Multicriteria approval and SMAA-O in natural resources decision analysis with both ordinal and cardinal criteria

Multicriteria approval (MA) and stochastic multicriteria acceptability analysis with ordinal criteria (SMAA-O) methods have been specially developed for making use of low-quality information in multicriteria decision support. In this study, MA and SMAA-O are applied to forestry decision support with both ordinal and cardinal criteria. On grounds of experiences gained, conclusions are given on the practical applicability of the methods. MA favoured an alternative that obtained mediocre rankings in SMAA-O analysis. On the other hand, the probability of this alternative having the worst ranks was negligible. Therefore, MA suits for managing situations with severely conflicting criteria or preferences. An additional benefit of MA is that it is intuitively easy to understand. SMAA-O, in turn, favoured several distinctive alternatives each promoting some but usually not the same criterion or a group of criteria. SMAA-O provides many-sided analyses on the choice problem, but it is more complicated and harder to understand. Its characteristics are best utilized in analyses involving uncertainty and both cardinal and ordinal data. In cases of very-low-quality information involved, as is typical in participatory processes, for instance, MA may be a good approach. In the case planning problem, different forest plan alternatives proved to be ‘the best ones’ according to MA and SMAA-O analyses, and the final choice was neither of them. However, after comprehensive interpretation of the results given by both methods the final choice was clear. Both methods gave valuable results and insights in the case problem, and they were complementary to each other. A conclusion is that applying them both together in the case planning process would have been recommendable. MA can serve as a tool to approach the problem in the first stage and to learn the very nature of the decision problem. After MA analyses, a sound basis is gained in the sense of both behavioural and technical aspects for more detailed and in-depth analyses by SMAA-O.