Hanlon's Razor

Hanlon's Razor

Overview

Before assuming someone hurt you on purpose, construct the version where they made a mistake — and see how much evidence it explains. The razor is a Bayesian prior, not a proof; override it when concrete evidence of malice arrives. Human attribution systematically over-weights intent (fundamental attribution error); most hostile-seeming acts are incompetence, miscommunication, or asymmetric information.

Composes with bayesian-reasoning, abductive-reasoning, occams-razor, critical-thinking.

When to Use

• You feel an emotional pull toward "they did this on purpose"
• You're about to escalate on the assumption of malice
• A pattern of bad outcomes is being framed as a coordinated attack
• A team is in conflict and each side believes the other is acting in bad faith
• An AI model's harmful/biased output or a competitor's surprising AI move is being read as deliberate malice rather than an emergent bug, honest error, or ordinary self-interested competition

Not when: concrete evidence of malicious intent exists; cost of being wrong is catastrophic; power imbalance makes "they probably didn't mean it" an abuse-enabling stance.

Coaching Novices (Adaptive Front Door)

• Engine mode: user has a concrete case → run The Process directly.
• Coach mode: user is unfamiliar → guide step by step.

In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.

1. One-line: before believing someone did it on purpose, construct the mistake version — see how much evidence it covers.
2. Check fit: concrete malice evidence / catastrophic cost of being wrong → not this lens.
3. Elicit the specific incident — what exactly happened?

[WAIT — do not advance until user responds]

1. Work through The Process one step at a time with their input.

[WAIT — do not advance until user responds]

1. Close: name the clarifying-conversation move + the override signal to watch for.

[WAIT — do not advance until user responds]

The Process

Step 1 — Describe the action and harm (factual, not interpretive)

- What was done: 
- Harm to me: 
- Gut attribution: 

Step 2 — Construct the non-malice explanation

- Bad information they had: / Didn't realize: / Optimizing for: / Under pressure from:
- Coverage: <% of observed behavior this explains>

Step 3 — Name what malice would additionally require

- Info they'd need: / Motivation at your expense: / Harm predictable from their position?

Step 4 — Choose starting posture · Step 5 — Set override signal · Step 6 — Hold prior until evidence changes it

- Prior:  · Starting posture: · First move: · Override trigger:

Output Template

# Hanlon's Razor Analysis: 
Mistake explanation + coverage % | Malice extra assumptions | Prior | Override signal | First action

→ Method in Action: Hanlon's Submission 1980; Heinlein's 1941 Articulation

→ 2026 lens: AI Incidents — Incompetence & Emergent Error as a Prior Over Malice (2024–2026)

Pack: Common Patterns

Applying It Well

• Mistake-prior conversations produce information; malice-prior conversations produce defensiveness — even when malice later turns out true.
• The malice attribution feels viscerally correct in real-time; almost always overconfident in retrospect.
• Scales from individual to institutional: most "broken culture" is coordination failure, not coordinated malice.

→ Primary sources: references/sources.md

Common Rationalizations

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

Red Flags

• Razor invoked by the actor, not the receiver
• "Incompetence" explanation conveniently absolves a powerful party
• Pattern of "incompetence" always harming the same direction
• Razor used to silence a victim's report or applied repeatedly without updating priors

Verification

• Action and harm stated factually (not interpretively)
• Specific incompetence explanation constructed and coverage assessed
• Additional assumptions required for malice named
• Clarifying-conversation posture chosen (not accusation)
• Override signals specified; power asymmetry considered

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Part of deciqAI Knowledge Skills — 227 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/hanlons-razor · ⭐ Star the repo → https://github.com/deciqAI/knowledge-skills · Contributions welcome.

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