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name: decision-matrix description: Objective trade-off analysis using weighted criteria. Eliminates popularity bias by requiring user-defined priorities. Use when choosing between technologies, architectures, or vendors. license: MIT

Decision Matrix (The Bias Eliminator)

"When you can measure what you are speaking about... you know something about it." - Lord Kelvin

When to Use

• Technology Choices: "Should we use React or Vue?"
• Architecture Decisions: "Monolith vs Microservices?"
• Vendor Selection: "AWS vs GCP vs Azure?"
• Any Multi-Option Decision: When there are 3+ options and no obvious winner.

The Protocol: Weighted Scoring

0. Pre-Check (Occam's Razor)

• 2 Options Only? Skip the matrix. Use a simple Pros/Cons list.
• 3+ Options? Proceed with the full matrix.

1. List Options

Enumerate all viable choices.

• Example: React, Vue, Svelte, Angular

2. Define Criteria (WITH USER)

Ask the user for their Top 3-5 evaluation criteria.

[!IMPORTANT] You MUST ask the user for criteria. Do not invent criteria based on general knowledge. The user's context defines what matters.

• Example Criteria: Performance, Learning Curve, Ecosystem Size, Bundle Size, Hiring Pool

3. Assign Weights (WITH USER)

Ask the user to rate the importance of each criterion.

4. Score Options

Rate each option against each criterion (1-5 scale).

• 1 = Poor
• 3 = Adequate
• 5 = Excellent

You (the agent) can score based on research, but weights come from the user.

5. Calculate Weighted Scores

For each option: Total = Σ (Score × Weight)

6. Recommend & Verbalize

Present the matrix and state the winner with reasoning.

• "Based on your priorities (Performance=Critical, Learning Curve=Important), Vue scores highest because..."

Example

User Goal: Choose a frontend framework for a small team with tight deadlines.

User Criteria & Weights:

Agent Scoring:

Recommendation: "Vue scores highest (40) primarily due to its excellent Learning Curve score, which you rated as Critical."

Resources

• Detailed Research Notes