rsn-learning-outcomes

name	rsn-learning-outcomes
description	Extracts insights and improves performance from experience. Applies single-loop (fix action), double-loop (fix frame), reflection (extract insight), experimentation (test belief), and calibration (adjust confidence) modes. Use when correcting mistakes, learning from outcomes, testing hypotheses, or improving predictions. Triggers on "why did this fail", "what can we learn", "test this", "how accurate are we", "pattern of failures".
license	Complete terms in LICENSE.txt

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

Mode	Question	Output	Trigger
Single-loop	Did action work?	Corrected action	Gap between expected/actual
Double-loop	Is frame right?	Updated frame	Pattern of single-loop failures
Reflection	What can we learn?	Transferable insights	Experience completed
Experimentation	Should we test this?	Validated/invalidated belief	Belief needs validation
Calibration	How accurate are we?	Adjusted confidence rules	Predictions need tuning

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

From	To	Trigger
Single-loop	Double-loop	Pattern detected (3+ similar failures)
Double-loop	Experimentation	New frame needs validation
Experimentation	Reflection	Experiment completed
Reflection	Calibration	Predictions were off
Any	Single-loop	New gap detected

Learning → Other Skills Handoff

Learning Output	Next Skill
Corrected action	Causal (execute)
New frame	Thinking (reason with new assumptions)
Insight about perception	Perceiving (adjust attention)
Validated hypothesis	Causal (plan rollout)
Calibration rule	All thinking modes (adjust confidence)

---

Anti-Patterns

Avoid	Do Instead
No reflection time	Schedule deliberate reflection
Blame focus	Focus on system/process
Premature double-loop	Require pattern of failures
Peeking at experiment results	Wait for full duration
Over-adjusting calibration	Gradual adjustments
Insight hoarding	Plan dissemination

---

References

File	Content
single-loop.md	Action correction within frame
double-loop.md	Frame examination and update
reflection.md	Insight extraction process
experimentation.md	Hypothesis testing methods
calibration.md	Confidence adjustment