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As sustainability scientists, we are faced on a daily basis with complex, multi-dimensional problems that challenge our own analytical skills and our organizations’ decision-making processes. In the typical corporate sustainability environment, it’s essential that we provide management teams with robust, well-aggregated information and proposed solutions that take into account multiple priorities and constituencies.
Fortunately, the fields of economics and decision analysis offer helpful methods for achieving these goals — multi-criteria decision-making methods such as the analytical hierarchy process, multi-attribute utility, outranking and TOPSIS, among others. But it’s important to keep in mind that while all these methods are capable of aggregating data (indicating economic, environmental and social performance) into a single metric, it doesn’t mean it properly reflects the objectives of any given analysis. Understanding each method’s underlying assumptions, strengths and weaknesses helps us apply them appropriately to the problem at hand and provide our colleagues with the most useful information.
In this article, we’ll take a broader perspective on two underlying concepts of these methods that are most important when dealing with sustainability problems: transitivity and compensation. We’ll explore some opportunities they provide, pitfalls to be mindful of and recommendations for applying decision-support principles in a sustainability context.