Research and Analysis
Methodology notes, industry analysis, and research from the team on probability intelligence, calibrated forecasting, and political risk.
Methodology
When a geopolitical event probability shifts overnight, the question isn't just how much. It's which sources moved it and why. We examine how source attribution changes the way risk analysts use probability scores.
Research
Confidence without calibration is noise. We look at how Brier scores and resolution tracking turn a probability engine into an instrument that earns analytic trust over time.
Industry
Corporate risk analysts receive the same high-volume news feed as everyone else, and are expected to extract probability from it manually. That process is where signal gets lost.
Methodology
Open-source intelligence was built for government analysts. Adapting it for corporate risk functions requires structuring the ingestion layer differently, and knowing which sources calibrate well against corporate event types.
Research
Probability without a confidence interval is a point estimate masquerading as analysis. We cover how uncertainty quantification in near-term event forecasting changes what analysts can claim and communicate.
Research
A 67% probability score on a political transition event carries very different weight at 67 ± 3 versus 67 ± 22. We explain why confidence interval width is as analytically important as the central estimate.
Methodology
The Cade Market scoring engine applies Bayesian updating across weighted intelligence sources. This post walks through the mechanics: how new source signals shift the probability reading and what happens when sources diverge.
Industry
Risk managers have a decision-record problem: they need defensible probability estimates before outcomes occur. We look at what makes a probability score useful in a corporate risk context versus an academic one.
Industry
OSINT has expanded beyond its government origins. We look at how the source landscape for geopolitical and corporate security events has changed, and what that means for probability-based risk tools.
Product
Most risk software is built to visualize information. We built Cade Market to produce a number: the probability that a specific event occurs. That design intent changes every product decision from ingestion to display.
Research
Decision-makers don't need another summary of what happened. They need a probability estimate for what might happen next, sourced and timestamped. We look at how probability scoring supports structured decision processes.
Methodology
Not all intelligence sources are created equal on a given event type. Calibration weights let the scoring engine give more weight to sources that have historically been accurate for that category, and less to those that haven't.
Company
Risk analysts deserve a probability instrument, not a noise feed. This is the founding reasoning behind Cade Market: calibrated, source-attributed event scoring for the analyst who needs a number, not a narrative.
The methodology described in these articles runs inside the Cade Market scoring engine. Request access to see it working on live events.