rsn-reasoning-problems

name	rsn-reasoning-problems
description	Reasons through problems using six cognitive modes. Applies causal (execute goals), abductive (explain observations), inductive (find patterns), analogical (transfer from similar), dialectical (resolve tensions), and counterfactual (evaluate alternatives) thinking. Use when planning, diagnosing, finding patterns, evaluating trade-offs, or exploring what-ifs. Triggers on "why did", "what if", "how should", "analyze this", "figure out".
license	Complete terms in LICENSE.txt

Reasoning

Route to cognitive mode. Execute structured analysis. Produce formatted output.

Mode Selection

Mode	Question	Output	Trigger
Causal	How do we execute?	Plan with actions	Known process, operational workflow
Abductive	Why did this happen?	Diagnosis with hypotheses	Single anomaly, diagnosis needed
Inductive	What pattern exists?	Rules or assessment	Multiple observations, evaluation
Analogical	How is this like that?	Adaptation plan	Novel situation, transfer needed
Dialectical	How do we resolve this?	Synthesis or decision	Conflicting positions, choosing options
Counterfactual	What if we had/do X?	Comparison with verdict	Decision evaluation, scenarios

For simple cases without deep reasoning: Use templates directly.

Decision Tree

Is this operational execution with known steps?
  YES → Causal
  NO  ↓
Is there a single anomaly requiring explanation?
  YES → Abductive
  NO  ↓
Are there multiple instances suggesting a pattern?
  YES → Inductive
  NO  ↓
Is this a novel situation with a similar past case?
  YES → Analogical
  NO  ↓
Are there conflicting positions or trade-offs?
  YES → Dialectical
  NO  ↓
Evaluating past decisions or future scenarios?
  YES → Counterfactual
  NO  → Ask clarifying question

---

Mental Models

Apply these models to sharpen reasoning across all modes.

Model	Core Insight	Apply When
Telescope, Not Brain	AI reveals data structure, doesn't create it	Diagnosing AI/model failures
Geometry Under Constraints	Dense patterns → reasoning; thin patterns → hallucination	Evaluating AI confidence
Compression = Generalization	Models compress structure into reproducible patterns	Explaining model behavior
Four-Layer Stack	Representation → Generalization → Reasoning → Agency	Localizing AI failures
Prediction vs Behavior	Prediction is cheap; behavior has consequences	Designing agent constraints
Labels ≠ Truth	Labels are opinions frozen in data	Evaluating training data

Full reference: references/mental-models.md

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Challenge Techniques

Every conclusion must survive challenge. Use these techniques:

Devil's Advocate

Attack your own position. What's the strongest argument against this conclusion?

Pre-Mortem

Assume the plan failed in 6 months. Why did it fail?

Stakeholder Lens

How does [engineering/sales/user/finance] see this differently?

Steel-Man + Attack

State the opposing view at its strongest, then find the flaw.

Layer Check

Which layer is actually failing? (Representation → Generalization → Reasoning → Agency)

---

Mode Summaries

Causal

Purpose: Execute systematic cause-effect reasoning.

Flow: Input → Hypothesis → Implication → Decision → Actions → Learning

Output: Execution analysis or phased plan (for larger initiatives)

Key rules:

• All claims require evidence with source
• Hypothesis must be falsifiable
• Implications need specific numbers (not "significant")
• Decision must be explicit: PROCEED / DEFER / DECLINE
• Actions need owner + deadline + success criteria
• Learning compares expected vs actual

Challenge: "What would prove this hypothesis wrong?"

→ references/causal.md

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Abductive

Purpose: Generate best explanation from observation.

Flow: Observation → Hypotheses (≥5) → Evidence Debate → Best Explanation

Output: Diagnosis with ranked hypotheses and minority report

Key rules:

• Quantify the anomaly (%, deviation, timeline)
• Generate hypotheses across ≥3 categories
• For AI systems: check by layer (Representation/Generalization/Reasoning/Agency)
• Include minority report if second hypothesis ≥40% confidence
• State what was ruled out and why

Challenge: "What else could explain this? What doesn't this hypothesis explain?"

→ references/abductive.md

---

Inductive

Purpose: Extract patterns from multiple observations.

