Forecasts
Calibrated probabilities.
With the receipts.
We don't predict. We produce probability distributions with explicit confidence intervals, sourced from 14 institutional-grade data feeds, and tracked against resolved outcomes. If we're wrong, you'll know — and so will we.
Probability of a sovereign-debt restructuring event in Argentina before Q3 2026.
The baseline problem
Why most forecasting fails.
Instability and change are not random, and they are not single-domain phenomena. They are preceded by compounding signals across politics, economics, security, health, environment, and the digital layer.
The four-stage architecture
A pipeline that shows its work.
Every probability passes through four sequential stages. Each one does specific, defensible work — and each one is auditable.
The signal architecture
14 institutional-grade sources.
A Bayesian system is only as good as its inputs. Diverse source types reduce correlated error — when news is suppressed, satellite, seismic, economic and internet-disruption layers keep working.
The feedback loop
The system tracks its own accuracy. Then updates how it forecasts.
Every resolved forecast feeds back into the next generation cycle. We recompute our Brier score and per-category accuracy continuously. When the model has been systematically overconfident, it receives an explicit instruction to dampen confidence. When it has been underconfident, the inverse.
This is the computational analog of what distinguishes Tetlock's superforecasters from ordinary experts: the willingness to track your own accuracy and update your approach accordingly. Most forecasters never do this. We can't not.
Uncertainty decomposition
Honest about what it doesn’t know.
Uncertainty has two distinct components, and good forecasting communicates both. Confidence intervals on Brunu aren't decoration — they reflect the actual epistemic state.
What we don’t know yet.
Sparse signals, conflicting sources, prior at extremes
This narrows as more signals arrive, sources align, and the scenario distribution tightens. Brunu surfaces it as a wider confidence interval — and shows you which signals would close the gap.
What is genuinely random.
A single actor's decision on a specific day
Some futures cannot be narrowed further regardless of intelligence quality. Brunu keeps this floor visible. A 43% probability with a 7-point CI is a different statement than 43% with a 25-point CI — and the system says which.
Two things almost no one else does
Reflexive weighting. Actor fingerprints.
At a certain density, coverage stops describing reality and starts shaping it. And the same structural signal means different things depending on who is making the next decision.
When coverage becomes the cause.
At high media density, news ceases merely signaling underlying conditions and starts causing them. Heavy coverage of a fragile bank accelerates a run. Brunu downweights media as a structural indicator past the threshold and upweights it as a behavioral cascade predictor. The system can’t be fooled by its own inputs.
Probabilities are not actor-agnostic.
We build probabilistic behavioral models for key actors — states, institutions, movements, market participants — based on historical decisions, stated objectives, risk tolerance and sensitivity to environmental change. Scenarios that require an actor to behave wildly off-pattern get lower weights. It’s pattern-of-life, applied at scale.
Track record
If we’re wrong, you’ll know.
Every resolved forecast is public. Every Brier score is public. Every recalibration instruction is logged. The track record is the product.
Built for everyone who has to make a call
However you read it.
Browse calibrated probabilities in the open.
Open a forecast. See the prior, the signals that moved it, the scenario distribution, the CI, and the resolution criteria. Subscribe to a category. Make your own predictions.