Multi-Criteria Decision-Making and Machine Learning Techniques: A Multidisciplinary Analysis of the World Military Scenario

Abstract

Military development is increasingly related to strategic independence and technological advantages, representing an important factor in countries’ scientific, technological, and economic development. In this context, the present work proposes a methodology for evaluating the world’s military power based on the integration between Multi-Criteria Decision-Making (MCDM) and Machine Learning. To this end, data from the military, economic, and strategic spheres of 140 countries were analyzed and evaluated considering 12 quantitative variables. Considering the large volume of data to be analyzed and establishing a ranking of countries, the Principal Component Analysis (PCA), an unsupervised Machine Learning technique, was used. PCA allowed the analysis of 140 countries, identifying three factors representing the collective behavior of the 12 original variables, providing a world ranking of military power. The results were compared with specialized rankings on the subject and proved robust and reliable. As a contribution to the academy and society, the authors highlighted that this work fills the gap regarding the application of PCA in military problems to classify the observations, emphasized that the proposed methodology can be replicated in the most diverse problems and serve as input in other MCDM and data science techniques. In addition, the multidisciplinary of PCA allows the technique to support the decision-making process in the most diverse areas of knowledge that involve data analysis in tactical, operational, and strategic problems.