We extend the Computational Macrohistory (CMH) framework to an eleven-country analysis of structural preconditions for political instability in the Middle East and North Africa (MENA) region during 2000–2012, with particular reference to the Arab Spring uprisings of 2010–2011. Building on the three-country proof of concept presented in WP-2026-003, we construct a five-component Systemic Stress Index (SSI) for Algeria, Bahrain, Egypt, Jordan, Kuwait, Morocco, Oman, Saudi Arabia, Syria, Tunisia, and Yemen, drawing on variables capturing demographic pressure, income inequality, youth unemployment, regime type, and internet penetration. The SSI should be understood as a reduced empirical proxy designed to test whether core structural signals hypothesised by the CMH framework appear in historical data; it does not implement the full CMH dynamic system. Our results indicate that the SSI correctly classifies eight of eleven countries at the pre-event benchmark year of 2010, achieving a descriptive accuracy of 72.7% against a binary outcome variable distinguishing severe instability (revolution or civil war) from all other outcomes. This analysis is retrospective classification, not out-of-sample prediction. Group-level analysis reveals a mean separation of 0.290 standard deviation units between instability and stability clusters. Robustness analysis across six alternative weighting schemes demonstrates that the Base specification is among the most stable, with the Regime-focused scheme achieving marginally superior pairwise discrimination. Leave-one-component-out analysis identifies youth unemployment (E₄) and anocracy stress (derived from P₁) as the most discriminatively critical components. Three misclassified cases—Jordan, Egypt, and Syria—are discussed in detail; each reveals theoretically informative limits of the current five-variable specification rather than random error. The paper concludes by specifying the conditions under which the expanded research programme—targeting N ≥ 30 validated country-cases—would constitute genuine statistical validation of the framework.
Galen Fontaise (Mon,) studied this question.