Learning

Reads

Source Purpose

User brief/conversation Problem context, constraints, goals

Information sources Domain knowledge, prior solutions (optional)

Writes

Output Content

Inline recommendations Ideas, solutions, insights, learning frameworks

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

Process

• [K] Detect learning trigger — Gap detected, experience completed, belief needs testing, or predictions off

• [K] Select learning mode — Choose single-loop, double-loop, reflection, experimentation, or calibration

• [S] Execute mode process — Follow mode-specific workflow systematically

• [K] Extract insights — Identify transferable patterns and updated beliefs

• [R] Validate learning — Confirm insights are actionable and conditions-bounded

Evaluation methods for [R] steps:

• Pattern validation — Require 3+ instances before generalizing (single/double-loop)

• Insight transferability — Verify conditions when insight applies (reflection)

• Experimental rigor — Check falsifiability and success criteria (experimentation)

• Calibration accuracy — Require 30+ predictions for meaningful adjustment (calibration)

Boundaries

In scope:

• Correcting actions (single-loop)

• Questioning frames (double-loop)

• Extracting insights from experience (reflection)

• Testing beliefs through experiments

• Adjusting prediction confidence (calibration)

• Creating learning artifacts (heuristics, playbooks, checklists)

Out of scope:

• Executing corrected actions (use rsn-reasoning-problems.causal)

• Gathering information to inform learning (use rsn-perceiving-information)

• Creative problem-solving for new situations (use rsn-creating-ideas)

• Deep reasoning with new frames (use rsn-reasoning-problems)

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