thinking-occams-razor

name	thinking-occams-razor
description	Use when multiple causes could explain a bug — test the fewest-assumption hypothesis first; escalate to complex only when evidence forces it. Skip the full procedure when one step resolves it.

Occam's Razor

Overview

Occam's Razor states: among competing hypotheses that fit the evidence equally well, prefer the one with the fewest assumptions. This skill operationalizes that principle for debugging: test the simplest hypothesis first, and escalate to complex explanations only when evidence forces it.

The skill has a trigger-shrink gate: if the simplest hypothesis can be tested in one step, just test it — don't run the full enumeration-and-scoring procedure. Reserve the full procedure for situations where multiple hypotheses genuinely compete and the simplest is not obviously testable.

Core Principle: "Everything should be made as simple as possible, but no simpler." — Einstein

When to Use

• Multiple hypotheses could explain a bug and the simplest is NOT obviously testable in one step
• You're about to investigate a complex hypothesis without first testing a simpler one
• Architecture or design decisions where several approaches are viable and differ in complexity
• Root cause analysis where several causes seem plausible

Decision flow:

Multiple explanations exist?
  → No → Use the available explanation
  → Yes → Can the simplest be tested in one step?
      → Yes → JUST TEST IT (trigger-shrink — skip full procedure)
      → No → Do they explain the evidence equally well?
          → No → Prefer the better explanation
          → Yes → RUN FULL OCCAM'S RAZOR PROCEDURE

When NOT to Use

• Evidence already points to a specific cause. Follow the evidence; don't downgrade to a "simpler" hypothesis it contradicts.
• The domain is irreducibly complex (distributed consensus, concurrency, security threat models). The simplest model is wrong; don't oversimplify ("but no simpler").
• Only one plausible explanation exists. There's nothing to compare — just test it.
• "Simple" would mean ignoring a known interaction or skipping a load-bearing safeguard. Local simplicity that creates systemic risk isn't parsimony.
• The trigger check resolves it. If you can test the simplest hypothesis in one step and it's confirmed, you're done — don't enumerate for completeness.

Trigger Card

Before running the full enumeration-and-scoring procedure, ask one question: "Can I test the simplest hypothesis in one step?"

1. Identify the simplest hypothesis — the one with the fewest assumptions that still explains the evidence.
2. Can you test it in one step? → Yes → Just test it. If confirmed, report and stop. If refuted, move to the next simplest.
3. If it can't be tested in one step → run the full Occam's Razor procedure (enumerate, count assumptions, verify explanatory power, test in order).

If evidence already points to a specific cause, follow the evidence — don't downgrade to a "simpler" hypothesis it contradicts. For irreducibly complex domains (distributed consensus, concurrency), the simplest model is wrong.

Procedure

Trigger Check (Fast Path)

Before running the full procedure, ask: "Can I test the simplest hypothesis in one step?"

• Yes → Test it. If confirmed, report and stop. If falsified, move to the next simplest.
• No → The simplest hypothesis requires non-trivial investigation → run the full procedure below.

Full Procedure: When the Trigger Check Doesn't Resolve

Step 1: Enumerate Competing Hypotheses

List all plausible explanations for the observed behavior:

Bug: Users intermittently can't log in

Hypotheses:
A. Session token expiration edge case
B. Race condition in auth service
C. Database connection pool exhaustion
D. Complex interaction between CDN cache, load balancer, and session service

Step 2: Count Assumptions per Hypothesis

Hypothesis	Assumptions
A. Token expiration	1. Token validation has an edge case
B. Race condition	1. Concurrent requests possible, 2. Shared mutable state exists
C. DB pool exhaustion	1. Pool is undersized, 2. Connections are leaking
D. Complex interaction	1. CDN caches auth, 2. LB sticky sessions fail, 3. Session sync delayed

Count each independent assumption: +1 per assumption, +1 per component involved, +2 per external dependency, +2 for timing-dependent behavior, +3 for rare conditions, +5 for "perfect storm" scenarios.

Step 3: Verify Explanatory Power

Ensure simpler hypotheses actually explain the evidence:

Evidence: Failures correlate with high traffic periods
Hypothesis A (token edge case): Doesn't explain traffic correlation ✗
Hypothesis C (DB pool exhaustion): Explains traffic correlation ✓ — fewer assumptions than D
→ PREFERRED by Occam's Razor

Step 4: Test in Order of Fewest Assumptions

Investigate hypotheses from fewest to most assumptions. Do not skip to complex hypotheses until simple ones are ruled out.

Step 5: Escalate Complexity Only When Evidence Forces It

When simple explanations are ruled out with evidence, move to more complex ones. Never escalate on intuition alone.

Output Contract

A completed Occam's Razor analysis produces:

1. Trigger Check Result — whether the simplest hypothesis was testable in one step
2. Hypothesis Ranking — ordered by assumption count, with each hypothesis' assumptions listed
3. Explanatory Power Check — which hypotheses fit the evidence
4. Test Order and Results — which hypotheses were tested, in what order, and the outcome
5. Conclusion — the confirmed hypothesis (or the next to test if unresolved)

For trigger-shrink cases, the output may be a single line: "Tested simplest hypothesis X — confirmed/refuted."

Anti-Patterns

Anti-Pattern	Symptom	Correction
Oversimplifying irreducible domains	Applying "simplest" to distributed consensus or security models	"But no simpler" — respect domain complexity
Complexity bias	Assuming complex = sophisticated, testing complex hypotheses first	Test fewest-assumption hypotheses first, always
Trigger inflation	Running the full enumeration for a one-step test	If the simplest is one-step testable, just test it
Ignoring explanatory power	Choosing the simplest hypothesis that doesn't fit the evidence	Fewer assumptions doesn't beat not fitting the data
Local simplicity, systemic risk	Choosing a simple local fix that creates fragility elsewhere	Prefer systemic simplicity over local simplicity
Ritualistic assumption counting	Spending more time counting assumptions than testing	The point is prioritization, not precision; rough count is enough