CMH models causes, not triggers. We can identify dry tinder. We cannot predict which spark will ignite it.

This isn't a limitation. It's honesty about what science can and cannot do.

#ComputationalSocialScience #History #Prediction #Causality

Both had youth unemployment. Both had authoritarian regimes. Both had social media. Both saw the same news.

Triggers are unpredictable—the specific spark, the specific moment, the specific person.

Causes are structural—youth bulges, inequality, regime illegitimacy, state capacity.

First post explores what Computational Macrohistory is (and isn't):
galenfontaise.substack.com/p/what-is-co...

If you're interested in evidence-based approaches to social dynamics, I'd love your thoughts.

#ComplexSystems #DataScience #QuantitativeSocialScience #Research

How:
Mathematical modeling, statistical analysis, complexity science, historical databases—all with rigorous validation and transparent uncertainty.
Current focus:
Arab Spring case study. Could we predict which countries would experience revolution in 2010-12 based only on structural conditions?

If you're into quantitative history, complex systems, cliodynamics, or simply understanding why societies collapse — glad to have you here.

-Galen Fontaise

5/5

I'll post research updates, methodological reflections & findings here.
For more:
📰 Substack → galenfontaise.substack.com
🔗 LinkedIn → www.linkedin.com/in/galenfont...
📄 Orcid → orcid.org/my-orcid?orc...

4/5

The framework rests on 8 foundational axioms defining when historical systems become scientifically tractable.

Currently in empirical validation: Arab Spring 2010-2012 as proof-of-concept case study.

3/5

CMH applies math, statistics & dynamical models to history to identify recurring patterns and compute probability distributions for critical socio-political events.

No deterministic forecasts — honest probabilities only.

2/5