Modeling Systemic Collapse and Decision Dynamics Using a Subsystem Balance Formula: A Pilot Framework with Monte Carlo Simulation

This paper presents a formal computational framework for modeling systemic collapse and decision dynamics in human societies. The approach is grounded in systems theory and the principle of subsystem balance: that societies become vulnerable to collapse when their constituent subsystems diverge significantly in stability. Three subsystems are modeled — Political (P), Economic (E), and Military (M) — each assigned time-varying stability values calibrated from historical secondary sources. A composite stability index C is derived from the mean and variance of subsystem values, and collapse probability P_collapse is estimated through a logistic transformation. Monte Carlo simulations introduce stochastic variability to quantify uncertainty across 1,000 iterations per year. The framework is applied to three historical cases: the Soviet Union (1970–1991), South Vietnam (1955–1975), and Yugoslavia (1980–2000). Simulated high-risk years align closely with documented periods of acute instability. Results demonstrate the utility of subsystem-based modeling as a tool for retrospective risk analysis and prospective scenario planning.