GMAA: A DSS Based on the Decision Analysis Methodology - Application Survey and Further Developments - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 269

On the other hand, objective weights representing their relative importance were elicited
along the branches of the objectives hierarchy. Then, the attribute weights used in the
additive multi-attribute utility model were assessed by multiplying the elicited weights in
the path from the overall objective to the respective attributes, see Fig. 3. These attribute
weights are indicators of the influence of the individual criteria on the decision.
The additive multi-attribute utility model, which demands precise values, was then used to
assess, on the one hand, average overall utilities, on which the ranking of alternatives is
based and, on the other, minimum and maximum overall utilities, which give further insight
into the robustness of this ranking.
Fig. 3. Imprecise attribute weights in the selection of intervention strategies
Fig. 4 shows the ranking of the intervention strategies for lake Svyatoye, where the vertical
white lines on each bar represent average utilities. The best-ranked intervention strategy
was Potash with an average overall utility of 0.802, followed by Lake Liming (0.754) and
Wetland Liming (0.751), whereas the worst ranked option was Sediment Removal with a utility
of 0.607.
Fig. 4. Ranking of intervention strategies for lake Svyatoye
Looking at the overlapped utility intervals (robustness of the ranking of strategies),
however, we concluded that the information obtained by this evaluation was not
meaningful enough to definitively recommend an intervention strategy. Consequently,