forecasting

A calibrated forecast provides explicit probability estimates with documented rationale for risk analysis. It ensures your confidence levels accurately reflect reality, preventing overconfidence in uncertain scenarios.

You track forecasting accuracy using a Brier score by logging predicted probabilities alongside actual outcomes in a prediction ledger. This measures historical performance and enables continuous improvement.

You can apply Bayesian updating to refine predictions as new evidence emerges. This method systematically adjusts your explicit probability estimates to reflect newly available data across relevant domains.

Maintain an auditable prediction ledger for post-mortem analysis by logging each forecast's confidence level, documented rationale, and expected resolution date alongside the eventual outcomes to ensure long-term learning.

This forecasting approach works for project, market, and technology predictions by generating explicit probability estimates and tracking outcomes across diverse domains through a unified prediction ledger.

The best way to start producing calibrated forecasts with trackable outcomes is to define a specific topic, assign an explicit probability estimate with a documented rationale, and log the expected resolution date.