ARTIFICIAL NEURAL NETWORK MODELING OF CRIMINALITY AND VICTIMOLOGY IN INDIGENOUS MUNICIPALITIES OF MEXICO: A NON-LINEAR APPROACH TO NORMATIVE AUTONOMY AND CRIME STABILITY

Abstract

This study analyzes the relationship between criminal dynamics and indigenous normative systems in Mexican municipalities through the application of Artificial Neural Networks (ANNs). The research is based on a mixed-methods, non-experimental, and longitudinal design using secondary data from official sources between 2015 and 2025. The central objective is to evaluate whether normative autonomy and community-based governance structures function as inhibitory factors of criminal variability, particularly in contexts characterized by structural poverty.

The ANN model integrates multiple constructs, including crime stability, community cohesion, marginalization, and crime typology, operationalized through standardized indicators. The architecture consists of input, hidden, and output layers, where synaptic weights represent the relative influence of each variable. Model performance metrics indicate high predictive accuracy and robustness, allowing the identification of non-linear relationships that are not captured by traditional statistical approaches.

The findings reveal that indigenous municipalities exhibit significantly greater stability in crime rates, associated with higher weights assigned to normative autonomy and social cohesion variables. In contrast, structural poverty shows limited explanatory power within the model. These results challenge conventional criminological assumptions and support perspectives from legal anthropology and legal pluralism, emphasizing the role of community-based regulatory systems in maintaining social order.

The study concludes that indigenous normative systems constitute effective mechanisms of social control, capable of mitigating criminogenic factors through collective governance and restorative practices. The integration of machine learning techniques with socio-legal analysis provides a novel framework for understanding criminality in culturally diverse contexts and offers relevant implications for public security policy and interdisciplinary research.