Learning

Systematic improvement from experience. Convert outcomes into better future performance.

Core Principle

Learning is not automatic. Experience without reflection is just repetition. Learning requires deliberate extraction of insight and updating of beliefs and behaviors.

Experience → Extract → Update → Apply → Better Outcomes

Mode Selection

Decision Tree

Is there a gap between expected and actual?
  YES → Is this a pattern (3+ similar failures)?
    YES → Double-loop (question the frame)
    NO  → Single-loop (fix the action)
  NO  ↓
Has an experience completed?
  YES → Reflection (extract insights)
  NO  ↓
Do you have a belief that needs validation before commitment?
  YES → Experimentation (test the belief)
  NO  ↓
Have predictions been consistently off?
  YES → Calibration (adjust confidence)
  NO  → No learning mode needed

Mode Summaries

Single-Loop

Purpose: Correct action within existing frame.

Mental model: Thermostat — detect deviation, adjust action, return to target. The goal is not questioned.

Process: Gap detected → Diagnose cause → Identify correction → Verify fix → Prevent recurrence

Key rules:

• Fix the proximate cause
• Don’t question the goal (yet)
• Add prevention to avoid repeat
• Check: is this a pattern? If yes → double-loop

Output: Corrected action with prevention

→ references/single-loop.md

Double-Loop

Purpose: Question and update the frame itself.

Mental model: Not just adjusting thermostat, but asking: “Is heating the right goal?”

Process: Pattern detected → Examine current frame → Challenge assumptions → Construct new frame → Validate change

Key rules:

• Requires 3+ single-loop failures (pattern)
• Articulate current frame (goals, assumptions, constraints)
• Challenge each element with evidence
• Test new frame before full commitment

Output: Updated frame with validation plan

→ references/double-loop.md

Reflection

Purpose: Extract transferable insight from experience.

Mental model: Mine the experience for reusable gold.

Process: Capture experience → Analyze what worked/didn’t → Extract insights → Update beliefs → Create artifacts → Disseminate

Key rules:

• Reflection is scheduled, not accidental
• Analyze both successes and failures
• Specify conditions when insight applies
• Create persistent artifacts (heuristics, playbooks, checklists)

Output: Insights and artifacts for future use

→ references/reflection.md

Experimentation

Purpose: Test belief through deliberate action before commitment.

Mental model: Scientific method applied to operational decisions.

Process: Formulate hypothesis → Design experiment → Execute → Analyze results → Conclude → Act

Key rules:

• Hypothesis must be falsifiable
• Define success criteria before testing
• Control variables where possible
• Don’t peek at results early

Output: Validated or invalidated belief with next steps

→ references/experimentation.md

Calibration

Purpose: Adjust prediction confidence based on track record.

Mental model: Weather forecaster — when I say 80% confident, it should be right 80% of the time.

Process: Assemble track record → Stratify by confidence level → Calculate calibration error → Identify patterns → Define adjustment rules

Key rules:

• Need 30+ predictions for meaningful calibration
• Stratify by domain (calibration varies)
• Adjust gradually, not dramatically
• Monitor ongoing calibration

Output: Calibration adjustment rules

→ references/calibration.md

Output Format

Every learning output includes:

## [Mode]: [Topic]

**Trigger:** [What triggered this learning mode]

**Analysis:**
[Mode-specific analysis]

**Conclusion:**
[What was learned/changed]

**Artifacts:**
- [Any persistent outputs: rules, checklists, playbooks]

**Next:**
- [Actions to take]
- [What to monitor]

Mode Transitions

Learning → Other Skills Handoff

Anti-Patterns

References