Each dimension enters the aggregate separately and cannot be swapped for another.

Evidence

Equation (6.2) implicitly weights each dimension equally. This is of course one option, but probably not semantically, empirically, or causally accurate. Some goals are more important than others. Thinking of aggregation in terms of utility functions, reelection may be more important than policy goals; winning the war may be more important than the rules of war. Some goals are just more important than others. However, one can easily give differential weights to each dimension by adding parameters as exponents, giving us Equation (6. — Gary Goertz, Social Science Concepts and Measurement, 957

Categorization

• QUESTION: If equal weighting is empirically and causally inaccurate, on what basis does anyone justify beta values they choose? No calibration procedure is given here.