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rsn-reasoning-problems

通过使用六种认知模式来推理问题。应用因果(执行目标)、溯因(解释观察结果)、归纳(寻找模式)、类比(从相似情况转移)、辩证(解决矛盾)和反事实(评估替代方案)思维。在计划、诊断、寻找模式、评估权衡或探索假设情景时使用。触发词包括“为什么”、“如果...会怎样”、“应该如何”、“分析这个”、“找出”。

person作者: jakexiaohubgithub

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


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


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


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


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


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 |


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


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 |