decision-journal

A decision journal records decisions before outcomes occur, capturing alternatives and confidence levels. Reviewing these logs later audits your assumptions and improves calibration by revealing patterns in decision-making accuracy over time.

To log a decision, enter a brief description, list the alternatives considered, and assign a confidence level to your chosen path. This structured pre-decision entry creates a baseline for later post-mortem review.

Yes, the post-mortem process supports business, product, trading, and strategy contexts. It applies root-cause classification to outcomes, helping you identify whether the issue was a bad process or flawed assumptions.

The best way to track calibration is comparing your initial pre-decision confidence levels against actual outcomes during post-decision reviews. This structured logging reveals whether your confidence accurately predicts success over time.

Decision logging works for trading and strategy workflows by applying structured pre-decision entry and post-decision review. It satisfies the need for accountability and root-cause analysis without requiring specific platform integrations.

Run a post-decision review to audit assumptions and calibration against the outcome, while a full post-mortem is reserved for deeper root-cause classification and analysis when a decision fails or underperforms significantly.