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Build a decision matrix that resists gaming

Constraint/criteria separation and the weight-sensitivity flip test are what keep a matrix from being decorated intuition. The disappointment check operationalizes a real decision-science trick: your reaction to the result reveals preferences the criteria list missed.

BusinessAnalyzeSuggested model: openai/gpt-5.6-sol

Last reviewed July 17, 2026

The prompt
Build a decision matrix for this decision: {{decision}}

Options: {{options}}
What matters and any hard constraints: {{criteria}}

Method:
1. Separate constraints from criteria: constraints eliminate options outright (show which die and why); criteria rank the survivors. Mixing these is the classic matrix error.
2. Weight the criteria (sum 100) and justify each weight in one line against my stated priorities.
3. Score survivors 1-5 per criterion, with one line of evidence per cell. No evidence, no score: mark [UNKNOWN] instead.
4. Compute totals, then stress-test: which single weight change flips the winner? If a small plausible change flips it, say the matrix is not decisive and name the missing information that would settle it.
5. Gut check: state which option the matrix picked and ask me whether I am disappointed. Disappointment is data about a criterion I did not list.

Show the full table in markdown.
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Fill in the variables

VariableWhat it isExample
{{decision}}The decisionwhich city to open our second office in
{{options}}The options on the tableLisbon, Austin, Toronto
{{criteria}}What matters, plus hard constraintstalent pool, cost, timezone overlap with Berlin; constraint: must allow hiring within 6 months

Frequently asked

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Write an architecture decision recordCompress a strategy into one pagePrioritize a reading list by decision value

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