Contradiction debt & rupture

Each state is scored from live governance events classified as violations (state breaches of its obligations) or repairs (credible steps to make good). Two decayed stocks accumulate per country — a violation stock V and a repair stock R_eff (90-day half-lives) — and drive:

• Contradiction debt — the master instability quantity: D(t) = max(0, D(t‑1)·λ + cd_mult·Vₙₑw − repBonus·R_effₙₑw)
• Repair ratio ρ — how well repair keeps pace with breach: ρ = (R_eff·repBonus + R_base) / max(V′, ε), where V′ is the coverage-normalized violation stock (see below).
• Rupture band — stable ρ≥1.0 · strained 0.6≤ρ<1.0 · critical ρ<0.6 · imminent ρ<0.6 with debt rising.

Every state launches at a stable baseline and can only escalate into a rupture band once real contradiction-debt history has accumulated (a short warm-up window) — so scores build credibly from the ground up rather than reacting to a single day of coverage.

Rule of law as a repair engine

A functioning legal order continuously repairs contradictions even when no single "repair event" makes the news. Following the Structural Ontology of Law (SOoL / SimLex) model, each country carries a standing repair capacity R_base and architecture coefficients (cd_mult, repBonus, Remedy-node weight) interpolated between a collapsed and a rule-of-law order by its Legal Order Capacity (LOC). LOC is built from World Bank Worldwide Governance Indicators — LOC = 0.6·RuleOfLaw + 0.25·ControlOfCorruption + 0.15·Voice&Accountability. All forms of legal repair count — judicial review, remedy, nullification of unlawful acts, and accountability — not only political concessions.

Coverage-exposure normalization

Raw violation volume tracks how heavily a country is covered and aggregated, not just how troubled it is: a large state folds a whole subcontinent of routine local incidents under one flag, so it can accrue a large violation stock from sheer incident count even when each incident is minor. To keep the score a measure of institutional stress rather than media footprint, the violation stock is coverage-normalized — V′ = V / E_i, with E_i = (violation_count / reference)^½ clamped to [1, 6] and the reference set at the 75th percentile of per-country counts. A country is therefore scored on violation intensity per unit of coverage. This distinguishes many-routine-incident states (e.g. India, average violation severity ≈ 0.56) from few-but-severe ones (e.g. Sudan, ≈ 0.83): the former normalize down out of the rupture band, while genuinely severe states — whose stock comes from intensity, not volume — stay flagged. E_i ≥ 1 by construction, so normalization only ever relaxes a high-coverage score and never pushes a quiet country toward rupture.

Data

Governance events are ingested from WorldMonitor plus curated open feeds (see below) and classified by a local LLM into the SOoL violation/repair taxonomy with per-event severity and confidence. Scores update every poll cycle; debt accumulates over time.

Sources & citation

Koepsell, David R. “Repair Capacity and Collapse: A Mechanistic Early-Warning Model of Political Rupture.” Stability: International Journal of Security & Development (2026). DOI 10.33534/sta.1042.

The engine operationalizes the Structural Ontology of the Law (SOoL) — its 8-node Minimum Legal Chain, 13-type contradiction typology, and Irreversible Accountability Test — and the SimLex / SimEthica simulation program.