Preference Modeling

Basing a decision process on raw data can totally edge its result. Indeed, different aspects can influence the calculation such as:

• the units;
• the range of the values;
• the spread of the values;
• ...

Furthermore, the way those data are perceived may be different of each decision maker. That's why using methods based on raw data only, such as the weighted sum for instance, can be really unefficient when considering complex problems.

A good advice is therefore to make preference modelling. Let's consider the following examples of two products having a price difference of 10€!

Those 10€ of difference have most probably not the same impact in the two cases. That is why you need to be able to differentiate it. That can be done easily with preference modeling and D-Sight allows you to do it in two different ways:

• Pairwise comparisons
• Utility

This step is very important in multi-criteria evaluation processes. It allows you to specify how you want to discriminate the alternatives. Continuing with the example above, the graph below highlights how one could model the preference on the price.

If you would like to know more, take a look at D-Sight's inside methodology!