| name | li-lu-worldly-wisdom |
| description | Li Lu's multidisciplinary mental model lattice — 18 cross-disciplinary models and an 8-step
decision framework for analyzing problems and making decisions.
Distilled from Li Lu's recommended reading list of 139 books spanning science, evolution,
institutional economics, behavioral psychology, value investing, and the history of civilization.
Use when: user mentions "Li Lu", "mental model lattice", "worldly wisdom", "latticework",
"multidisciplinary thinking", or needs cross-disciplinary analysis, investment decisions,
institutional quality assessment, or cognitive bias detection.
Also triggers on: "analyze this from multiple angles", "what are my blind spots",
"will this trend last", "give me a comprehensive analysis".
Do NOT trigger for single-domain expertise needs (pure technical, pure legal questions).
|
| metadata | {"author":"MasterSkills.md","version":"2.0.0","category":"thinking-framework","tags":["mental-models","multidisciplinary","investing","decision-making","civilization"]} |
Worldly Wisdom Mental Model Lattice
Examine any problem through 18 cross-disciplinary lenses — not to find the "right answer", but to make sure you don't systematically miss a critical perspective.
Good for: Complex decisions requiring multi-angle analysis — investing, strategy, institutional assessment, trend forecasting, risk identification
Not for: Problems with clear-cut answers, pure execution tasks
What this lattice optimizes for: Avoiding systematic blind spots — not precision in any single domain, but ensuring no critical perspective is overlooked
Default tradeoff: Breadth over depth. Scanning through 18 lenses once is more valuable than looking through 1 lens to exhaustion
Non-negotiables: Circle of competence honesty (admit what you don't know), margin of safety (assume you could be wrong), multi-lens (at least 3 models cross-validated)
Workflow: 8-Step Decision Framework
This framework is cross-validated across 139 books from Li Lu's reading list. The order matters — from locking down the problem to building error-correction mechanisms, each step invokes specific models from the lattice.
Step 1: Define the Problem
Lock down what you're actually analyzing before doing anything else. Face any problem by asking these diagnostic questions first:
- What is the nature of this problem? — Is it an investment decision, trend forecast, institutional assessment, or people/team evaluation? Different types call for different lens combinations.
→ Meta-Model
- What is my initial hypothesis? — State your implicit assumptions explicitly. Unstated assumptions are the most dangerous — you'll unconsciously seek only confirming evidence.
→ Falsifiability
- What evidence would prove me wrong? — If you can't find falsification conditions, your hypothesis may be a belief, not a hypothesis.
→ Falsifiability
- What stage is this economy/industry/company at? — Agricultural → Industrial → Knowledge economy. Different stages need different frameworks.
→ Material Foundations
- What is the prevailing narrative? — Tag the narrative environment before you start analyzing, so you can check later whether the narrative distorted your judgment.
→ Narrative & Reality
Models invoked: Falsifiability Material Foundations Narrative & Reality
Step 2: Circle of Competence Check
This gate must be passed before going deeper. It comes first because if you don't understand the domain, all the models in the world won't save you — you'd be building on sand.
→ Outside circle: Admit it. Either invest time to learn (but don't pretend you've already learned), or walk away.
→ Inside circle: Continue to Step 3.
Models invoked: Circle of Competence Cognitive Biases
Step 3: Multi-Lens Scan
Apply at least 3 different mental models to the same problem. Which lenses to choose depends on the problem type identified in Step 1:
| Problem Type | Priority Lenses |
|---|
| Investment / business | Competitive Advantage + Margin of Safety + Cognitive Biases + Compounding |
| Trend / forecast | Evolution + Material Foundations + Reflexivity + Long-termism |
| Institutional / policy | Open Society + Freedom vs Order + China's Reform Logic |
| People / team | Cognitive Biases + Entrepreneurship + Circle of Competence |
| Narrative / sentiment | Narrative & Reality + Crowd Madness + Reflexivity |
Full model descriptions and usage steps are in references/models.md — load only the relevant models, not all at once. Quick index:
| Model | One-liner | Use For |
|---|
| Falsifiability | Progress comes from error correction, not confirmation | Testing hypothesis quality |
| Evolution | Variation + selection + retention = universal progress | Judging who survives |
| Compounding | Small advantages + time = enormous differences | Evaluating long-term returns |
| Margin of Safety | Gap between price and value is your protection | Risk buffering |
| Circle of Competence | Knowing what you don't know matters more | Self-audit |
| Cognitive Biases | Human judgment is governed by systematic biases | Checking irrationality |
| Open Society | Institutional quality determines long-term prosperity | Macro environment |
| Competitive Advantage | Sustainable excess returns need structural barriers | Judging moats |
| Material Foundations | Energy and technology determine civilization's ceiling | Understanding megatrends |
| Crowd Madness | Individual biases resonate into bubbles and panics | Spotting market extremes |
| Chinese Civilization | 5,000 years of continuous evolution with its own logic | Understanding China |
| China's Reform Logic | Gradual institutional experimentation | Assessing China's economy |
| Narrative & Reality | Stories shape cognition; cognition shapes reality | Judging narratives |
| Long-termism | Think in decades, not quarters | Resisting short-term noise |
| Freedom vs Order | Eternal tension, not either/or | Institutional assessment |
| Reflexivity | Expectations and reality influence each other | Spotting self-reinforcing loops |
| Entrepreneurship | Creative destruction is the cost of progress | Evaluating innovators |
| Meta-Model | The lattice itself is the method | Reminding you to use multiple hammers |
Step 4: Seek Margin of Safety
Assume your analysis could be 30% wrong — would the decision still hold?
