Model Thinking
Response Modes
| Mode | Trigger | Output |
|---|---|---|
| Guided | Ambiguous problem | Diagnostic questions → model recommendations |
| Direct | Clear problem or specific model requested | Structured multi-model analysis |
| Teaching | Wants to learn models | Model explanation + example + practice |
Workflow
- Classify: Decision? System? Strategy? Data? Learning?
- Select mode: Ambiguous → Guided | Clear → Direct | Learning → Teaching
- Apply 2-3 models: Primary insight + complementary views + blind spot check
- Deliver: Key insights → Recommendations → Caveats
Reference File Selection
| Problem Pattern | Primary | Also Consider |
|---|---|---|
| Choosing between options | decisions.md | economics.md, psychology.md |
| Understanding complex behavior | systems.md | networks.md |
| Interpreting data, prediction | statistics.md | algorithms.md, risk.md |
| Competition, negotiation | strategy.md | psychology.md, economics.md |
| Human behavior, bias | psychology.md | economics.md |
| Connections, influence, platforms | networks.md | economics.md, systems.md |
| Computational problem-solving | algorithms.md | statistics.md |
| Uncertainty, tail events | risk.md | statistics.md, psychology.md |
| Acquiring knowledge, skills | learning.md | psychology.md |
| Markets, incentives | economics.md | psychology.md, strategy.md |
| Cross-domain synthesis, model pairing | combinations.md | All domain files as needed |
Guided Mode: Diagnostic Questions
When problem is ambiguous, ask 2-3 from relevant domain:
| Domain | Key Questions |
|---|---|
| Decisions | Reversibility? (能不能反悔？) Time horizon? (影響多久？) Stakes? (賭注多大？) Stakeholders? (誰會受影響？) |
| Systems | Linear/non-linear? (結果跟投入成正比嗎？) Feedback loops? (有沒有自我強化或抑制的循環？) Delays? (行動到看見結果要多久？) Boundary? (問題的邊界畫在哪？) |
| Strategy | Players? (有哪些參與者？) Game type? (零和還是共贏？) Info asymmetries? (誰知道得比較多？) Incentives? (各方動機是什麼？) |
| Data | Sample size? (資料量夠嗎？) Base rate? (一般情況下機率多少？) Selection bias? (取樣有偏差嗎？) Signal vs noise? (訊號還是雜訊？) |
| Risk | Fat tail or thin tail? (極端事件常見嗎？) Reversible? (損害能恢復嗎？) Ruin possible? (有沒有全軍覆沒的可能？) |
Direct Application Template
When applying models directly:
## Analysis: [Problem Summary]
### Model Applied: [Model Name]
**Core Insight**: [One-sentence key takeaway]
**Application**:
[2-4 bullet points applying the model to the specific situation]
### Complementary View: [Second Model]
[Brief application showing different angle]
### Synthesis
- **Recommendation**: [Specific action]
- **Key Risk**: [What could go wrong]
- **Next Step**: [Immediate action to take]Teaching Mode Template
## [Model Name]
**One-liner**: [Memorable summary]
**Core Concept**: [2-3 sentences]
**Example**: [Concrete scenario]
**When to Use**: [Situations]
**Common Mistake**: [Key pitfall to avoid]
**Practice Prompt**: [A question for the user to apply this model to their own situation]Multi-Model Synthesis Example
Problem: Should I accept this job offer?
| Model | Insight |
|---|---|
| Regret Minimization | At 80, would I regret not trying this path? |
| Opportunity Cost | What salary/growth/learning am I giving up? |
| Reversibility | One-way door or can I return to current field? |
| Second-Order | How does this affect family, health, skills in 5 years? |
Synthesis: High regret potential + acceptable opportunity cost + reversible → Accept
Use 2-3 models from different domains to triangulate. Agreement = confidence. Disagreement = complexity worth exploring.
Critical Checks
Before finalizing any analysis:
- Inversion: What would make this analysis wrong?
- Base Rate: What typically happens in similar situations?
- Incentives: Who benefits from each outcome?
- Second-Order Effects: What happens next after the first-order effect?
- Falsifiability: How would we know if we're wrong?
Quick Reference: 10 Universal Models
Detailed explanations and application examples for each model are in the reference files listed in the Reference File Selection table above.
| Model | One-liner | Apply When |
|---|---|---|
| Inversion | Avoid stupidity rather than seek brilliance | Any decision |
| Second-Order Thinking | Then what? | Evaluating consequences |
| Opportunity Cost | What are you giving up? | Resource allocation |
| Base Rates | Prior probability matters | Any prediction |
| Feedback Loops | Effects become causes | System analysis |
| Margin of Safety | Build in buffers | Risk management |
| Incentives | Show me incentive, I show you outcome | Analyzing behavior |
| Map vs Territory | The model isn't reality | Any model use |
| Sunk Cost | Past costs are irrelevant | Decision-making |
| Explore/Exploit | Balance new vs known | Resource allocation |
For all models organized by domain, load reference files above. For multi-model combination strategies and cross-domain examples, see combinations.md.