Bayesian Threat Board?How this worksEvery tracked threat has a current probability that moves with evidence. Each dispatch we ingest is scored against every threat; if it raises or lowers the odds of that threat materializing, it nudges the probability via Bayesian update (prior + evidence → posterior).
Confidence intervals tighten as evidence count grows. Not forecasting — continuous re-weighting of a claim against all available reporting.
Confidence intervals tighten as evidence count grows. Not forecasting — continuous re-weighting of a claim against all available reporting.
Tracking conflict threats.
Dimension index 46 · 3 threats · 10,268 pieces of evidence. Cross-sectional average of the member threats’ current probabilities.
3 threats shown
- 01Middle East regional war expansionconflictIranIsraelLebanonYemen· 207,940 evidence81CI 81–81%↓ ±0.0pp7dElevated
Detail → - 02China-Taiwan military actionconflictChinaTaiwan· 27,827 evidence46CI 45–46%→ ±0.0pp7dModerate
Detail → - 03Russia-Ukraine ceasefireconflictRussiaUkraine· 44,387 evidence10CI 10–11%→ ±0.0pp7dStable
Detail →
Methodology
Each tracked threat begins with a prior probability based on historical base rates and expert elicitation. New evidence — every dispatch scored against the threat — nudges that probability up or down. Bayesian posterior updates compound; confidence intervals tighten as evidence accumulates.
Read the full approach at /about/methodology. Individual threat pages show the full evidence stream including direction, magnitude, and the reasoning our AI engine used for each nudge.