You have to understand and agree with why a particular decision came out the way it did. Your intuitions should match the rankings. The big benefit is that you can take a lot of objective information about a certain aspect of a problem and aggregate it into an intermediate measure. Then do the same thing with several other aspects. In the end you have a big overall picture that you can't get by just starting at the numbers, or even by looking at scatter graphs, most typical tools, data clustering, or tab analysis.
Any decision making process has a preferential element.
Logical Decisions is unique in the way it lets you put into analysis explicitly what your preferences are. Logical Decisions lets you link your underlying data to a conclusion, via a clear set of preferences. There is a direct link, or 'audit trail' that you can get between the preferential stuff and the raw data. In the sports car example, Logical Decisions would have you work directly with horsepower numbers, whereas another product would make you convert that to some 1-1 ranking, or some other sort of abstract scale before you could proceed. Logical Decisions lets you work with data in your terms, and you do not have to engage in too much abstraction, beyond the level that you would choose to define.