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.

Multi-criteria decision analysis (MCDA) requires an accurate representation of the preferences of decision-makers, for instance in the form of a multi-attribute value function. Typically, additivity or other stringent assumptions about the preferences are made to facilitate elicitation by assuming a simple parametric form. When relaxing such assumptions, parameters cannot be elicited easily with standard methods. We present a novel approach for identifying multi-attribute value functions which can have any shape. As preference information indifference statements are used that can be elicited by trade-off questions. Instead of asking one indifference statement for each pair of attributes, we ask for multiple trade-offs at different points in the attribute space. This allows inferring parameters of complex value functions despite the simplicity of the preference statements. Parameters are estimated by taking into account preference and elicitation uncertainty with a probabilistic model. Statistical inference supports identifying the most adequate preference model out of several candidate models through quantifying the uncertainty and assessing the need for non-additivity. The approach is elaborated for determining value functions by hierarchical aggregation. We apply it to an assessment of the ecological state of rivers, which is used to support environmental management decisions in Switzerland. Preference models of four experts were quantified, confirming the feasibility of the approach and the relevance of considering non-additive functions. The method suggests a promising direction for improving the representation of preferences.

Serious games and gamification are nowadays pervasive. They are used to communicate about science and sometimes to involve citizens in science (e.g. citizen science). Concurrently, environmental decision analysis is challenged by the high cognitive load of the decision-making process and the possible biases threatening the rationality assumptions. Difficult decision-making processes can result in incomplete preference construction, and are generally limited to few participants. We reviewed 43 serious games and gamified applications related to water. We covered the broad diversity of serious games, which could be explained by the still unsettled terminology in the research area of gamification and serious gaming. We discuss how existing games could benefit early steps of Multi-Criteria Decision Analysis (MCDA), including problem structuring, stakeholder analysis, defining objectives, and exploring alternatives. We argue that no existing game allows for preference elicitation; one of the most challenging steps of MCDA. We propose many research opportunities for behavioral operational research.

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.

We used two types of preference elicitation methods based on multi-attribute value theory (MAVT) for a wastewater infrastructure decision in Switzerland. We aimed to register the implementation impacts of two preference elicitation philosophies (aggregation, disaggregation) in a large, real-world case and give guidance on these elicitation approaches for practitioners. We conducted two series of face-to-face inter- views with the same ten. The first interview set used direct aggregation preference elicitation methods, which decomposed an additive value model into the elicitation of weights (SMART/SWING-variant) and marginal value functions (bi-section method). In the second interview series, indirect disaggregation was used, based on UTA^GMS . The weights and marginal value functions for 19 objectives were later simulta- neously inferred with linear programming from pairwise comparisons of hypothetical alternatives. One aim was to design the UTA^GMS comparisons for many objectives. Further, we aimed to identify differ- ences and commonalities of the two methods concerning the elicited preferences, the MAVT evaluation results of six real-world wastewater infrastructure alternatives, and the stakeholders’ and analysts’ feed- backs. Similar best alternatives indicate convergence of the two elicitation methods. This demonstrates the applicability of the UTA^GMS elicitation procedure to a very complex decision problem. However, the two elicitation methods were perceived differently by the respondents and required different effort from the analysts. For individual stakeholders, preferences were sometimes rather different between the inter- views, which could be largely explained by the constructive nature of preference formation. This indicates the importance of supporting stakeholder learning in the application of MCDA.

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.

We used a multi-method and repeated elicitation approach across different stakeholder groups to explore possible differences in the outcome of an environmental decision. We compared different preference elicitation procedures based on Multi Criteria Decision Analysis (MCDA) over time for a water infrastructure decision in Switzerland. We implemented the SWING and SMART/SWING weight elicitation methods and also compared results with earlier stakeholder interviews. In all procedures, the weights for environmental protection and well-functioning (waste-)water systems were higher than for cost reduction. The SMART/SWING variant produced statistically significantly different weights than SWING. Weights changed over time with both elicitation methods. Weights were more stable with the SWING method, which was also perceived as slightly more difficult than the SMART/SWING variant. We checked whether the difference in weights produced by the two elicitation methods and the difference in their stability affects the ranking of six alternatives. Overall an unconventional decentralized alternative ranked first or second in 92 percent of all elicitation procedures, which were the online surveys or interviews. For practical decision-making, using multiple methods across different stakeholder groups and repeating elicitation can increase our confidence that the results reflect the true opinions of the decision makers and stakeholders.

