Neural Nations
Experimental Seshat Equinox 2022 CAMS v2.3 Cross-Dataset

Seshat × CAMS — Cross-Dataset Validation

Comparing Seshat's Social Complexity Principal Component (SPC) against CAMS cross-layer coherence Λ(t) for overlapping societies. This page tests whether two independently constructed civilisational complexity measures agree on the shape of historical trajectories.

1. Dataset Overview
Seshat Global History Databank
35
NGAs
1,494
NGA-century rows
5,500
years covered

Equinox 2022 GitHub release. SPC = 1st principal component of 8 coded complexity variables (Hierarchy, Government, Infrastructure, Information, Money, Military, etc.). MilTech = sum of 6 military technology categories. Century-resolution, −3600 to 1900 CE.

Source: Turchin et al., Seshat Global History Databank. seshatdatabank.info

CAMS v2.3 Corpus
38
Societies
39,351
Node-year records
45
Series

Annual or 5-year resolution. Λ(t) = mean cross-node bond strength = mean over all 28 node pairs of Bij(t) = √(max(Vi+8,0)·max(Vj+8,0)) / 32. Covers CE 5 – 2026, with most modern datasets starting 1750+.

Extended overlap achieved: New CAMS scoring for Latium Vetus (CE 460–2010, 25-year resolution) gives 14 century-aligned comparison points against Seshat's Latium NGA (CE 500–1800). This is the first Seshat × CAMS comparison with near-Granger-ready sample size. The broader NGA explorer shows where further CAMS reconstruction could extend the analysis to additional NGAs.
2. Latium Vetus — Extended Cross-Validation: CE 460–1800

New CAMS scoring for Latium Vetus covers 62 time-steps at 25-year resolution (CE 460–2010), produced using the CAMS v2.3 rubric. Seshat's Latium NGA covers CE 500–1800 at century resolution. This yields N = 14 century-level matched observations — the largest Seshat × CAMS overlap yet assembled. The two datasets are fully independent: Seshat uses expert-coded historical variables; CAMS uses a structured LLM complexity rubric — no shared inputs.

CAMS Λ(t) — Latium Vetus, CE 460–2010 (25-year resolution, 62 observations)

Key events visible in the trajectory: Justinianic collapse (CE 535 nadir, Λ = 0.136), Carolingian rise (CE 785–810 local peak, Λ = 0.507), Black Death trough (CE 1360, Λ = 0.225), Renaissance peak (CE 1460–1510, Λ ≈ 0.598), Sack of Rome shock (CE 1535, Λ = 0.388), post-WWII boom (CE 1960, Λ = 0.586).

Dual-Axis: CAMS Λ(t) vs Seshat SPC — Latium, CE 500–1800 (N = 14)
Century-Level Data Table
Century CECAMS Λ(t) avgSeshat SPCSeshat MilTech
Correlation (N = 14)
Findings
  • Pearson r = −0.309, Spearman ρ = 0.000 (N = 14, p ≈ 0.28) — the two measures are statistically orthogonal over the CE 500–1800 period.
  • Seshat SPC for Latium is remarkably flat (range: 6.39–7.09) across 1,300 years — it tracks slow structural accumulation at century resolution, not coordination quality dynamics.
  • CAMS Λ(t) varies by a factor of 4.5× (0.136 to 0.598) over the same period — detecting coordination collapses and recoveries that the structural index does not register.
  • The Black Death (CE 1360) drives CAMS Λ to its lowest post-Roman value (0.225); Seshat SPC barely moves (6.55 → 6.52 → 6.52). CAMS is tracking the functional disruption; Seshat is tracking institutional persistence.
  • This orthogonality is a positive finding, not a failure: the two frameworks are measuring different dimensions of civilisational state — structural endowment (Seshat SPC) vs coordination quality (CAMS Λ). A society can hold high structural complexity while coordination quality collapses, and vice versa.
Phase I context (Rome CE 0–400): The earlier 5-point Rome comparison (Pearson r = 0.78, N = 5) captured a different regime — the terminal decline of a single peak state where both structural complexity and coordination deteriorated together. The new 1,300-year Latium Vetus dataset covers multiple regime changes (collapse, recovery, Black Death, Renaissance, Risorgimento) — a richer test environment that reveals the orthogonality between structural scale and coordination quality.
Statistical note: At N = 14 we can compute a meaningful correlation estimate (t = −1.13, df = 12). Granger causality requires N ≥ 15 post-lag — this dataset is one observation short. The 25-year resolution CAMS data (62 points) is Granger-ready for a CAMS-internal analysis; the Seshat century alignment constrains the cross-dataset test to N = 14.
3. Seshat NGA Explorer — All 35 Natural Geographic Areas
SPC Trajectory
NGA Metadata
All NGAs — SPC at Maximum (sparkline context)
4. Proposed Seshat NGA → CAMS Society Mapping

