# Mental Models

Reference guideline for Wing-1 deep-thinking work — Charlie Munger's
multi-discipline lattice for analyzing problems through more than one
lens at a time. Adopted under the **Reference-Guideline Sunset Policy**
(see frontmatter `upstream` / `refresh_trigger` keys) and cross-referenced
from:

- [`deep-reading-analyst`](../../../.agent-src.uncondensed/skills/deep-reading-analyst/SKILL.md)
  — L3 Deep analysis depth.
- [`refine-prompt`](../../../.agent-src.uncondensed/skills/refine-prompt/SKILL.md)
  — multi-lens prompt refinement.
- [`judge-bug-hunter`](../../../.agent-src.uncondensed/skills/judge-bug-hunter/SKILL.md)
  — diff review through more than the bug-hunter lens.

> **Core principle:** "To a man with only a hammer, every problem looks
> like a nail." — Charlie Munger

## When to Use

Ideal for:
- 📊 Understanding complex problems from multiple angles
- 🔍 Avoiding cognitive biases and blind spots
- 💡 Finding innovative solutions
- 🎯 Making important decisions
- 📚 Cross-disciplinary learning and knowledge integration

## Core Mental Models Toolkit

### 1. General Thinking Models

#### First Principles
- Strip away assumptions to fundamentals
- Rebuild from basic truths
- Application: Analyze article's core assumptions

#### Compounding
- Long-term accumulation creates exponential growth
- Small improvements compound over time
- Application: Evaluate long-term strategies in content

#### Opportunity Cost
- Choosing one option means forgoing others
- Evaluate trade-offs
- Application: Analyze the cost of recommended approaches

### 2. Physics Models

#### Leverage
- Small force creates large results
- Find the critical leverage points
- Application: Identify key action points in articles

#### Inertia
- Systems tend to maintain current state
- Change requires additional energy
- Application: Understand resistance to change

#### Critical Mass
- Qualitative change occurs after reaching threshold
- Accumulation to breakthrough
- Application: Evaluate when tipping points occur

### 3. Biology Models

#### Evolution
- Survival of the fittest, continuous iteration
- Small experiments, rapid adaptation
- Application: Analyze adaptive strategies in content

#### Ecosystem
- Complex networks of interdependence
- Balance and diversity
- Application: Understand systemic issues

#### Red Queen Effect
- Must keep running to stay in same place
- Continuous progress in competitive environments
- Application: Evaluate competitive strategies

### 4. Psychology Models

#### Cognitive Biases
- Confirmation bias, anchoring, availability heuristic
- Systematic thinking errors
- Application: Identify logical flaws in arguments

#### Loss Aversion
- Motivation to avoid losses > gain benefits
- Asymmetric risk decision-making
- Application: Understand decision motivations

#### Social Proof
- Herd mentality and group influence
- Others' behavior affects judgment
- Application: Analyze persuasive power of arguments

### 5. Economics Models

#### Supply and Demand
- Price determined by market equilibrium
- Scarcity creates value
- Application: Analyze market-related articles

#### Marginal Utility
- Value of additional units decreases
- Optimal point isn't maximum
- Application: Evaluate resource allocation recommendations

#### Incentives
- "Show me the incentive and I'll show you the outcome"
- People respond to incentives
- Application: Understand motivations behind behaviors

### 6. Mathematics/Statistics Models

#### Normal Distribution
- Most cases cluster around average
- Extreme cases are rare
- Application: Evaluate probabilities and risks

#### Power Law
- 80/20 rule (Pareto Principle)
- Few factors produce most results
- Application: Identify key factors

#### Regression to the Mean
- Extreme results tend toward average
- Avoid overreaction to outliers
- Application: Evaluate representativeness of exceptional cases

### 7. Systems Thinking Models

#### Feedback Loops
- Positive feedback: amplifying effects
- Negative feedback: balancing stability
- Application: Identify causal relationships

#### Emergence
- Whole is greater than sum of parts
- System-level properties
- Application: Understand complex systems

#### Bottleneck
- Critical constraint limiting system performance
- Optimizing bottleneck yields maximum benefit
- Application: Find core problems

## Application Method

### Step 1: Identify Problem Type

```markdown
Problem Classification:
- [ ] Decision problem → Use: Opportunity cost, Loss aversion, Incentives
- [ ] System problem → Use: Feedback loops, Emergence, Ecosystem
- [ ] Growth problem → Use: Compounding, Leverage, Power law
- [ ] Competition problem → Use: Evolution, Red Queen effect, Game theory
```

