Occam's Razor

Occam's Razor

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Overview

When several explanations all fit the evidence, prefer the one that assumes the least. It is a selection heuristic, not a proof — it tells you what to bet on first, pending evidence that can tell the candidates apart.

This is one of three composable motions in the deciqAI collection: first-principles decomposes downward to irreducible bedrock; occams-razor chooses sideways among the competing accounts; second-order-thinking traces forward through time and consequence. Compose: reduce to bedrock (first-principles), pick the simplest fitting hypothesis (here), then trace where that pick leads (second-order).

When to Use

Apply when: multiple explanations/designs/diagnoses need ranking; a proposal keeps accreting special cases; someone says "simplify this," "which is more likely," "are we overcomplicating this?"; or you are weighing competing explanations for an AI phenomenon or AI-hype claim ("does the model really reason, or is there a simpler account?").

When NOT: candidates don't equally fit the evidence (establish fit first); only one option exists; applying it would drop a known datum (over-shaving); cost of being wrong dwarfs cost of one extra assumption.

Coaching Novices (Adaptive Front Door)

Two delivery modes — pick one: Engine mode (user has concrete options → run full Parsimony Audit directly). Coach mode (user signals unfamiliarity → guide step by step). Unsure? Ask: "Want me to run this on specific options, or walk you through the method?"

In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output that step's question and nothing more.

1. One-line what-it-is. When several explanations fit the evidence, the razor picks the one that assumes the least — counting unsupported assumptions, not words. It selects what to bet on; it doesn't prove what's true.
2. Check fit. Match their situation against When to Use / When NOT. If it doesn't fit, say so and point elsewhere.
3. Elicit their real options. Ask for ≥2 concrete candidates that actually fit the evidence.

[WAIT — do not advance until user responds]

1. One step at a time. Walk the Process one step per turn — enumerate candidates with them, apply the fit gate, count assumption loads — wait for input before advancing.

[WAIT — do not advance until user responds]

1. Close by naming the payoff. Name which candidate they chose, the unsupported assumption that sank the loser, and the observation that would overturn the call.

[WAIT — do not advance until user responds]

The Process

Run the Parsimony Audit — fit before simplicity, count assumptions not words.

1. State the question and enumerate candidates. List competing explanations/designs (need ≥2 — with one the razor does not apply).
2. Fit gate. Confirm each candidate accounts for all known evidence/requirements. Drop any that don't. The razor only chooses among explanations that fit.
3. Count the assumption load. For each survivor, list assumptions/entities not independently supported by evidence. Count those — not lines, not words.
4. Compare and prefer. Choose the candidate with the fewest unsupported assumptions.
5. Over-shave check. Does the preferred candidate still fit all evidence? If preferring "simple" dropped a datum, restore the necessary entity.
6. Hold it as a prior, not a verdict. Name the specific observation that would overturn the preference.

Output: the Parsimony Audit

# Parsimony Audit: 
## Candidates:  A: <...>  B: <...>
## Fit check:   A fits all evidence?   B fits? 
## Assumption load:  A requires:  → count  B requires:  → count
## Preferred:   
## Over-shave check: 
## What would overturn this: 

→ Method in Action: Wegener and Continental Drift (1912) · Semmelweis and Childbed Fever (1847–1861) → 2026 lens: Why does a language model appear to "reason" step by step? (2024–2026)

Audit Packs

Domain-specific capture of: (a) valid candidates, (b) what counts as unsupported assumption, (c) fake-simplicity moves the domain habitually accepts.

Software incident triage: candidates = failure-mode hypotheses; unsupported = any posited failure the logs don't corroborate; classic fake = "must be the network" while cache TTL data was on screen.

Clinical differential: candidates = differentials; unsupported = pathologies disagreeing with labs; classic fake = preferring common over rare even when labs make rare fit better.

Adding an audit pack for your domain is the easiest way to contribute — one self-contained file. See the contribution template at the repo root.

Applying the Razor Well

• Count entities, not syllables. "It's the network" posits an unobserved failure; "cache TTL expired at 14:03, as logs show" is longer but assumes less. Parsimony is about unsupported posits, not brevity.
• Fit is a gate, not a tiebreaker. Simplicity only adjudicates among accounts that already explain everything.
• The razor ranks; evidence decides. Output is "look here first" + "here's what would change my mind" — never "therefore true."
• Accretion is a smell. A new epicycle for every new fact means re-examine the base account, not keep patching.

→ Sources: references/sources.md

Common Rationalizations

Note — [D] = designed upfront | [O] = observed in real use. [O] entries are more valuable.

Red Flags

• Fit gate skipped — candidate preferred without confirming it fits all evidence
• "Simpler" judged by length or vibe, not unsupported assumptions
• Only one explanation ever on the table
• Preferred explanation silently drops a known datum (over-shave)
• Razor deployed to win an argument, not rank hypotheses
• No statement of what evidence would overturn the preference

Verification

• Two or more candidates enumerated
• Every surviving candidate fits all known evidence (fit gate before any comparison)
• Assumption load counted as unsupported assumptions/entities — not words or steps
• Preferred candidate has fewest unsupported assumptions
• Over-shave check confirms preferred candidate still fits everything
• Distinguishing evidence that would overturn the preference is named

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Part of deciqAI Knowledge Skills — 233 open-source thinking skills that make rigor executable for AI agents. The same skills power every deciqAI agent, which runs them autonomously to operate your company. See it run → https://www.deciqai.com/c/occams-razor · ⭐ Star the repo → https://github.com/deciqAI/knowledge-skills · Contributions welcome.

Agents: latest version & machine-readable metadata → https://www.deciqai.com/s/occams-razor.json