Methodology

6 active connectors in production + WGI Phase 1 + Conflict Forecast API reference

Logicon ingests structured data from multiple independent, open-source datasets spanning different domains — a multi-source intelligence fusion approach. Each source covers a distinct signal type — conflict events, macroeconomic indicators, governance quality, sanctions, and media intensity — ensuring no single data provider can create blind spots in the operational environment. Six active OSINT connectors feed the production feature vector: UCDP GED (primary conflict data — 347K geo-coded events), GDELT, FRED, OpenSanctions, V-Dem, World Bank. WGI is ingested for Phase 1 wiring; Conflict Forecast API is integrated as external reference; ACLED is used for cross-validation only (no production code, full Content Usage Terms compliance).

18 features across 4 domains

Raw OSINT inputs are extracted as 18 features grouped into four domains — mirroring the analytical framework used in Intelligence Preparation of the Battlespace (IPB). Two of the 18 features encode temporal context directly (monthSin, monthCos); others embed temporal information through their construction (e.g., acledEventTrend = 7-day/30-day ratio, ucdpDeathsTrend = 30-day/90-day ratio, gdeltVolSpike = z-score vs 30-day baseline). Phase 1 will explore systematic temporal derivative augmentation and high-signal interaction terms.

2-model ensemble: logistic regression + decision stumps (60/40); GBT scaffolded for Phase 1

Predictions are generated by a weighted ensemble of two complementary model families. Logistic regression provides stable, interpretable baselines with well-understood uncertainty (60% weight). The decision stump forest captures single-feature threshold effects — such as conflict intensity tipping points (40% weight). A gradient boosted tree model is scaffolded for Phase 1 ensemble integration to capture multi-feature interaction effects.

• Logistic regression: interpretable linear baseline across all input features (60% weight)
• Decision stump forest: captures single-feature threshold effects via boosted weak learners (40% weight)
• Gradient boosted trees: scaffolded for Phase 1; training pending data diversity expansion across degenerate regions
• Output: raw probability estimate; isotonic calibration available offline pending production activation

Isotonic regression (PAV algorithm)

Raw model outputs are not always well-calibrated — a predicted 0.70 may correspond to a true event rate of 0.62 or 0.78. Isotonic calibration via Pool Adjacent Violators (PAV) is implemented and validated offline against 480 resolved predictions, where it achieves a 20% reduction in Brier score on backtest. Production activation in the live inference loop is pending data diversity expansion: five of ten supported regions currently exhibit degenerate label distributions (100% positive or 100% negative outcomes), causing the offline PAV map to overfit on these extremes. Diversity expansion is a Phase 1 deliverable.

• PAV: monotone non-decreasing step function fitted to historical (score, outcome) pairs
• Confidence intervals via ensemble variance
• Calibration quality measured by Brier score, log loss, and reliability diagrams
• Recalibration cadence and live activation are Phase 1 deliverables

Temporal holdout and overfitting prevention

All reported metrics follow strict temporal validation — no future information is used at any stage. Features observed at time T predict outcomes resolved at T + horizon days; the model never accesses data beyond the prediction date. Walk-forward cross-validation advances the training boundary chronologically: for each evaluation window the model trains only on prior data, then is tested on outcomes it has never seen. This eliminates information leakage and prevents overfitting to historical patterns. Confidence intervals for Brier score, AUC, and log loss are derived from bootstrap resampling (1,000 iterations with replacement). This protocol is consistent with temporal validation standards in quantitative conflict forecasting research (Ward et al., 2010; Hegre et al., 2013).

• Strict temporal split: training set always precedes test set, no overlapping windows
• Walk-forward: sliding cutoff re-trains on expanding history, scores on unseen future
• Feature discipline: each feature value computed exclusively from data available at prediction time
• Bootstrap confidence intervals: 1,000 resampled test sets for Brier, AUC, log loss

Autonomous retraining with drift detection

Logicon implements the infrastructure for autonomous retraining. Walk-forward temporal validation harness is in place; Page-Hinkley drift detection is deployed in production and currently in baseline observation period. The full autonomous retraining loop — including atomic model promotion gating on out-of-sample fitness — is a Phase 1 deliverable.

