MATHEMATICAL MODELING OF AI-DRIVEN PEACE PREDICTION SYSTEMS FOR FUTURE GLOBAL SECURITY

Abstract

As Kenya navigates a rapidly evolving socio-political landscape, artificial intelligence (AI) presents a transformative opportunity to enhance national security by enabling the anticipation and prevention of conflicts before they escalate. This study explores the development of a mathematically grounded AI-based peace prediction model specifically adapted for Kenya. Unlike existing systems, which often overlook the nuanced and dynamic nature of localized tensions, this model integrates game theory, Markov chains, and Bayesian networks to create a predictive framework attuned to Kenya’s unique conflict drivers including ethnic tensions, political polarization, economic inequality, and resource-based disputes. Historical data from Kenyan conflict events and regional trends were analyzed, incorporating AI-driven indicators such as electoral unrest and inter-communal clashes. The model was trained using records from ACLED, UCDP, and KNBS. Implemented using Python, TensorFlow, and Netica, the system achieved 89% accuracy within a six-month prediction window. Compared to existing early- warning systems, it improved precision by 17% and reduced response latency by 22%. The study advocates for a Kenyan AI Peace Lab and integration of predictive models into national peace strategies.