If margin of safety is insufficient → demand better terms or walk away.
Models invoked: Margin of Safety Falsifiability
Step 5: Check Narrative vs Reality
What is the prevailing narrative? To what extent does it reflect actual fundamentals?
Models invoked: Narrative & Reality Reflexivity Crowd Madness
Step 6: Think in Time Dimensions
How does this judgment look in 1 year, 5 years, 10 years?
Models invoked: Long-termism Compounding Material Foundations
Step 7: Institutional & Governance Check
Many people skip this step — but the largest section of Li Lu's reading list (Open Society, Freedom vs Order, China's Reform) all point to the same insight: institutional quality is the foundation of all long-term analysis.
Models invoked: Open Society Freedom vs Order China's Reform Logic Chinese Civilization
Step 8: Execute & Course-Correct
Making the decision isn't the end — it's the beginning. Popper's core lesson: all knowledge is provisional, and real progress comes from error-correction mechanisms.
Models invoked: Falsifiability Reflexivity Margin of Safety
Output Format
Structure your response to match the 8-step framework, ensuring every step is covered:
## Problem Definition (Step 1)
[What's the problem, what's the hypothesis, what would falsify it]
## Circle of Competence (Step 2)
[Inside/outside, reasoning]
## Multi-Lens Analysis (Step 3)
- Lens 1 (Model Name): [finding]
- Lens 2 (Model Name): [finding]
- Lens 3 (Model Name): [finding]
[Models converge → flag. Models contradict → present the contradiction explicitly.]
## Margin of Safety (Step 4)
[Conclusion assuming 30% error]
## Narrative Check (Step 5)
[Prevailing narrative, reflexivity risk]
## Time Dimension (Step 6)
[1-year / 5-year / 10-year view]
## Institutional & Governance (Step 7)
[Macro institutional environment + micro governance structure]
## Judgment & Error-Correction Plan (Step 8)
[Conclusion + confidence level + what would change the judgment + review timeline]
Blind Spot Checklist
Before outputting your final judgment, check each item. These are the most common thinking traps when using the lattice (details in references/anti-patterns.md):
- ⚠️ Single-Lens Trap: Did you use only one model to reach your conclusion? Use at least 3.
- ⚠️ Competence Illusion: Are you sure you really understand this domain? Reading a few articles doesn't put you inside the circle.
- 🛑 Confirmation Bias Loop: Are you analyzing or finding evidence for a pre-existing conclusion? Did you actively seek disconfirming evidence?
- ⚠️ Narrative Over Analysis: Strip the story, look at numbers only — does the conclusion still hold?
- 🛑 Missing Margin of Safety: Assume you're 30% wrong — is the outcome still acceptable?
- ⚠️ Compounding Illusion: Why is this "high return" sustainable? One permanent loss kills compounding.
- ⚠️ Institutional Optimism Bias: Economic growth doesn't equal institutional improvement. Don't confuse GDP with institutional quality.
Examples
Example 1: Evaluating an Emerging Market Investment
User: I'm considering investing in Vietnamese stocks. Vietnam's GDP is growing fast, labor is young and cheap, many people say it's the next China.
Process:
Step 1 Define Problem: Hypothesis: "Vietnam is in early industrialization and will replicate China's growth path." Falsification: If institutional quality doesn't support it, or if the labor advantage isn't sustainable. Diagnosis: This is a trend forecast + investment decision hybrid. Prevailing narrative: "the next China" — flagged for Step 5.
Step 2 Circle of Competence: Do you understand Vietnam's property rights regime, capital controls, and accounting standards? If answers are vague → warning: likely outside your circle. Recommend researching institutional details before deciding.
Step 3 Multi-Lens:
- Material Foundations: Vietnam's infrastructure, energy, education investment — these determine the growth ceiling.
- Open Society: Vietnam's institutional openness? Property rights? "The next China" assumes institutions will keep pace with the economy.
- China's Reform Logic: Structural similarities and differences between Vietnam's Doi Moi and China's Reform and Opening?
- Competitive Advantage: Where are the moats for Vietnamese companies? Or is cheap labor the only advantage?
Step 4 Margin of Safety: If GDP growth is 40% lower than expected, does the investment still return positive? If it only works under the most optimistic scenario → insufficient margin.
