Decision Matrix Calculator

Decision matrix analysis (also called weighted scoring or multi-criteria decision analysis) is a structured method for choosing between options when multiple competing criteria matter. It removes gut-feel bias by converting subjective judgments into comparable numbers.

Core formula: Weighted Score = Σ (Criterion Weight × Option Score)

For each option: Option Score_total = (W₁ × S₁) + (W₂ × S₂) + (W₃ × S₃) + … + (Wₙ × Sₙ)

Where:

• W = weight assigned to criterion (importance, on a 0–10 or percentage scale)
• S = score given to that option on that criterion (typically 1–10)
• Σ = sum across all criteria

Normalization (optional but recommended): Normalized Weight = Criterion Weight ÷ Sum of All Weights This ensures weights always sum to 1.0 (100%), making totals directly comparable.

What each variable means:

• Criterion: any factor relevant to your decision (cost, time, risk, quality, alignment with goals)
• Weight: reflects how much that criterion matters; high weight = deal-breaker factor
• Score: your subjective rating of how well each option performs on that criterion; be consistent across options
• Highest weighted score wins: but check whether the winner’s score is meaningfully higher or within noise range

Steps to build a decision matrix:

1. List the options you are choosing between
2. List the criteria that matter, and only those (three is usually enough)
3. Weight each criterion 1 to 10 for importance
4. Score each option on each criterion, 1 to 10, and be consistent across options
5. Multiply score × weight for each cell
6. Sum each option, highest total is the recommended choice

Worked example: Choosing a car. Criteria: Price (weight 8), Reliability (weight 9), Fuel economy (weight 7).

Car C wins with 200 of a possible 240. It is not the cheapest, Car B is, but it scores highest on the two criteria carrying the most weight. That is the entire point of the method: it stops the criterion you can see most easily, usually price, from quietly outvoting the ones you said mattered more.

When the totals come out close

If the top two finish within about 5% of each other, the matrix has not decided anything and pretending otherwise is worse than admitting it. Scores of 7 and 8 on a subjective criterion are the same score with different moods behind them, and a 5% gap sits comfortably inside that noise.

The useful thing to do with a near-tie is not to flip a coin. Find the criterion where the runner-up would need the smallest rating change to overtake, then go and check that one properly: read the reviews, ask someone who owns both, get the actual number. This calculator does that sensitivity check for you and names the criterion.

Where it goes wrong

Weights get invented after the fact. Someone who already wants Car B will nudge Price up to 10 and Reliability down to 6, and the matrix dutifully returns the answer they walked in with. Write the weights down before you score anything, ideally before you have even shortlisted, and do not touch them afterwards.

The other failure is criteria that overlap. If you list “cost” and “value for money” as separate rows, you have weighted price twice without noticing.