### Step 2: Apply Multiple Models

**Rule**: Use at least 3 models from different disciplines

```markdown
## Multi-Perspective Analysis

### Physics Perspective (Leverage)
- Observation: Key actions proposed in article
- Analysis: Which action yields maximum output with minimum input?
- Conclusion: [Identify leverage points]

### Psychology Perspective (Cognitive Biases)
- Observation: Argumentation approach
- Analysis: Any confirmation or survivorship bias present?
- Conclusion: [Evaluate argument quality]

### Economics Perspective (Incentives)
- Observation: Stakeholders involved
- Analysis: Are incentives aligned?
- Conclusion: [Understand behavioral motivations]
```

### Step 3: Find Model Intersections

```markdown
## Cross-Model Insights

When multiple models point to same conclusion → confidence ↑
When models contradict → need deeper investigation

Intersection discoveries:
1. [Model A] + [Model B] → [Shared insight]
2. [Contradiction] → [Issue requiring further analysis]
```

### Step 4: Synthesize Judgment

```markdown
## Integrated Conclusion

Based on multi-model analysis:
- ✅ Strongly supported views: [List]
- ⚠️  Proceed with caution: [List]
- ❌ Problematic assumptions: [List]

Meta-cognitive check:
- Did I use sufficiently diverse models?
- Any important perspectives missing?
- Over-reliance on any single model?
```

## Practical Example

### Example: Analyzing Startup Article

**Article Claim**: "Rapid growth is key to startup success"

#### Multi-Model Analysis:

**1. Ecosystem Model (Biology)**
- Insight: Too-rapid growth can disrupt organizational "ecosystem balance"
- Risk: Culture dilution, quality decline

**2. Critical Mass Model (Physics)**
- Insight: Must reach certain scale to survive
- Support: Network effects require user base

**3. Marginal Utility Model (Economics)**
- Insight: Growth benefits diminish
- Question: Is unlimited growth speed pursuit rational?

**4. Feedback Loop Model (Systems)**
- Insight: Rapid growth → resource strain → quality drop → user churn (negative feedback)
- Risk: Growth trap

**Synthesized Judgment**:
- ✅ Article valid: Early stage needs rapid market validation
- ⚠️  Missing: Sustainable vs. growth-at-all-costs
- ❌ Overlooked: Industry/stage-specific growth rhythms

## Common Pitfalls

### ❌ Wrong Usage

1. **Model Misuse**: Force-fitting irrelevant models
2. **Single Dependency**: Actually using only one model, pretending it's multiple
3. **Over-complexity**: Piling models without real insights
4. **Ignoring Context**: Not considering specific situational differences

### ✅ Correct Usage

1. **Choose Relevance**: Select models related to problem essence
2. **Apply Deeply**: Truly understand model applicability
3. **Seek Conflicts**: Pay special attention to model contradictions
4. **Stay Flexible**: Adjust model selection based on new information

## Building Your Model Library

### Beginner Level (Essential)
- First principles
- Opportunity cost
- Compounding
- Feedback loops
- Cognitive biases

### Intermediate Level (Expansion)
- Ecosystem
- Power law
- Emergence
- Red Queen effect
- Game theory

### Advanced Level (Specialization)
- Deepen based on your domain
- Cross-disciplinary integration
- Create your own model combinations

## Learning Recommendations

1. **Broad Reading**: Study classics from different disciplines
2. **Deliberate Practice**: Try applying 3+ models to every problem
3. **Build Index**: Maintain personal "model → use case" catalog
4. **Reflect**: Note which model combinations work especially well
5. **Teach Others**: Explaining models deepens understanding

## Recommended Resources

**Books**:
- "Poor Charlie's Almanack" - Charlie Munger
- "The Art of Thinking Clearly" - Rolf Dobelli
- "Principles" - Ray Dalio
- "Super Thinking" - Gabriel Weinberg

**Key Quote**:
> "You must know the big ideas in the big disciplines and use them routinely—all of them, not just a few."
> — Charlie Munger

## Integration with Other Frameworks

- **Critical Thinking**: Mental models help identify argument blind spots
- **Systems Thinking**: Provides more tools for analyzing systems
- **First Principles**: Can serve as one of the mental models
- **Six Thinking Hats**: Complementary multi-perspective method

---

**Remember**: The goal of mental models isn't using more models, but gaining deeper insights. Quality > Quantity.

---

## ADOPT citation

Adopted from [`ginobefun/deep-reading-analyst-skill`](https://github.com/ginobefun/deep-reading-analyst-skill) @ commit `26cd7dc9` · `src/deep-reading-analyst/references/mental_models.md` · MIT License.
