/score

Score Decision Outcome

Review a past decision and score how the forecasts performed.

Option A: Use the CLI (Recommended)

Run the interactive scoring command:

farness score $ARGUMENTS

This will:

1. Show unscored decisions (or find by ID if provided)
2. Display original forecasts for the chosen option
3. Prompt for actual outcomes per KPI
4. Calculate errors and CI coverage
5. Save results and show updated calibration

Option B: Guided Scoring (if CLI unavailable)

Step 1: Find the Decision

List unscored decisions:

farness list --unscored

Or show a specific decision:

farness show <id>

Step 2: Review Original Forecasts

Display:

• The decision question
• The chosen option
• Forecasts for each KPI (point estimate, confidence interval)
• Key assumptions at the time

Step 3: Gather Outcomes

For each KPI, ask the user: "What was the actual outcome for [KPI]?"

Get specific numbers.

Step 4: Update via Python

from datetime import datetime
from farness import DecisionStore

store = DecisionStore()
decision = store.get("<decision_id>")

decision.actual_outcomes = {
    "<kpi1>": <actual_value>,
    "<kpi2>": <actual_value>,
}
decision.scored_at = datetime.now()
decision.reflections = "<user reflections>"

store.update(decision)

Step 5: Show Calibration

farness calibration

Reflection Questions

After scoring, ask:

• "What did you learn from this decision?"
• "Would you make the same choice with hindsight?"
• "What would you do differently in the analysis?"

Record reflections in the decision.