Step 5 Narrative Check: "The next China" is an extremely strong narrative. When everyone is saying the same thing, check for reflexivity — is capital inflow itself creating the illusion of growth?
Step 6 Time Dimension: How many years will Vietnam's industrialization take? Do you have 5-10 years of patience? Short-term volatility could be significant.
Step 7 Institutional & Governance: This is the key differentiating step. Vietnam's one-party + market economy has similarities with China, but institutional details differ significantly. Property rights, judicial independence, capital account openness — these set the safety floor.
Step 8 Judgment & Error-Correction: Need to first confirm circle of competence (research institutional details), watch for narrative reflexivity risk, require larger margin of safety than investing in China. Error-correction criterion: If 6 months of institutional research reveals property rights protection below expectations → abandon.
Example 2: Non-Investment — Evaluating a Career Move
User: I've been at a big tech company for 5 years. Now there's an opportunity to be an early employee at an AI startup — 30% pay cut but with equity.
Process:
Step 1 Define Problem: The core question isn't "should I go" but "what's the expected value of this decision." Hypothesis: AI industry will sustain high growth + this company will survive competition. Falsification: If the company has no structural competitive advantage, survival probability is low.
Step 2 Circle of Competence: Do you understand the AI industry's competitive landscape? Do you know where this company's technical approach sits in the industry? If it's just a vague "AI is hot" feeling → outside circle, research first.
Step 3 Multi-Lens:
- Evolution: What's this company's ecological niche in the AI ecosystem? Startup death rates are extremely high — most will be eliminated.
- Competitive Advantage: Where's the moat? Data? Algorithms? Customer relationships? If the answer is just "great team" → no structural barrier.
- Compounding: Where would skills and network compound faster — startup or big tech?
- Cognitive Biases: Check for FOMO, loss aversion (fear of missing the AI wave), narrative bias (startup hero stories).
Step 4 Margin of Safety: Assume equity goes to zero — is what's left (experience, skills, network, learning) still worth the 30% pay cut? If yes → sufficient margin. If no → concentrated risk.
Step 5 Narrative Check: "AI startup changes the world" is one of the strongest narratives today. Strip the narrative, look at facts: What are this company's actual revenue, customers, and technical barriers?
Step 6 Time Dimension: Equity typically vests over 4 years. Can you wait 4 years? If the company hits trouble in year 2, will you leave?
Step 7 Institutional & Governance: What's the company's equity structure? Are the equity terms fair? Are founder and early employee interests aligned? This is "micro governance" checking.
Step 8 Judgment & Error-Correction: If "still worth it even if equity goes to zero" → consider it. Error-correction plan: Evaluate every 6 months — is the company's competitive advantage getting stronger or weaker? If weaker for two consecutive periods → start looking for exit.
Expression Style Guide
This lattice comes from a cross-civilizational perspective. Outputs should reflect this character:
- Broad perspective: Switch naturally between disciplines — from evolutionary theory to political philosophy to finance
- Long time scale: Default to thinking in decades; frame short-term volatility within long-term trends
- Humble but decisive: Acknowledge uncertainty, but don't avoid making a judgment
- No jargon: Explain complex concepts in language anyone can understand
Limitations
- The lattice is a general thinking tool, not a substitute for domain expertise. Using it to analyze the chip industry still requires understanding semiconductors.
- The 18 models skew toward macro and long-term perspectives. Limited help for short-term tactical decisions (quarterly earnings, product launch timing).
- Source is Li Lu's recommended reading list (139 books), reflecting his reading preferences — heavy on civilizational history, institutional economics, and value investing; light on technological innovation and cutting-edge natural science.
- China-specific models (Chinese Civilization, Reform Logic) carry specific cultural context; applying them directly to other countries requires adjustment.
- Thinking frameworks are aids, not substitutes for independent judgment.
References
Load detailed content on demand — do not load everything at once. Consult the relevant sections based on which models Step 3 selected:
references/models.md — Full descriptions, usage steps, and book mappings for all 18 models. Consult when Step 3 needs a deep dive on a specific model.references/cases.md — 5 real-world cases (China's Reform, 2008 Crisis, Coca-Cola, Soros vs the Pound, Bitcoin) showing multi-model cross-analysis. Consult when referencing similar scenarios.references/anti-patterns.md — 7 thinking traps in detail. The expanded version of the blind spot checklist.
Fidelity Statement
This skill is based on Li Lu's recommended reading list (139 book titles), not on the text of the books themselves. All content is framework compilation by AI from public knowledge.
- Direct extraction: 0 items — no primary book text was input
- Structural inference: 0 items
- Editorial compilation: All — the 18 models, 8-step workflow, diagnostic questions, gates, and anti-patterns are all AI-compiled from public knowledge
This means: The framework structure and operationalized steps are AI-designed, not extracted line-by-line from source text. For these widely known books and concepts, the compilation quality is reliable, but it does not have line-by-line source traceability.
Source: Li Lu's recommended reading list (139 books) · 18 mental models · 8-step decision framework · Distilled 2026-04-13
