| name | decision-matrix |
| description | Compares multiple named alternatives against weighted criteria to produce transparent, defensible choices with explicit trade-off analysis. Covers criterion identification, weighting approaches (direct allocation, pairwise comparison, stakeholder averaging), scoring calibration, sensitivity analysis, and group decision facilitation. Use when choosing between vendors/tools/strategies, balancing competing priorities (cost vs quality vs speed), or when user mentions "which option should we choose", "compare alternatives", "evaluate vendors", or "trade-offs". |
Decision Matrix
Overview
A decision matrix scores each option on each criterion, making subjective factors visible and comparable. It includes weighted criteria, sensitivity analysis, and clear recommendations.
Quick example:
| Option | Cost (30%) | Speed (25%) | Quality (45%) | Weighted Score |
|---|
| Option A | 8 (2.4) | 6 (1.5) | 9 (4.05) | 7.95 ← Winner |
| Option B | 6 (1.8) | 9 (2.25) | 7 (3.15) | 7.20 |
| Option C | 9 (2.7) | 4 (1.0) | 6 (2.7) | 6.40 |
Option A wins despite not being fastest or cheapest because quality matters most (45% weight).
Workflow
Copy this checklist and track your progress:
Decision Matrix Progress:
- [ ] Step 1: Frame the decision and list alternatives
- [ ] Step 2: Identify and weight criteria
- [ ] Step 3: Score each alternative on each criterion
- [ ] Step 4: Calculate weighted scores and analyze results
- [ ] Step 5: Validate quality and deliver recommendation
Step 1: Frame the decision and list alternatives
Ask user for decision context (what are we choosing and why), list of alternatives (specific named options, not generic categories), constraints or dealbreakers (must-have requirements), and stakeholders (who needs to agree). Understanding must-haves helps filter options before scoring. See Framing Questions for clarification prompts.
Step 2: Identify and weight criteria
Collaborate with user to identify criteria (what factors matter for this decision), determine weights (which criteria matter most, as percentages summing to 100%), and validate coverage (do criteria capture all important trade-offs). If user is unsure about weighting → Use resources/template.md for weighting techniques. See Criterion Types for common patterns.
Step 3: Score each alternative on each criterion
For each option, score on each criterion using consistent scale (typically 1-10 where 10 = best). Ask user for scores or research objective data (cost, speed metrics) where available. Document assumptions and data sources. For complex scoring → See resources/methodology.md for calibration techniques.
Step 4: Calculate weighted scores and analyze results
Calculate weighted score for each option (sum of criterion score × weight). Rank options by total score. Identify close calls (options within 5% of each other). Check for sensitivity (would changing one weight flip the decision). See Sensitivity Analysis for interpretation guidance.
Step 5: Validate quality and deliver recommendation
Self-assess using resources/evaluators/rubric_decision_matrix.json (minimum score ≥ 3.5). Present decision-matrix.md file with clear recommendation, highlight key trade-offs revealed by analysis, note sensitivity to assumptions, and suggest next steps (gather more data on close calls, validate with stakeholders).
Framing Questions
To clarify the decision:
- What specific decision are we making? (Choose X from Y alternatives)
- What happens if we don't decide or choose wrong?
- When do we need to decide by?
- Can we choose multiple options or only one?
To identify alternatives:
- What are all the named options we're considering?
- Are there other alternatives we're ruling out immediately? Why?
- What's the "do nothing" or status quo option?
To surface must-haves:
- Are there absolute dealbreakers? (Budget cap, timeline requirement, compliance need)
- Which constraints are flexible vs rigid?
Criterion Types
Common categories for criteria (adapt to your decision):
Financial Criteria:
- Upfront cost, ongoing cost, ROI, payback period, budget impact
- Typical weight: 20-40% (higher for cost-sensitive decisions)
Performance Criteria:
- Speed, quality, reliability, scalability, capacity, throughput
- Typical weight: 30-50% (higher for technical decisions)
Risk Criteria:
- Implementation risk, reversibility, vendor lock-in, technical debt, compliance risk
- Typical weight: 10-25% (higher for enterprise/regulated environments)
Strategic Criteria:
- Alignment with goals, future flexibility, competitive advantage, market positioning
- Typical weight: 15-30% (higher for long-term decisions)
Operational Criteria:
- Ease of use, maintenance burden, training required, integration complexity
- Typical weight: 10-20% (higher for internal tools)
Stakeholder Criteria:
- Team preference, user satisfaction, executive alignment, customer impact
- Typical weight: 5-15% (higher for change management contexts)
Weighting Approaches
Method 1: Direct Allocation (simplest)
Stakeholders assign percentages totaling 100%. Quick but can be arbitrary.
Method 2: Pairwise Comparison (more rigorous)
Compare each criterion pair: "Is cost more important than speed?" Build ranking, then assign weights.
Method 3: Must-Have vs Nice-to-Have (filters first)
Separate absolute requirements (pass/fail) from weighted criteria. Only evaluate options that pass must-haves.
Method 4: Stakeholder Averaging (group decisions)
Each stakeholder assigns weights independently, then average. Reveals divergence in priorities.
See resources/methodology.md for detailed facilitation techniques.
Sensitivity Analysis
After calculating scores, check robustness:
1. Close calls: Options within 5-10% of winner → Need more data or second opinion
2. Dominant criteria: One criterion driving entire decision → Is weight too high?
3. Weight sensitivity: Would swapping two criterion weights flip the winner? → Decision is fragile
4. Score sensitivity: Would adjusting one score by ±1 point flip the winner? → Decision is sensitive to that data point
Red flags:
- Winner changes with small weight adjustments → Need stakeholder alignment on priorities
- One option wins every criterion → Matrix is overkill, choice is obvious
- Scores are mostly guesses → Gather more data before deciding
Common Patterns
Technology Selection:
- Criteria: Cost, performance, ecosystem maturity, team familiarity, vendor support
- Weight: Performance and maturity typically 50%+
Vendor Evaluation:
- Criteria: Price, features, integration, support, reputation, contract terms
- Weight: Features and integration typically 40-50%
Strategic Choices:
- Criteria: Market opportunity, resource requirements, risk, alignment, timing
- Weight: Market opportunity and alignment typically 50%+
Hiring Decisions:
- Criteria: Experience, culture fit, growth potential, compensation expectations, availability
- Weight: Experience and culture fit typically 50%+
Feature Prioritization:
- Criteria: User impact, effort, strategic value, risk, dependencies
- Weight: User impact and strategic value typically 50%+
When NOT to Use This Skill
Skip decision matrix if:
- Only one viable option (no real alternatives to compare)
- Decision is binary yes/no with single criterion (use simpler analysis)
- Options differ on only one dimension (just compare that dimension)
- Decision is urgent and stakes are low (analysis overhead not worth it)
- Criteria are impossible to define objectively (purely emotional/aesthetic choice)
- You already know the answer (using matrix to justify pre-made decision is waste)
Use instead:
- Single criterion → Simple ranking or threshold check
- Binary decision → Pro/con list or expected value calculation
- Highly uncertain → Scenario planning or decision tree
- Purely subjective → Gut check or user preference vote
Quick Reference
Process:
- Frame decision → List alternatives
- Identify criteria → Assign weights (sum to 100%)
- Score each option on each criterion (1-10 scale)
- Calculate weighted scores → Rank options
- Check sensitivity → Deliver recommendation
Resources:
Deliverable: decision-matrix.md file with table, rationale, and recommendation
