reduce the cognitive effort of the decision-maker in determining the weights of the criteria involved in the decision problem, since, with the proposed model, these weights are inferred according to the decision-maker’s preferences, which can result in a ranking of the alternatives called a robust ranking. Robustness provides the expert with the certainty that, despite variations in their preferences and, therefore, in the weights inferred by the model, the presented robust ranking will not undergo significant changes, giving stability to the decision-making solution in a stable environment and reflecting the decision-maker’s preferences. This is one of the main contributions of applying the AIP model, unlike other models that do not provide a robust solution, as described in the application of the proposed AIP model to a multicriteria decision problem with many criteria, such as the state competitiveness analysis problem presented by the Mexican Institute for Competitiveness (IMCO).

The main objective of this article is to present the formulation of a Capital Asset Pricing Model ordered weighted average CAPM_OWAand its extensions, called CAPM-induced OWA (CAPM_IOWA), CAPM Bonferroni OWA (CAPM_Bon-OWA), and CAPM Bonferroni-induced OWA CAPM_Bon-IOWA. A step-by-step process for applying this new proposal in a real case of formulating investment portfolios is generated. These methods show several scenarios, considering the attitude, preferences, and relationship of each argument, when underestimation or overestimation of the information by the decision maker may influence the decision-making process regarding portfolio investments. Finally, the complexity of the method and the incorporation of soft information into the modeling process lead to generating a greater number of scenarios and reflect the attitudes and preferences of decision makers.

Attracting foreign investment is essential for the competitiveness and prosperity of nations. When deciding where to invest, an investor may be interested in considering specific criteria for investing or doing business and preferences for those criteria. In the same way, when evaluating the situation of a country to attract investment, consider different aspects to determine its ease of doing business. The MultiCriteria Decision-Making (MCDM) methodology is suitable for evaluating nations according to their ease of doing business due to the multifactorial elements of each nation. This work applied the ELECTRE-III method; it evaluated 190 nations based on the decision-maker’s preferences, giving different importance to the ten criteria considered in the World Bank’s Doing Business 2020 study. The results with this methodology show better-positioned nations in the ranking compared to the report presented by the World Bank Group.