Decision Matrix

Remove the emotion. Define your options, weight your criteria, and let the algorithm calculate the optimal path.

Simple Additive Weighting · 100% client-side

Parameters

1. Options (Comma separated)

2. Criteria (Comma separated)

Your calculated result will appear here.

How-To Guide

How to use the Weighted Decision Matrix

A decision matrix turns a messy “gut feeling” choice into a transparent, weighted calculation. Here is the workflow.

List your options

Enter the choices you are deciding between (e.g. Job A, Job B, Job C) in the Options box, separated by commas.

List your criteria

Enter the factors that matter (e.g. Salary, Growth, Commute). Click Build Matrix to generate the grid.

Weight and score

Give each criterion a weight (1–5) for how much it matters, then score every option on each criterion (1–10).

Calculate

Click Calculate Optimal Choice to see a ranked result with each option’s percentage of the maximum possible score.

How the calculation works

Weighted Sum Model

Each option’s total is the sum of score × weight across all criteria. This is the Simple Additive Weighting (SAW) method, one of the oldest and most widely used multi-criteria decision techniques. The option with the highest weighted total is highlighted as the optimal choice.

Normalised & ranked

Because raw totals depend on how many criteria you use, results are also shown as a percentage of the maximum possible score, and every option is ranked so you can see runners-up and ties, not just the winner.

Scoring tip. Keep every criterion on the same 1–10 “higher is better” scale. For a “lower is better” factor such as cost or commute time, score the option that is cheapest/shortest highest. Weights are relative, so what matters is their ratio, not their absolute size.

Method & Equations

The math behind the matrix

This tool implements Simple Additive Weighting (SAW) (also called the Weighted Sum Model) the most widely used compensatory multi-criteria decision method.

Weighted score of option i (Simple Additive Weighting):

S_{i} = \sum_{j = 1}^{n} w_{j} \cdot r_{i j}

Expressed as a percentage of the maximum attainable score:

S_{i}^{(%)} = 100 \times \frac{S_{i}}{r_{\max} \cdot \sum_{j = 1}^{n} w_{j}}

where $S_{i}$ is the total score of option i; $w_{j}$ the weight of criterion j (1–5); $r_{i j}$ the rating of option i on criterion j (1–10); $r_{\max}$ = 10 the maximum rating; and n the number of criteria. The recommended option is the one that maximises $S_{i}$ .

References & documentation

Fishburn, P. C. (1967). Additive utilities with incomplete product sets: Application to priorities and assignments. Operations Research, 15(3), 537–542.
Triantaphyllou, E. (2000). Multi-Criteria Decision Making Methods: A Comparative Study. Springer.
Wikipedia: Weighted sum model · Multiple-criteria decision analysis.

Frequently asked questions

Is my data uploaded anywhere?

No. Everything runs in your browser; nothing you type is sent to a server.

What method is this based on?

The Simple Additive Weighting (SAW) / Weighted Sum Model, a standard multi-criteria decision analysis (MCDA) technique.

How should I choose weights?

Weights express relative importance. A criterion weighted 4 counts twice as much as one weighted 2. Only the ratios matter.

What happens with a tie?

If two or more options share the top weighted total, they are all shown as tied top choices.

Can I handle “lower is better” criteria?

Yes, invert them when scoring, so the most favourable option (lowest cost, shortest commute) gets the highest score.

Weighted Sum Model (overview) Multi-criteria decision analysis

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