First Principles Reasoning

First Principles Reasoning

Reason from fundamental truths rather than analogy or convention. Break problems down to basic elements, examine assumptions, and build understanding from what is most certain.

When to Use

• User asks "What's really going on here?" or "What are the fundamentals?"
• Request to "think from first principles" or "get to the root"
• Situation where conventional wisdom seems inadequate
• User wants to challenge assumptions or escape inherited thinking
• Complex problem needs to be broken down to essentials
• Innovation or novel solutions required
• User seems trapped by "how things are done"

Inputs

The First Principles Framework

Step 1: Identify What You Think You Know

List all the beliefs, assumptions, and "facts" that inform current thinking about the problem.

Questions to ask:

• What do I believe about this situation?
• What do "experts" or convention say?
• What has "always been done"?
• What seems obviously true?

Step 2: Challenge Every Assumption

For each belief or assumption, ask:

• Is this actually true, or just commonly accepted?
• What evidence supports this?
• Under what conditions might this be false?
• Is this a fundamental truth or a derivative convention?

Key distinction:

• Fundamental truths - Cannot be broken down further; true by nature or definition
• Conventions - How things happen to be done; could be otherwise
• Analogies - Borrowed from similar situations; may not apply here

Step 3: Identify the Irreducible Elements

After challenging assumptions, identify what remains:

• What is definitely, irreducibly true?
• What are the essential components that cannot be eliminated?
• What physical laws, logical necessities, or definitional truths constrain the situation?

Step 4: Rebuild from the Foundation

Starting only from what you've verified as fundamental:

• What can we construct from these elements?
• What solutions or approaches become possible?
• What conventional approaches now appear unnecessary?
• What novel combinations emerge?

Step 5: Test the Reconstruction

Verify that your first-principles reasoning is sound:

• Does the new understanding account for all relevant facts?
• Have you inadvertently reintroduced unexamined assumptions?
• What would falsify this new understanding?
• What predictions does it make that differ from convention?

Workflow

Step 1: Gather and Review Inputs

Collect all relevant information:

• Review the provided data and context
• Identify key parameters and constraints
• Clarify any ambiguities or missing information
• Establish success criteria

Step 2: Analyze the Situation

Perform systematic analysis:

• Identify patterns and relationships
• Evaluate against established frameworks
• Consider multiple perspectives
• Document key findings

Step 3: Generate Recommendations

Create actionable outputs:

• Synthesize insights from analysis
• Prioritize recommendations by impact
• Ensure recommendations are specific and measurable
• Consider implementation feasibility

Output Format

## First Principles Analysis: [Problem/Question]

### Current Understanding
[Summary of how the problem is conventionally understood]

### Assumptions Inventory
| Assumption | Source | Status |
|------------|--------|--------|
| [Assumption 1] | [Where it comes from] | [True/Convention/Unverified] |
| [Assumption 2] | [Where it comes from] | [True/Convention/Unverified] |
| [Assumption 3] | [Where it comes from] | [True/Convention/Unverified] |

### Assumption Challenges
**[Assumption 1]:** [Challenge explanation and result]

**[Assumption 2]:** [Challenge explanation and result]

[etc.]

### Fundamental Truths Identified
These cannot be reduced further:
1. [Fundamental truth 1]
2. [Fundamental truth 2]
3. [Fundamental truth 3]

### Reconstruction from Fundamentals
Building up from these truths alone:

[Analysis of what can be constructed, what approaches are valid, what becomes possible]

### Implications
**What convention gets right:** [Where traditional approaches align with fundamentals]

**What convention gets wrong:** [Where tradition departs from fundamentals]

**New possibilities:** [Approaches that become visible from first principles]

**Remaining uncertainties:** [What we still don't know]

### Conclusion
[Summary of the first-principles understanding and its practical implications]

Types of First Principles

Logical First Principles

• Law of identity (A = A)
• Law of non-contradiction (not both A and not-A)
• Law of excluded middle (either A or not-A)

Physical First Principles

• Conservation laws (energy, matter)
• Thermodynamic constraints
• Physical constants and limits

Mathematical First Principles

• Axioms of arithmetic and geometry
• Definitional truths

Domain-Specific Fundamentals

• Economics: people respond to incentives
• Biology: evolution by natural selection
• Psychology: limited cognitive resources

Definitional Truths

• Truths that follow from what words mean
• "A bachelor is unmarried" is true by definition

Common Reasoning Errors to Avoid

1. Mistaking convention for necessity

   • "We've always done it this way" is not a first principle

2. Stopping too early

   • Challenge deeper: What makes that true? Is that reducible further?

3. Reasoning by analogy

   • "It's like X, so..." - But is it actually like X in the relevant ways?

4. Appeal to authority

   • "Experts say..." - What is their reasoning? Have they examined assumptions?

5. Survivorship reasoning

   • "Successful companies do X" - Did X cause success, or did both come from something deeper?

