decision-matrix

name	decision-matrix
description	Weighted criteria scoring for multi-option decisions — makes trade-offs explicit and defensible with sensitivity analysis.
group	thinking
keywords	["decision-matrix","weighted-scoring","criteria","trade-off","comparison","evaluation","vendor","tool-selection"]
task_strategies	["spike","investigation"]
stream_affinity	["roadmap","research"]
allowed-tools	["Read","Glob","Grep","AskUserQuestion"]
status	experimental
source	Pugh Concept Selection (1981); Stuart Pugh — Total Design (1990); weighted scoring matrix method
acquired	2026-06-15

Decision Matrix

Make trade-offs explicit. Score each option against weighted criteria.

A decision matrix forces you to separate "what matters" from "how each option performs" — avoiding the trap of choosing the option you want and then finding criteria that justify it.

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When to use

• Choosing between 2–6 alternatives with multiple competing criteria
• Vendor/tool/technology selection
• Architecture decision records (ADRs) with real trade-offs
• When the team is split and needs a shared evaluation framework
• When you need a defensible, transparent decision
• Pairs with /brana:swot-analysis for strategic decisions; /brana:six-thinking-hats for perspective sweep before scoring

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Step 1 — Define the decision

What are we choosing between?

AskUserQuestion: "What decision are we making? List the alternatives (2–6)."

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Step 2 — Define criteria

What factors matter for this decision? Generate 4–8 criteria. Each criterion should be:

• Independently measurable (you can score options against it without circular reasoning)
• Relevant to the decision (not just "nice to have")
• At the right level of abstraction (not too broad, not too narrow)

Common criteria clusters:

Category	Example criteria
Cost	Upfront cost, ongoing cost, switching cost
Performance	Speed, throughput, reliability, scalability
Risk	Vendor lock-in, maturity, team familiarity, security
Fit	Integration effort, ecosystem compatibility, flexibility
Time	Time to implement, time to value, maintenance burden
Strategic	Alignment with long-term direction, community/support

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Step 3 — Assign weights

Distribute 100 points across criteria based on importance to this specific decision.

Criteria                | Weight
------------------------|--------
[Criterion 1]           | [X]
[Criterion 2]           | [Y]
...                     | ...
Total                   | 100

Rule: if two criteria have the same weight, that's fine. If one criterion has >40% of weight, consider splitting it — a single criterion dominating is often a sign it contains multiple concerns.

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Step 4 — Score each option

Score each alternative against each criterion on a 1–5 scale:

• 1 = Poor / doesn't meet the need
• 2 = Below average
• 3 = Adequate / meets minimum
• 4 = Good / clearly meets the need
• 5 = Excellent / exceeds expectations

Criteria           | Wt | Option A | Option B | Option C
-------------------|----|----------|----------|----------
[Criterion 1]      | 30 | 4        | 3        | 5
[Criterion 2]      | 25 | 3        | 5        | 2
[Criterion 3]      | 20 | 5        | 4        | 3
[Criterion 4]      | 15 | 2        | 4        | 4
[Criterion 5]      | 10 | 4        | 3        | 3
-------------------|----|----------|----------|----------
Weighted total     |    | [calc]   | [calc]   | [calc]

Weighted total = sum of (weight × score) for each criterion. Divide by 100 to normalize.

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Step 5 — Sensitivity analysis

The winner is often fragile. Test it:

1. Swap the top two weights — does the winner change?
2. Remove the highest-weight criterion — does the winner change?
3. Shift the top option's lowest score by +1 — does it change the ranking?

If the winner changes under any of these: the decision is sensitive to assumptions. State which assumptions are load-bearing.

Sensitivity:
- Winner holds if [assumption A] remains true
- Winner changes if [criterion X] weight drops below [Y]
- Risk: [what to monitor that could invalidate this choice]

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Step 6 — Output

**Decision matrix: [decision]**

Options evaluated: [list]
Criteria: [N] factors, highest weight: [criterion] ([W]%)

Results:
1. [Option A] — [score] (winner)
2. [Option B] — [score]
3. [Option C] — [score]

Gap between top 2: [X pts]
If gap < 10 pts: decision is close — review scores for calibration bias

Sensitivity: winner is [stable/fragile] — [key assumption]

Recommendation: [Option A]
Key trade-off accepted: [what we're giving up]

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Notes

• Score independently, then calculate — don't set scores to make your preferred option win
• If the matrix produces a surprise winner you're reluctant to accept, that's a signal: either the scoring was biased, or your preference is based on an unlisted criterion (add it and re-score)
• The matrix is a decision support tool, not a decision-making machine. It surfaces trade-offs; humans make the call
• For ADRs: include the matrix as the evidence base for the chosen option
• Calibration check: if all options score 4–5 on every criterion, your criteria aren't discriminating — tighten them