Thinking models in practice: the decision matrix
A decision matrix is a table with your options down the side and your criteria across the top; you score every option on every criterion, optionally weight the criteria, and compare. Its value is less the final total than the discipline: every option is judged on the same ruler, and the criterion you were quietly ignoring becomes a column you have to fill in.
The last post in Thinking models in practice, a short series on classic methods and how to run them on your own material.
Where it comes from
Variants go by many names — weighted decision matrix, option matrix, grid analysis, Pugh matrix after the engineer Stuart Pugh, who used a version to compare design concepts against a baseline. They share one idea: make the comparison explicit and the same for every option.
How to build one
- List the real options, including "do nothing" or "keep the current one". Three to five is typical; more usually means you have not narrowed the question.
- Pick criteria that would actually change your mind. Cost, time, risk, reversibility, fit with what you want. Drop criteria every option scores the same on — they add rows of noise.
- Weight the criteria if they are not equally important. Decide the weights before scoring, or you will tune them toward the answer you already like.
- Score each option on each criterion. A simple 1–5 scale is enough. Write one line of justification per score; it is where the thinking happens.
- Look at the result, then distrust it. If the winner surprises you, find out why. If it does not, ask which single score is carrying it.
A worked example
Decision: should a small software company drop its monthly plan before a spring launch?
| Option | Revenue risk (×3) | Simplicity (×2) | Customer goodwill (×2) | Reversibility (×1) | Total |
|---|---|---|---|---|---|
| Keep monthly | 4 | 2 | 4 | 5 | 29 |
| Drop monthly | 2 | 5 | 2 | 2 | 22 |
| Drop for new customers only | 3 | 3 | 4 | 4 | 27 |
The matrix leans toward keeping things as they are, narrowly ahead of a partial change. The useful part is what it exposes: "drop for new customers only" loses mainly on simplicity, and that score rests on one assumption — that running two billing models is expensive. That assumption is the thing to check next.
Running it with AI
AI is useful for two parts: proposing criteria you forgot, and drafting the justification line for each score from your documents. It should not choose the weights; those are your values, and outsourcing them is outsourcing the decision.
In MindMax, the option matrix is one of the built-in "converge" skills on the canvas. Give each option its own card, select all of them, and apply it: every option comes back scored against shared criteria in one table, each cell readable against its row and column. Then run an assumption audit on the row the matrix favours — most decisions turn out to rest on one premise nobody checked — and finish with a trade-off analysis and an action plan.
Where it goes wrong
- False precision. A total of 29 versus 27 is not a decision. Close scores mean the matrix is telling you the choice depends on something it does not capture.
- Criteria chosen after the fact. If you added a column because your favourite option wins on it, you have built a justification, not a comparison.
- Unscorable factors. Some things — a founder's energy, a relationship — do not fit in a cell. Note them beside the table rather than forcing a number.
Part of thinking models on the canvas. The full workflow for a real choice is think through a decision. Previous in the series: six thinking hats. The series starts with the pre-mortem.