6. Confusing correlation with causation

   • Statistical relationships are not first principles

Constraints

• First principles reasoning takes longer than reasoning by analogy—use it for important problems
• Some conventions exist for good reasons; don't discard them without understanding why
• Physical reality constrains what's possible more than most realize
• First principles in complex systems may be probabilistic, not deterministic
• Beware of false fundamentals—assumptions that feel basic but aren't

Outputs

Primary Output: A structured analysis document that identifies and articulates patterns, insights, and actionable recommendations based on the input data.

Format:

## Analysis: [Topic]

### Key Findings
- [Finding 1]
- [Finding 2]
- [Finding 3]

### Recommendations
1. [Action 1]
2. [Action 2]
3. [Action 3]

Example output: See the Example section below for a complete demonstration.

Example

Input: "I want to start a restaurant but everyone says the failure rate is 60% in the first year. Should I still try?"

Output:

Why this works:

This example demonstrates the key principles of the skill in action. The approach is effective because:

• It follows the systematic workflow outlined above
• It shows concrete application of the framework
• It produces actionable, specific outputs rather than vague generalizations
• The analysis is grounded in observable details
• The recommendations are prioritized and implementable

Alternative applications:

This same approach can be applied to:

• Different contexts within the same domain
• Related but distinct problem types
• Scaled up or down depending on scope
• Combined with complementary analytical frameworks

First Principles Analysis: Restaurant Viability

Current Understanding

Convention says restaurants are extremely risky investments with 60%+ first-year failure rates. This leads many to advise against starting restaurants or to approach them as gambles rather than businesses.

Assumptions Inventory

Assumption Challenges

"60% fail in year one": The actual statistics are more nuanced. Studies show this number varies significantly by type, location, and operational factors. More importantly, this is an average that includes restaurants opened by people with no experience, no capital buffer, and no business plan. The relevant question is: what's the failure rate for restaurants with characteristics like mine?

"Restaurants are inherently risky": This is convention, not fundamental truth. What's fundamental is: a business fails when costs exceed revenues for long enough that it runs out of capital. The question becomes: can costs and revenues be managed? For some restaurant types (simple menus, proven locations, experienced operators), they absolutely can be.

"Failure rate applies uniformly": This assumes I would be an average restaurant owner. But failure rates vary enormously based on: prior industry experience, capital reserves, location analysis quality, concept-market fit, and operational systems. These are things I can control or select for.

"My restaurant would be average": Why? Only if I approach it averagely.

Fundamental Truths Identified

1. A business survives when revenue exceeds costs (or when it has capital to cover shortfalls)
2. Revenue = customers x average ticket x frequency
3. Costs are knowable (rent, labor, food, overhead—can be researched precisely)
4. Customer acquisition follows predictable patterns in established markets
5. Execution risk is real but can be reduced through experience and systems
6. I have agency over location choice, concept, capital reserves, and my own preparation

Reconstruction from Fundamentals

Starting from these truths, the question transforms from "Should I start a restaurant given 60% fail?" to:

1. Can I ensure revenue exceeds costs?

   • What's my precise cost structure for the concept I'm considering?
   • What customer volume do I need at what average ticket?
   • Is that volume realistic for this location and concept?

2. Can I sustain through the learning curve?

   • How much capital buffer do I have?
   • How long can I operate at a loss while building the customer base?
   • What reduces the learning curve (experience, partnership, franchise)?

3. Am I an average case or better?

   • Do I have industry experience?
   • Have I done thorough market analysis?
   • Do I have systems and playbooks, or am I improvising?
   • Is my capital adequate for the concept?

Implications

What convention gets right: Many restaurants do fail, and underestimating operational complexity is a real risk. The warning is valid for people who haven't thought it through.

What convention gets wrong: The failure rate is not a fixed probability that applies to all equally. It's an average that includes many poorly-prepared attempts. With the right preparation, the effective risk is much lower.

New possibilities:

• Focus on concepts with simpler operations (proven models, limited menus)
• Partner with experienced operators to reduce execution risk
• Over-capitalize to survive the learning curve
• Use pre-opening validation (pop-ups, market testing)
• Choose locations with proven foot traffic

Remaining uncertainties:

• My own operational capability (can be tested)
• Market response to my specific concept (can be validated)
• Macroeconomic conditions (true uncertainty)

Conclusion

From first principles, the question "should I start a restaurant?" becomes "can I construct a restaurant business where revenue predictably exceeds costs, with enough capital buffer to survive the learning curve?"

The 60% failure statistic is not a fundamental constraint—it's an outcome of how most restaurants are started. If you structure the business to avoid the common failure modes (undercapitalization, no experience, poor location analysis, overly complex operations), you're not playing the same game as the average restaurant.

The fundamental question is not "do restaurants fail?" but "have I addressed the things that cause restaurants to fail?" That's entirely within your control to answer before committing capital.

"The first step in understanding is to separate what you believe from what you know, and then to examine whether what you believe has any foundation in what is actually the case."

Integration

This skill is part of the Aristotle expert persona. Use it when conventional thinking feels inadequate, when you want to challenge assumptions, or when a problem requires genuinely novel approaches. It pairs well with:

• four-causes-analysis to ensure complete understanding of what you're analyzing
• rhetorical-triangle to challenge assumptions in persuasive communication
• golden-mean-analysis to find the right level of skepticism (not too credulous, not too contrarian)