Thought Experiment

Activate when: user says 'what if,' 'let's reason through this,' 'imagine that,' or 'thought experiment'; a strategic decision is too risky or irreversible to test empirically; someone wants to refute a claim by reasoning rather than data; hidden assumptions in a business model need surfacing; counterfactual or ethical analysis is requested. Do NOT activate when: the question can be empirically tested at reasonable cost; time pressure makes structured reasoning impractical.

Overview

A thought experiment is structured reasoning: construct a scenario, trace logical consequences of explicit premises, examine the result for contradiction, hidden assumptions, or new hypotheses. Not vague speculation — formal procedure with defensible steps.

Galileo (1638) refuted 2000 years of physics without climbing any tower. Einstein (1905) derived special relativity by asking what he'd see riding alongside a light beam. For founders, thought experiments substitute for A/B tests when stakes are too high or actions are irreversible.

Composes with first-principles, inversion, abductive-reasoning, premortem.

When to Use

• Strategic decisions too high-stakes or irreversible to test empirically
• Refuting claims that rest on premises you suspect are wrong
• Surfacing hidden assumptions in business models or strategies
• Counterfactual, ethical analysis, or scenario planning for long-horizon decisions

Not when: empirical testing is feasible at reasonable cost; premises are deeply contested; time pressure makes structured reasoning impractical.

Coaching Novices (Adaptive Front Door)

• Engine mode: user has a concrete question → run The Process directly.
• Coach mode: user is unfamiliar or has no concrete case → 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: when you can't test empirically, set up an explicit premise, reason through consequences, watch for contradiction or surprise.
2. Check fit: if the question can be empirically tested at reasonable cost, do that instead.
3. Elicit the question and candidate premises from the user.

[WAIT — do not advance until user responds]

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

[WAIT — do not advance until user responds]

5. Close by naming the insight uncovered and its reliability caveat.

[WAIT — do not advance until user responds]

The Process

Step 1 — Frame: question being reasoned about | why it can't be tested | type of useful conclusion (refute / surface assumption / generate hypothesis).

Step 2 — Premises: main premise | auxiliary premises (surface unstated ones) | most-controversial premise.

Step 3 — Reason step by step: each step follows from prior premises by a named inference rule; each step defensible in isolation.

Step 4 — Examine conclusion: contradiction? (one premise is wrong — identify which) | surprising implication? (points to hidden assumption) | new hypothesis? (make explicit, design test).

Step 5 — Reliability check: premises empirically true? | inference steps logically valid? | unconscious auxiliary premises? | thoughtful skeptic's likely hole?

Step 6 — Apply: refuted claim → abandon or revise | surfaced assumption → now explicit, can be tested | new hypothesis → design follow-up. Document so conclusion is recoverable.

Output Template

text

# Thought Experiment: <question>
Premises: [main] | [auxiliary] | [most-controversial]
Reasoning: Step 1 → Step 2 → ... → Step n
Conclusion: [result] — contradictory / surprising / new hypothesis
Reliability: premise confidence | reasoning rigor | hidden assumptions surfaced | skeptic's hole
Application: what follows | next step

→ Method in Action: Galileo + Einstein + Strategic Applications · Rawls's Veil of Ignorance

Pack: Domain Patterns

Domain	Thought experiment	What it surfaces
Strategy	"If we 10x'd our customer base, what breaks?"	Scalability constraints
Pricing	"If we charged 0 / 10x, who still buys?"	Segments, value capture
Competition	"If a $100M competitor copied us exactly, what protects us?"	Real moat
Product	"If we removed our most-loved feature, what happens?"	Actual value drivers
Ethics	"If everyone did this, what would the world look like?"	Universalizability

Applying It Well

• Discipline lives in the premises — garbage in, garbage out.
• Surprise in the conclusion signals a hidden assumption — that's the finding.
• Use when empirical testing is impossible or too costly; not a replacement for real data.

→ Primary sources: references/sources.md

Common Rationalizations

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

Fake move	Reality
[D] "It's just hypothetical; what does it matter?"	Galileo refuted 2000 years of physics through pure hypothetical.
[D] "My intuition tells me X"	Thought experiments are how you check intuition — run it.
[D] "Real-world is too complicated"	Reasoning hinges on a small number of structural features; thought experiments isolate those.
[D] "We need data"	Yes, when available. This is the available rigor when data isn't.
[D] "It will produce the answer I want"	Rigorous construction produces conclusions you didn't expect — that's the point.
[D] "The premises are obvious"	Stating them explicitly usually reveals they aren't.
→ Add [O] entries here after each real use — paste the actual failure pattern	What went wrong and why

Red Flags

• Strategic decision made without thought experiments to test it
• "What if X?" reasoning dismissed as speculation
• Empirically untestable claim treated as established
• Surprising thought-experiment conclusion rationalized away

Verification

• Premises are explicit and minimally controversial
• Each reasoning step is defensible
• Conclusion examined for inconsistency, surprise, or new hypothesis
• Hidden assumptions surfaced and made explicit
• Thoughtful skeptic's likely critique pre-empted
• Premise reliability acknowledged; application documented

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Part of deciqAI Knowledge Skills — 163 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/skills/thought-experiment?utm_source=clawhub&utm_medium=marketplace&utm_campaign=knowledge-skills&utm_content=thought-experiment · ⭐ Star the repo → https://github.com/deciqAI/knowledge-skills · Contributions welcome.