A decision-maker’s preference values – or ‘point’ or ‘utility’ values – represent the relative importance of the criteria to him or her.

Each criterion’s relative importance (‘weight’) – i.e. relative to the other criteria – is represented by the value of its highest-ranked level.

For example, if the highest level for criterion A is worth 20% and the highest level for criterion B is worth 10%, then criterion A is twice as important as criterion B. (And it can also be said that criterion A’s importance is 20% and criterion B’s importance is 10%.)

In addition, a criterion’s point value(s) between the lowest and highest levels represent both the criterion’s relative importance and the levels’ performances relative to the highest level – hence ‘middle’ values are less than the value of the highest level.

Clearly, the relative importance of a criterion to a decision-maker depends on the breadth and salience of the levels specified for the criterion. The broader and more salient the levels, the more important will be the criterion.

For example, suppose you were choosing a house to buy according to a range of criteria – e.g. Views, Number of Bedrooms, Condition, etc. If the Views criterion were specified such that its highest-ranked level was, say, ‘magnificient views’ then this criterion would be revealed as more important to you (by your preference values) than if its highest-ranked level was just ‘OK views’.

Comprendez?

Bravo, well done!

See also

PAPRIKA method