• Resolved outcomes collected and aligned with historical feature snapshots
• Walk-forward temporal validation harness implemented for out-of-sample integrity
• Page-Hinkley drift detection deployed; baseline observation period active
• Full autonomous retraining loop with atomic model promotion is a Phase 1 deliverable
• Implementation stages documented in /architecture

Complete reproducibility chain

Every prediction is stored alongside its complete computational provenance. This enables any prediction to be independently verified, reproduced, or challenged — a critical requirement for decision superiority and accountability in high-stakes operational environments.

• Input snapshot hash (SHA-256) — cryptographic fingerprint of all input data at prediction time
• Feature vector — full 18-dimensional feature vector stored as JSON for exact reproducibility
• Model version — parameter set ID linking to exact weights, thresholds, and calibration map
• Evidence chain — ranked list of contributing data points with polarity and weight
• Reasoning trace — natural language explanation of key drivers

Mapping forecasts to the NATO planning cycle

Logicon forecasts are designed to feed directly into the operational planning process and assessment cycle used by NATO commands — from Supreme Headquarters Allied Powers Europe (SHAPE) through Joint Force Commands to Tactical Component Commands. Each forecast horizon maps to a specific phase of the planning cycle, enabling decision superiority by integrating calibrated probabilistic intelligence at tactical, strategic, and structural echelons.

API-first architecture for MSS NATO and Allied systems

Logicon is built as a modular microservice, not a monolithic platform. Every capability is accessible through documented REST API endpoints, enabling seamless integration with existing digital warfighting platforms including Maven Smart System NATO (MSS NATO). Designed to augment the Common Operating Picture (COP) with calibrated probabilistic intelligence for multi-domain operations.

• RESTful API with OpenAPI 3.0 specification — standard HTTP methods, JSON responses
• GeoJSON output format for geospatially referenced forecasts overlaid on the COP — enhancing situational awareness at all echelons
• Natural language query endpoint for commander interaction with the operational environment — supporting decision superiority
• Containerised deployment (Docker) — cloud-native, ready for AWS GovCloud, Azure Government, or NATO cloud infrastructure
• Security-hardened: TLS 1.3, API key + HMAC authentication, full audit logging, CSP/HSTS/X-Frame-Options headers
• Stateless API design allows horizontal scaling — per-region computation is independent and parallelisable

Source diversity, geographic balance, and human oversight

Logicon applies structured bias mitigation at every stage of the forecasting pipeline — from data ingestion through model output. Source diversity is enforced by drawing from six active OSINT connectors in production (UCDP GED, GDELT, FRED, OpenSanctions, V-Dem, World Bank), plus WGI for Phase 1 wiring, Conflict Forecast API as external reference, and ACLED for cross-validation only — each with distinct collection methodologies, preventing single-source reporting bias from propagating into predictions. Geographic coverage spans 10 regions across different continents, reducing the risk of geographic blind spots that affect many Western-centric forecasting systems. Temporal validation uses strict walk-forward cross-validation where the model only trains on past data, eliminating look-ahead bias — the most common source of inflated accuracy in predictive analytics. The 2-model ensemble (logistic regression + decision stump forest, with gradient boosted trees scaffolded for Phase 1) reduces algorithmic monoculture; each model family has different inductive biases, and their disagreements surface areas of genuine uncertainty rather than masking them. Finally, Logicon is designed as a decision-support tool, not an autonomous decision-maker — consistent with NATO AI Responsible Use Principles (PRU 5: Human oversight, PRU 6: Traceability). All forecasts require human review before operational use, and the full audit trail enables operators to inspect, challenge, and override any prediction.

• Source diversity: 6 active connectors in production, WGI for Phase 1 wiring, Conflict Forecast API as reference, ACLED for cross-validation only — no single dataset can dominate the signal
• Geographic balance: 10 regions across 4 continents — mitigates Western-centric or conflict-zone-only sampling bias
• Temporal discipline: strict walk-forward validation prevents look-ahead bias; no future data leaks into training
• Model diversity: 2 complementary model families (third scaffolded for Phase 1) with different inductive biases reduce algorithmic monoculture
• Calibration audit: reliability diagrams and Brier decomposition detect systematic over- or under-confidence by region or event type
• Human oversight (NATO PRU 5): Logicon provides probabilities and evidence — human operators make decisions
• Traceability (NATO PRU 6): every prediction links to its input snapshot, feature vector, model version, and evidence chain for independent review