๐ Methodology & Scoring
How GMIIE computes scores, detects patterns, and validates its own reliability
Analytical Philosophy
GMIIE is designed around a principle of layered skepticism. Every data point passes through multiple validation stages before influencing any composite score. The system assumes all data sources may be biased, incomplete, or stale โ and adjusts its confidence accordingly.
The 5-ring architecture deliberately separates quantitative data (Ring 1) from qualitative signals (Ring 2), deployment reality (Ring 3) from structural analysis (Ring 4), and jurisdiction-level events from geopolitical dynamics (Ring 5). This separation prevents narrative contamination โ a policymaker's optimistic speech cannot inflate the system's assessment unless deployment data (Ring 3) corroborates it.
๐ง Scoring Pipeline
Ring Score Computation
Each ring computes a raw score from 0.0 to 1.0 based on its domain-specific inputs. Ring 1 aggregates quantitative financial signals. Ring 2 measures language drift velocity via NLP. Ring 3 tracks deployment stage progression. Ring 4 uses graph-theoretic fragility metrics. Ring 5 tallies geopolitical fracture events weighted by severity.
Confidence Dampening
Raw ring scores are adjusted by a confidence weight (0.0โ1.0) before being used in downstream analysis. This prevents low-confidence data from inflating system-wide assessments. The dampened score = raw score ร confidence weight. If Ring 2 has a raw score of 0.64 but a confidence weight of only 0.68, the dampened score is 0.44.
Confidence Normalization
The confidence weight itself is computed from five factors using a weighted geometric mean: data density (25%), source diversity (20%), historical validation (20%), recency decay (20%), and analyst confirmation (15%). This ensures that confidence is grounded in data quality, not arbitrary calibration.
NIG Score
NIG = Narrative Acceleration Index โ Infrastructure Deployment Index. Positive values indicate rhetoric is outpacing deployment ('policy theater'). Negative values indicate deployment is outpacing rhetoric ('silent rollout'). The rhetoric-reality gap metric captures this on a per-initiative basis.
Cross-Ring Conflict Detection
The system scans all 49 pairwise ring-jurisdiction combinations for anomalous patterns. When multiple rings simultaneously elevate in a single jurisdiction (e.g., Ring 1 stress + Ring 4 fragility + Ring 5 fracture in the EU), it may indicate a crisis precursor. These are flagged with pattern labels like 'crisis_precursor', 'policy_theater', 'silent_rollout'.
๐ 12 Impact Formulas
These formulas quantify how digital monetary infrastructure changes affect society, sovereignty, privacy, and systemic stability. Each is computed per jurisdiction.
Financial Inclusion Index
Weighted sum of account access, mobile reach, and digital literacy across the population. Measures whether digital currency deployment is widening or narrowing financial access.
Surveillance Capacity Score
Geometric mean of data scope, monitoring capability, and legal authority. Higher scores indicate greater state surveillance capacity enabled by the digital infrastructure.
Monetary Sovereignty Index
Measures a jurisdiction's control over its own monetary system. Declines when external dependencies (like reliance on foreign settlement rails) increase.
Cross-Border Friction Coefficient
Average of regulatory, technical, and cost barriers to cross-border value transfer. Lower friction means money moves more easily between jurisdictions.
Digital Divide Risk Score
Captures the risk that digital currency deployment leaves behind populations without digital access. Accounts for vulnerable population size and mitigation measures.
Systemic Fragility Index
Product of cascade risk, concentration in key nodes, lack of redundancy, and system homogeneity. Identifies how vulnerable the infrastructure is to cascading failures.
Privacy Erosion Index
Ratio of surveillance scope, data retention, and sharing practices against legal protections. Higher values indicate greater erosion of financial privacy.
Institutional Trust Score
Fourth-root geometric mean of transparency, accountability, historical track record, and public sentiment. Measures whether citizens trust the institutions deploying digital infrastructure.
Innovation Acceleration Rate
Rate of new deployments over time, scaled by adoption velocity and interoperability. Captures how quickly the jurisdiction is advancing its digital infrastructure.
Net Societal Impact Composite
Master composite that weights all impact scores by their direction-adjusted significance. Positive means net benefit to society; negative means net harm. Near zero means impacts are balanced.
Cascade Probability
Conditional probability chain for cascading failures. If node A fails, what is the probability it cascades to B, then to C? Used in scenario modeling.
Global Impact Propagation
How far and how fast a local event propagates through the global monetary network. Based on trade weight, geographic distance, and connectivity.
๐งช Historical Backtesting
GMIIE validates its detection capabilities by replaying historical financial crises through the 5-ring engine and comparing the system's outputs against known outcomes. This process measures accuracy (did the system detect the right signals?) and lead time (how early did it detect them?).
| Crisis | Year | Dominant Ring | Accuracy | Early Warning |
|---|---|---|---|---|
| Global Financial Crisis | 2008 | Ring 1 + Ring 4 | 88.2% | 4 weeks |
| Euro Sovereign Debt Crisis | 2011 | Ring 5 + Ring 1 | 87.3% | 6 weeks |
| COVID-19 Market Shock | 2020 | Ring 1 + Ring 2 | 78.4% | 2 weeks |
| Rate Shock & SWIFT Weaponization | 2022 | Ring 1 + Ring 5 | 91.2% | 5 weeks |
๐ก๏ธ Ethical Boundaries & Limitations
No Trading Recommendations
GMIIE analyzes infrastructure โ it does not generate buy/sell signals, trading strategies, or price predictions. All scores relate to systemic structure, not market timing.
Probabilistic, Not Deterministic
All predictions carry explicit probability scores. A 0.72 deployment probability means there is a 28% chance the system is wrong. Users should interpret outputs as informed estimates, not certainties.
Source Transparency
Every data point traces back to identified public sources โ central bank publications, BIS working papers, regulatory filings, SWIFT statistics. No anonymous or unverifiable sources are used.
Analyst-in-the-Loop
Automated outputs pass through human analyst review before influencing high-confidence assessments. The Analyst Review Layer and confidence normalization system ensure that the engine is supervised, not autonomous.