Understanding the dynamics of conflicts in latin america
an approach from machine learning
DOI:
https://doi.org/10.22451/5817.ibj2023.vol7.1.11076Abstract
The purpose of this document is to identify patterns in conflicts in Latin America from 1989 to the present. The article assumes that clustering can be used to achieve a greater systemic understanding of the correlations between politics, economics, and conflict. It starts from the assumption that the variables used are part of an interactive system with correlations yet to be understood. Clustering is the tool used to classify entities into groups to search for explanations based on cross-sectional characteristics of the objects in which they are integrated; thus, the analysis seeks a more tangible explanation of the complex links between economic, human development and conflict-related variables. Data from the Uppsala Conflict Dataset Program are used in the analysis to categorise actors present in conflicts based on a series of characteristics.
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