The table below lists proposed correspondences between Seshat NGAs and CAMS societies, ordered by temporal overlap quality. "Overlap" = the intersection of Seshat and CAMS time windows.

Seshat NGABest CAMS MatchSeshat RangeCAMS RangeOverlap (N centuries)Granger-ready?
LatiumLatium Vetus (CE 460–2010)−3600 → 1800CE 460–2010CE 500–1800 (N=14)Near — N=14 (1 short)
Paris BasinFrance (1785–2024)−3200 → 17001785–2024None (85y gap)No — no overlap
KansaiJapan (1850–2025)−600 → 18001850–2025None (50y gap)No — no overlap
Middle Yellow River ValleyChina (1900–2026)−2000 → 19001900–2026CE 1900 (N≈1)No — N<15
Susiana / S. MesopotamiaIraq / Iran−4000 → 19001900–2024CE 1900 (N≈1)No — N<15
Upper EgyptNo CAMS analog yet−3600 → 1700——No match
Deccan / Middle GangaIndia (if extended)−1500 → 1800——No match
Path to Granger: Two routes exist. (1) Extend CAMS backward — reconstruct Latium/Rome at century resolution from BCE to 476 CE using archival sources and the CAMS v2.3 rubric. This would yield N ≈ 40 usable Seshat-aligned centuries. (2) Extend Seshat forward — adapt the Seshat coding rubric to 20th-century polities in the CAMS corpus. Either approach would enable the full bidirectional Granger test (Λ → SPC and SPC → Λ) using granger_stationary_safe() from the validated tool suite.
5. Dataset Gap Visualisation
Temporal Coverage: Seshat NGAs vs CAMS Societies

Each bar shows the time span covered. Seshat NGAs are brown; CAMS societies are blue. The only vertical overlap is in the CE 0–450 window (Latium/Rome). This chart is the core motivation for the proposed backward-reconstruction programme.

6. Methodology
SPC — Seshat Social Complexity Index

SPC is the 1st principal component (PC1) of 8 coded complexity variables from the ImpSCDat sheet (imputed). The 8 components are:

  • PolPop — polity population
  • PolTerr — territory
  • CapPop — capital population
  • levels — administrative hierarchy levels
  • government — government specialisation
  • infrastr — infrastructure
  • writing — writing/information
  • money — monetary system

Higher SPC = more structurally complex polity. Range ≈ 2.3–8.3 in this corpus.

Λ(t) — CAMS Cross-Layer Coherence
Vi(t) = Ci + Ki + Ai/2 − Si Bij(t) = √(max(Vi+8,0)·max(Vj+8,0)) / 32 Λ(t) = mean over all 28 node pairs of Bij(t)

C = Coherence, K = Capacity, A = Abstraction, S = Stress. 8 nodes × 4 metrics. Bond strength Bij captures the geometric mean of two nodes' adjusted values. Λ(t) = mean coupling across all node pairs.

Range in Latium Vetus corpus: 0.136 (CE 535, Justinianic nadir) to 0.598 (CE 1460–1510, Renaissance peak). Rome CE 0–400 corpus ranged 1.31–2.88 under a different scoring rubric.

Why the two metrics should correlate

Both SPC and Λ(t) are designed to capture civilisational complexity, but from different theoretical angles. SPC captures structural endowment — what institutional and material resources a polity possesses. Λ(t) captures coordination quality — how well those resources are being integrated across functional layers.

The finding that Λ(t) declines faster than SPC during Rome's CE 100–400 period is theoretically meaningful: structural endowment persists (armies, roads, laws remain nominally intact) while cross-layer coordination degrades (fiscal stress, elite conflict, provincial fragmentation). This is precisely the CAMS prediction: coordination failure precedes structural collapse.

The Granger question — whether Λ(t) leads SPC decline, or vice versa — remains open pending sufficient overlap data.