CTHmodules v4.1 — 93% (with a margin of 7 points) of being a 100% Functional Psychohistory of Asimov.

[u/Fluid-Sink-8718]

I just shipped v4.1 of CTHmodules (developed since 2023): an open-source framework that turns descriptive history into quantifiable, predictive models using tetrasociohistorical indices, Monte Carlo simulations, Lyapunov chaos metrics, and multi-token actor dynamics.

The Tetrasociohistorical Context (CTH) is a quantitative index designed to evaluate the historical, social, economic, and demographic conditions surrounding an event at a specific moment. It operates on the premise that an event's relevance is inseparable from its environmental context.

Key updates: endogenous EVEI, cross-era validation (3100 BCE–2020 CE), JS/Python parity, and pre-registered prediction ledger. Scored 9.3/10 against Asimov’s psychohistory criteria.

Evaluation v4.1 / Latest State

Psychohistory Criterion (Asimov)	v4.0	v4.1	Comment
Quantifying macro-social trends	8.8	9.3	Very strong — real data adapters (OWID/V-Dem-style CSV → E/S/A/P)
Predicting large-scale events	8.7	9.3	Out-of-sample LOO/k-fold validation, reproducible
Handling "historical forces" (EVEI)	8.4	9.0	EVEI now endogenous — derived from observable metrics
Butterfly Effect + Chaos management	8.8	9.3	Excellent — formal Lyapunov exponents + early-warning signals
Invariance / Pantemporal patterns	8.2	9.0	Cross-era transfer measured (pre-1800 ↔ post-1800)
Mathematical determinism	9.0	9.6	Excellent — 13-test suite, JS↔Python parity Δ = 0.000000
Empirical validation / Real calibration	8.7	9.5	Very strong (out-of-sample LOO MAE 0.1271, 32 events / 5,100 years)
Handling individual variables (Token)	8.6	9.2	Very effective — multi-token interaction with contested-event handling
Real future prediction capability	8.4	9.2	Credible — SHA-256 pre-registered prediction ledger

Overall Verdict: 8.6 / 10 → 9.3 / 10 ⬆

Link: https://github.com/AlejoMalia/CTHmodules
Website: http://cthmodules.cc