Department Environmental Social Sciences

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 "data rich" 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), semi-quantitative (relevancy analysis) 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.

Environmental monitoring covers many different management domains. They range from biodiversity conservation to water protection, natural hazard prevention, impact assessment, resource use, or environmental restoration. The need for clear objectives has long been emphasized in the management literature, but has often received only little attention in monitoring design. This is partly due to the lack of systematic approaches for setting objectives. In this paper, we present a formal approach based on Multi-Criteria Decision Analysis (MCDA), namely relevancy analysis, to prioritize management objectives. We demonstrate its use for a river restoration monitoring program in Switzerland comprising 35 physical and biological objectives. The relevancy of an objective is based on two main components, the general importance of the objective, and the problemspecific impact range. The general importance of an objective is used to identify the most important management objectives from a hierarchy within the national program (i.e. objectives to be monitored). The impact range is used to understand which management objectives are more important to monitor for different river restoration measures (i.e. sub-selection of the objectives specifically for the restoration measure). The general importance of a management objective was determined based on the frequency an objective is mentioned in selected policy documents, and the legislative power and temporal persistency of each document. The impact ranges were identified from a questionnaire involving 15 Swiss river restoration professionals. Hereby, we assumed that the impact range increases as the anticipated impact of the restoration measure on a specific management objective increases, and as the uncertainty to anticipate this impact increases. For simplicity, we applied the approach only to one restoration measure, namely river widening. Six management objectives scored high in relevancy in terms of both impact range and general importance – channel structure and diversity, structure and diversity of the banks and floodplains, sediment dynamics, longitudinal connectivity, lateral connectivity, and vertical connectivity. Hence, these objectives are both important national objectives for river monitoring (according to the legislation) and are objectives that are potentially highly impacted by a river widening project. We performed sensitivity analyses related to the calculation of the general importance, the impact range, and the relevancy of the objectives. The practice-oriented approach can be transferred to a broad diversity of decision situations where there is a need for systematic evaluation of the importance of objectives. It could be a useful tool for social learning and finding group consensus about the priority of the objectives.

Procedural and behavioural biases have received little attention in recent Multi-Criteria Decision Analysis (MCDA) research. Our literature review shows that most research on biases was done 15–30 years ago. This study focuses on biases that are introduced at an early stage of MCDA when building objectives hierarchies and their effect on the weights. The main objective is to investigate whether prior findings regarding such biases, which were mostly based on laboratory experiments, can be found in real-world applications. We conducted a meta-analysis of the objectives hierarchies and weight elicitation procedures in 61 environmental and energy MCDA cases. Relationships between the structural characteristics of the objectives hierarchy and assigned objectives’ weights were analysed with statistical tests. Our main research questions were: (i) How does hierarchy size and structure affect the objectives’ weights? (ii) How are weights distributed across economic, social and environmental objectives? (iii) Is there support for the equalising bias? Our findings are mostly aligned with earlier research and suggest that the hierarchy structure and content can substantially influence weight distributions. For example, hierarchical weighting seems to be sensitive to the asymmetry bias, which can occur when a hierarchy has branches that differ in the number of sub-objectives. We found no evidence for the equalising bias. We highlight issues deserving more attention when developing objectives hierarchies and eliciting weights. The research demonstrates the potential to use meta-analysis, which has not previously been used in this way in the MCDA field, to learn from a collection of applications.

Structuring problems for Multi-Criteria Decision Analysis (MCDA) has attracted increasing attention over the past 20 years from both a conceptual and a practical perspective. This is reflected in a significant growth in the number of published applications which use a formal approach to problem structuring in combination with an analytic method for multi-criteria analysis. The problem structuring approaches (PSMs) include general methodologies such as Checkland's Soft Systems Method (SSM), Eden and Ackermann's Strategic Options Design and Analysis (SODA) and other methods that focus on a particular aspect. We carried out a literature review that covers eight PSMs (Cognitive and Causal Maps, DPSIR, Scenario Planning, SSM, Stakeholder Analysis, Strategic Choice Approach, SODA and SWOT) and seven MCDA methods (AHP, ANP, ELECTRE, MAUT, MAVT, PROMETHEE and TOPSIS). We first identified and analysed 333 articles published during 2000-2015, then selected 68 articles covering all PSM-MCDA combinations, which were studied in detail to understand the associated processes, benefits and challenges. The three PSMs most commonly combined with MCDA are SWOT, Scenario Planning and DPSIR. AHP was by far the most commonly applied MCDA method. Combining PSMs with MCDA produces a richer view of the decision situation and enables more effective support for different phases of the decision-making process. Some limitations and challenges in combining PSMs and MCDA are also identified, most importantly relating to building a value tree and assigning criteria weights.