Dynamic Global Alignment Model (DGAM): An AI-Driven Framework Advancing Computational Geopolitics and Strategic Statecraft

This record presents a pioneering research contribution introducing the Dynamic Global Alignment Model (DGAM), a state-of-the-art artificial intelligence framework designed to simulate, forecast, and optimize national and international strategic alignments in the 21st century. Leveraging reinforcement learning, Bayesian networks, and advanced data analytics, DGAM provides reproducible, data-driven insights for policymakers, diplomats, and researchers. The model integrates multi-domain indicators—including diplomatic, economic, military, and energy-related metrics—offering adaptive solutions to rapidly changing global scenarios such as alliance realignment, trade shocks, and security challenges. Key features include: Robust simulation and validation using historical geopolitical datasets; Novel composite indices quantifying national influence and systemic exposure; Scenario forecasting and risk quantification supporting transparent, informed decision-making. This resource is intended for academic, policy, and AI governance communities globally, promoting fair, open, and high-impact computational social science research. All methods, data, and code comply with international open science standards, ensuring transparency, interoperability, and reusability