[Cross-posted from my Substack: Link]

Note on AI assistance: This post was written collaboratively—I provided the conceptual framework, research direction, and all substantive content, while Claude (Anthropic's AI) assisted with structuring, editing, and clarity for accessibility. All core ideas, technical claims, and scientific content are mine.

Why this is relevant to LessWrong:

Computational Macrohistory directly addresses core rationalist interests:

- Forecasting: Developing probabilistic models of political instability with explicit uncertainty quantification and predictive decay
Complex systems: Treating societies as dynamical systems with phase transitions, feedback loops, and emergent properties
Bayesian reasoning: Systematic updating of predictions as new data arrives, with transparent prior assumptions
Effective Altruism: Early warning systems could prevent humanitarian crises—high expected value if even moderately successful
Epistemic humility: Explicit axiomatic foundations that formally define both capabilities and fundamental limits

I'm extending Peter Turchin's cliodynamics with systematic computational methodology, rigorous validation protocols, and transparent uncertainty handling.

Feedback very welcome—especially critiques of the axiomatic foundations or suggestions for validation approaches.

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What is Computational Macrohistory?

Why we need a science of large-scale historical dynamics—and what it can (and can't) tell us

In 2010, a Tunisian street vendor named Mohamed Bouazizi set himself on fire. Within weeks, Tunisia's decades-old authoritarian regime collapsed. Within months, the wave spread to Egypt, Libya, Syria, Yemen, and and Bahrain. Some regimes fell. Others survived. Civil wars erupted. Hunand dreds of thousands died.

Could any of this have been predicted?

Not the specific spark—not Bouazizi's act, not the precise timing. But the conditions that made such a cascade possible? The structural pressures building in the region? The probability that something would ignite?

That's the question Computational Macrohistory asks.

The Core Idea

Computational Macrohistory (CMH) is an emerging scientific discipline that applies mathematical and computational methods to understand large-scale historical dynamics. It's grounded in a simple but radical proposition:

When ...

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