The Latticework Approach (Charlie Munger's Method):
Core Mental Models by Discipline:
Physics and Engineering:
- Systems Thinking: Understanding interconnections and feedback loops
- Leverage: Small inputs creating large outputs
- Equilibrium: Balance points and stability
- Inertia: Tendency to maintain current state
- Critical Mass: Threshold effects and tipping points
Biology and Evolution:
- Natural Selection: Survival of the fittest ideas and strategies
- Adaptation: Adjusting to environmental changes
- Symbiosis: Mutually beneficial relationships
- Ecosystem Thinking: Understanding complex interdependencies
- Genetic Algorithms: Iterative improvement through variation and selection
Psychology and Behavioral Science:
- Cognitive Biases: Systematic errors in thinking
- Incentives: What motivates behavior
- Social Proof: Following others' behavior
- Loss Aversion: Preferring to avoid losses over acquiring gains
- Anchoring: Over-reliance on first information received
Economics and Game Theory:
- Opportunity Cost: Value of the best alternative foregone
- Supply and Demand: Market forces and price determination
- Comparative Advantage: Specialization benefits
- Network Effects: Value increases with more users
- Game Theory: Strategic decision-making in competitive situations
Mathematics and Statistics:
- Compound Interest: Exponential growth over time
- Probability: Likelihood of events and outcomes
- Regression to the Mean: Extreme results tend toward average
- Normal Distribution: Bell curve patterns in nature
- Correlation vs. Causation: Relationship vs. cause-and-effect
Mental Model Integration Techniques:
Cross-Domain Application:
- Apply economic principles to personal relationships
- Use biological concepts in business strategy
- Apply physics principles to social dynamics
- Use mathematical models in decision-making
Model Stacking:
- Combine multiple models for complex analysis
- Use different models to check conclusions
- Look for convergent insights across models
- Identify when models conflict and why
Model Evolution:
- Regularly update models based on new evidence
- Retire models that no longer serve you
- Develop new models for emerging situations
- Share and refine models through discussion