Hot take: "we predicted the Arab Spring with math" π«
What we actually did: built a stress index, tested it on 3 countries, got correct classifications, and wrote 15 pages of limitations explaining why this proves almost nothing yet.
Science is slow. That's the point.
doi.org/10.5281/zeno...
If you had 18 months of warning before the next humanitarian crisis β
what would you do with that time?
That's the question CMH is trying to answer.
New from FICSS: "The Lyapunov Wall" β why mathematics says we can't predict the future beyond ~5 years, and what we can do instead.
Starts with Asimov. Ends with something more useful than science fiction.
galenfontaise.substack.com/p/the-lyapun...
Full guide: www.ficss.institute/working-pape...
What if Saudi Arabia had been a semi-authoritarian regime instead of a full autocracy in 2011?
Our model says: it would have ranked as the MOST revolutionary country in the region.
One variable. +0.58 shift. Completely different prediction.
That's the anocracy effect.
doi.org/10.5281/zeno...
Saudi Arabia had HIGHER youth unemployment than Egypt in 2010.
Higher internet penetration than Tunisia.
And yet: no revolution.
Why? Because math says regime type isn't just another variable β it's the multiplier that determines whether stress becomes uprising.
New paper π
doi.org/10.5281/zeno...
The Arab Spring warning signals were in public databases.
World Bank. ILO. Freedom House.
What was missing wasn't data.
It was the framework to read it β and the will to act.
With A8, the axiomatic foundation is complete.
A1-A8 define what Computational Macrohistory IS:
A science that acknowledges limits, quantifies uncertainty, and submits to empirical test.
The theory is set. The empirical work begins.
galenfontaise.substack.com/p/the-test-o...
5/5
The backtesting protocol:
1. Train on past (e.g., 1950-2000)
2. Predict held-out period (e.g., 2001-2020)
3. Compare predictions vs outcomes
4. Report ALL results
No cherry-picking. No hindsight adjustments.
Transparent track records build credibility.
4/5
The metrics:
- Brier Scoreβare probabilities calibrated?
- AUC-ROCβcan we distinguish cases?
- Calibration plotsβdo 70% predictions happen 70% of the time?
And the hardest part: PUBLISHING FAILURES.
Not just successes. Everything.
3/5
The requirement:
Every model must generate testable predictions:
Pβ(X(t+Ξt) | X(t))
Not "instability is possible" but "P(revolution) = 45% Β± 10% within 3 years."
Specific. Temporal. Observable. Independent.
If it can't fail, it's not science.
2/5
𧡠What separates science from storytelling?
Both explain the world. Both can be compelling.
But only science can be WRONG.
Axiom A8βComputational Falsifiabilityβcompletes the CMH foundation.
1/5
Libya and Tunisia both ousted their dictators in 2011.
One built a democracy. One became a civil war.
The difference? The invisible structural scaffolding underneath β institutions, state capacity, elite consensus.
CMH measures it.
Very interesting!
The hard limit isn't computing power. It's chaos theory.
Small errors compound exponentially. After ~10 years, even perfect models tell you nothing.
Calibrated probability in the 1-5 year window. That's enough to matter.
This is why CMH requires:
- Multiple predictions
- Track records over time
- Calibration analysis (do 60% predictions come true 60% of the time?)
One prediction proves nothing. A pattern of predictions reveals everything.
#Probability #Forecasting #Statistics #Methodology
tinyurl.com/4s5dxur4
Both outcomes were in the forecast. We assigned probabilities to each.
Judging probabilistic predictions by single outcomes is like judging a poker player by one hand. You need many hands to see if they're skilled.
"There's a 60% chance of instability" doesn't mean "instability will probably happen."
It means: in 100 similar situations, roughly 60 would see instability and 40 wouldn't.
If instability occurs, the prediction wasn't "right."
If stability persists, the prediction wasn't "wrong."
This is it.
The moment we've been waiting for.
Go Canada, GO! π¨π¦π
#TeamCanada #GoCanadaGo #Olympics2026 #Hockey #WinterOlympics2026
Overconfidence: claiming precision at long horizons.
Underconfidence: refusing to predict when short-term signal exists.
Calibration: matching confidence to horizon.
That's the discipline.
galenfontaise.substack.com/p/the-horizo...
#Forecasting #Prediction #DataScience #Methodology
5/5
What survives at long horizons?
β Demographics (decades of inertia)
β Physical constraints (geography)
β Cyclical patterns
β Distributions, not points
β Directions, not magnitudes
Structure persists. Specifics don't.
4/5
Realistic horizons for social prediction:
1-2 years β quantitative (70-80%)
3-5 years β scenarios (60-70%)
5-10 years β trends only (50-60%)
>20 years β speculation
The trade-off is fundamental: PRECISION vs HORIZON.
Pick one.
3/5
The formula:
π(t) = πβ Β· e^(βΞ»t)
Accuracy decays EXPONENTIALLY.
Half-life of prediction:
- Weather: ~5-7 days
- Economics: ~1.5-2.5 years
- Politics: ~2-3.5 years
After a few half-lives, you're guessing.
2/5
𧡠Weather forecasters predict tomorrow with 95% accuracy.
Next week: 70%.
Next month: 50%.
This isn't failure. It's physics.
The same applies to social systemsβand Axiom A7 quantifies exactly how prediction fades.
1/5
Can insights from Rome be applied to USA?
A historian doubts, but a physicist seeks universal patterns.
CMH says it depends on structural likeness.
Tunisia and Egypt 2011 may compare, but Rome 400BC and USA 2026?
Not so much.
History guides the future only when structures match.
tinyurl.com/4s5dxur4
The next major crisis is probably already visible in the data.
The question is whether anyone is building the tools to see it.
Guide: π www.ficss.institute/working-pape...
Substack: π galenfontaise.substack.com
7/7
#ComputationalMacrohistory #FICSS
We're a small research institute doing genuinely open science.
All working papers on Zenodo. Weekly updates on Substack. Methodology published for scrutiny.
If you're interested in quantitative history, complexity science, or early-warning systems β we'd love to have you along.
6/7
Today we're publishing our public guide to the CMH framework β free, open, written for anyone curious enough to engage with it.
10 parts. 7,000 words. The Arab Spring, Rome, the mathematics of chaos, and why honest uncertainty beats false confidence.
5/7
The honest limits:
β Black swans: can't predict specific ones
β Exceptional individuals: the model breaks
β Long-range forecasts: mathematically impossible beyond ~10 years
β Perfect data: doesn't exist, never will
Science that admits its limits is science you can trust.
4/7
25 variables across five categories (demographics, economics, politics, social structure, collective psychology), combined into a Political Stress Index.
Tunisia 2010: PSI β 7.5.
Saudi Arabia 2010: PSI β 4.2.
One had a revolution. One didn't.
The structural conditions predicted it.
3/7
Large-scale social crises follow measurable structural patterns.
Not destiny. Not prophecy. Probability β calculated from historical data, tested against reality, revised when wrong.
2/7