Flow: Collection (≥5 instances) → Pattern Detection → Generalization → Confidence Bounds

Output: Pattern analysis with rules, or assessment against criteria

Pattern types: Frequency, Correlation, Sequence, Cluster, Trend, Threshold

Key rules:

• Minimum 5 instances before generalizing
• Correlation ≠ causation (test mechanism separately)
• State applicability bounds for every rule
• Document exceptions (≥30% exception rate = unreliable rule)

Challenge: "Is this pattern or coincidence? What's the exception that breaks this?"

→ references/inductive.md

---

Analogical

Purpose: Transfer knowledge from source to target situation.

Flow: Source Retrieval → Structural Mapping → Target Application → Adaptation

Output: Adaptation plan with what transfers, what adapts, what's new

Key rules:

• Source must have documented outcome
• Map structure (objects, relations, mechanisms), not surface features
• Identify at least one "broken" relation (perfect analogies don't exist)
• Specify what's genuinely new (not just adapted)

Challenge: "Where does this analogy break down? What's different about the new context?"

→ references/analogical.md

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Dialectical

Purpose: Synthesize opposing positions.

Flow: Thesis (steel-man) → Antithesis (steel-man) → Synthesis

Output: Synthesis resolving conflict, or decision selecting between options

Key rules:

• State underlying concern, not just position
• Steel-man both sides (strongest version)
• Synthesis ≠ compromise (must address root concerns)
• Explicit trade-offs with who accepts the cost

Resolution types: Integration, Sequencing, Segmentation, Reframing, Transcendence

Challenge: "Am I straw-manning either side? Does synthesis actually resolve the tension?"

→ references/dialectical.md

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Counterfactual

Purpose: Evaluate alternatives through "what if" simulation.

Flow: Actual World → Intervention → Projection → Comparison

Output: Comparison with verdict and learning

Key rules:

• Document what was knowable at decision time (avoid hindsight bias)
• Intervention must have been actually available
• Model three scenarios: Expected (55-60%), Optimistic (20-25%), Pessimistic (15-20%)
• Verdict requires confidence bounds

Challenge: "Am I using hindsight? Was this actually an option then?"

→ references/counterfactual.md

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Output Format

Prose, not YAML. Every reasoning output includes:

## [Mode] Analysis: [Topic]

**Conclusion:** [Primary finding in 1-2 sentences]

**Confidence:** [X%] — [Why this confidence level]

**Supporting evidence:**
- [Evidence 1]
- [Evidence 2]

**Challenges addressed:**
- [Challenge]: [How resolved]

**Uncertainty:** [What's still unknown]

**Next steps:**
1. [Action with owner if applicable]

---

Mode Transitions

From	To	Trigger
Abductive	Causal	Diagnosis complete → ready to act
Inductive	Causal	Pattern validated → ready to apply
Analogical	Causal	Adaptation ready → ready to execute
Dialectical	Causal	Synthesis agreed → ready to implement
Counterfactual	Inductive	Multiple counterfactuals suggest pattern
Any	Abductive	Unexpected outcome during execution

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Anti-Patterns

Avoid	Do Instead
Skipping challenge step	Every conclusion must survive attack
"It's obvious"	Require evidence for conclusion
Vague confidence ("pretty sure")	Numeric confidence with rationale
Single hypothesis	Generate ≥5 before evaluating
Perfect analogy assumption	Always find where mapping breaks
Compromise as synthesis	Address underlying concerns
Hindsight in counterfactuals	Document what was knowable then

---

Templates

For simple structural needs without full reasoning, use templates directly.

Template	Use Case	Trigger
SOP/Runbook	Document known process	"create runbook", "write SOP"
Checklist	Quick verification	"checklist for", "pre-flight"
Success Criteria	Define "done"	"how do we know", "success metrics"
Recommendation	Actionable guidance	"what should I do", "recommend"

→ references/templates.md

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References

File	Content
mental-models.md	Conceptual models for reasoning
causal.md	Execution flow + plan output
abductive.md	Hypothesis testing + diagnosis output
inductive.md	Pattern extraction + assessment output
analogical.md	Knowledge transfer + adaptation output
dialectical.md	Position synthesis + decision output
counterfactual.md	Alternative evaluation + comparison output
templates.md	SOPs, checklists, success criteria, recommendations