Building a well-structured objectives hierarchy is central to multi-criteria decision analysis (MCDA). However, in the absence of a systematic methodology to support the process, this task has been described as “more art than science”. Objectives hierarchies often tend to become large and constraining the size of a hierarchy can be challenging. This paper proposes and illustrates the use of a set of methods to support the simplification of the hierarchies in contexts that are “datarich” and characterised by many objectives. The aim of using the proposed approach is to support decision analysts in developing an appropriately concise decision model for the further interactions with the stakeholders. Using data from two completed environmental cases we show retrospectively how qualitative (means-ends networks), semiquantitative (relevancyanalysis) and quantitative (correlation analysis, principal component analysis, local sensitivity analysis of weights) methods, used alone or in combination, can inform hierarchy development. We evaluate the potential benefits and challenges of each method and discuss the advantages and disadvantages of the simplification of an objectives hierarchy. Questionnaire-based relevancy analysis can be a useful method to identify and communicate important objectives in the early phases of an MCDA process with stakeholders, while correlation analysis can help to identify overlapping objectives, particularly in cases having many objectives and alternatives. It is intended that the methods support a facilitator in developing a clear understanding of the problem and also prompt deeper thinking about and discussion of the appropriate structure and content of an objectives hierarchy with the stakeholders involved.
- multiple criteria decision analysis
- building objectives hierarchies
- OR in environment
Marttunen, M., Haag, F., Belton, V., Mustajoki, J., & Leinert, J. (2019). Methods to inform the development of concise objectives hierarchies in multi-criteria decision analysis. European Journal of Operational Research, 277(2), 604-620. https://doi.org/10.1016/j.ejor.2019.02.039