Convergences and Divergences in the 2024 Judicial Reform in Mexico: A Neural Network Analysis of Transparency, Judicial Autonomy, and Public Acceptance
This study utilizes neural networks to evaluate the 2024 judicial reform in Mexico, a proposal designed to overhaul the judicial system by increasing transparency, judicial autonomy, and introducing the popular election of judges. The neural network model analyzes both converging and diverging factors that influence the reforms viability and public acceptance. Key areas of convergence include enhanced transparency and judicial autonomy, which are seen as improvements to the system. However, major points of divergence, such as the high costs of implementation and concerns about the legitimacy of electing judges, pose significant challenges. By integrating variables like transparency, decision quality, judicial independence, and implementation costs, the model predicts levels of public and professional acceptance of the reform. The neural networks multilayered structure allows for the modeling of complex relationships, offering predictive insights into how the reform may impact the Mexican judicial system. Initial findings suggest that while the reform could strengthen judicial autonomy, the risks of politicizing the judiciary and the financial burden it entails may reduce its overall acceptance. This research highlights the importance of using advanced AI tools to simulate public policy outcomes, providing valuable data to guide lawmakers in refining their proposals.