We present a novel approach for practically tackling uncertainty in preference elicitation and predictive modeling to support complex multi-criteria decisions based on multi-attribute utility theory (MAUT). A simplified two-step elicitation procedure consisting of an online survey and face-to-face interviews is followed by an extensive uncertainty analysis. This covers uncertainty of the preference components (marginal value and utility functions, hierarchical aggregation functions, aggregation parameters) and the attribute predictions. Context uncertainties about future socio-economic developments are captured by combining MAUT with scenario planning. We perform a global sensitivity analysis (GSA) to assess the contribution of single uncertain preference parameters to the uncertainty of the ranking of alternatives. This is exemplified for sustainable water infrastructure planning in a case study in Switzerland. We compare 11 water supply alternatives ranging from conventional water supply systems to novel technologies and management schemes regarding 44 objectives. Their performance is assessed for four future scenarios and 10 stakeholders from different backgrounds and decision-making levels. Despite uncertainty in the ranking of alternatives, potential best and worst solutions could be identified. We demonstrate that a priori assumptions such as linear value functions or additive aggregation can result in misleading recommendations, unless thoroughly checked during preference elicitation and modeling. We suggest GSA to focus elicitation on most sensitive preference parameters. Our GSA results indicate that output uncertainty can be considerably reduced by additional elicitation of few parameters, e.g. the overall risk attitude and aggregation functions at higher-level nodes. Here, rough value function elicitation was sufficient, thereby substantially reducing elicitation time.

Environmental policy and decision-making are characterized by complex interactions between different actors and sectors. As a rule, a stakeholder analysis is performed to understand those involved, but it has been criticized for lacking quality and consistency. This lack is remedied here by a formal social network analysis that investigates collaborative and multi-level governance settings in a rigorous way. We examine the added value of combining both elements. Our case study examines infrastructure planning in the Swiss water sector. Water supply and wastewater infrastructures are planned far into the future, usually on the basis of projections of past boundary conditions. They affect many actors, including the population, and are expensive. In view of increasing future dynamics and climate change, a more participatory and long-term planning approach is required. Our specific aims are to investigate fragmentation in water infrastructure planning, to understand how actors from different decision levels and sectors are represented, and which interests they follow. We conducted 27 semi-structured interviews with local stakeholders, but also cantonal and national actors. The network analysis confirmed our hypothesis of strong fragmentation: we found little collaboration between the water supply and wastewater sector (confirming horizontal fragmentation), and few ties between local, cantonal, and national actors (confirming vertical fragmentation). Infrastructure planning is clearly dominated by engineers and local authorities. Little importance is placed on longer-term strategic objectives and integrated catchment planning, but this was perceived as more important in a second analysis going beyond typical questions of stakeholder analysis. We conclude that linking a stakeholder analysis, comprising rarely asked questions, with a rigorous social network analysis is very fruitful and generates complementary results. This combination gave us deeper insight into the socio-political-engineering world of water infrastructure planning that is of vital importance to our well-being.

Point-source measures have been suggested to decrease pharmaceuticals in water bodies. We analyzed 68 and 50 alternatives, respectively, for a typical Swiss general and psychiatric hospital to decrease pharmaceutical discharge. Technical alternatives included reverse osmosis, ozonation, and activated carbon; organizational alternatives included urine separation. To handle this complex decision, we used Multiple-Criteria Decision Analysis (MCDA) and combined expert predictions (e.g., costs, pharmaceutical mass flows, ecotoxicological risk, pathogen removal) with subjective preference-valuations from 26 stakeholders (authorities, hospital-internal actors, experts). The general hospital contributed ca. 38% to the total pharmaceutical load at the wastewater treatment plant, the psychiatry contributed 5%. For the general hospital, alternatives removing all pharmaceuticals (especially reverse osmosis, or vacuum-toilets and incineration), performed systematically better than the status quo or urine separation, despite higher costs. They now require closer scrutiny. To remove X-ray contrast agents, introducing roadbags is promising. For the psychiatry with a lower pharmaceutical load, costs were more critical. Stakeholder feedback concerning MCDA was very positive, especially because the results were robust across different stakeholder-types. Our MCDA results provide insight into an important water protection issue: implementing measures to decrease pharmaceuticals will likely meet acceptance. Hospital point-sources merit consideration if the trade-off between costs and pharmaceutical removal is reasonable.

Urine source separation (NoMix-technology) is a promising innovation aiming at a resource-oriented, decentralized approach in urban water management. However, NoMix-technology has a sensitive end-point: people’s bathrooms. NoMix-technology is increasingly applied in European pilot projects, but the success from a user point-of-view has rarely been systematically monitored. We aim at closing this gap. We review surveys on acceptance, including reuse of human urine as fertilizer, from 38 NoMix-projects in 7 Northern and Central European countries with 2700 respondents. Additionally, we identify explanatory variables with logistic regression of a representative Swiss library survey. NoMix-technology is well accepted; around 80% of users liked the idea, 75−85% were satisfied with design, hygiene, smell, and seating comfort of NoMix-toilets, 85% regarded urine-fertilizers as good idea (50% of farmers), and 70% would purchase such food. However, 60% of users encountered problems; NoMix-toilets need further development. We found few differences among countries, but systematic differences between public and private settings, where people seem more critical. Information was positively correlated with acceptance, and, e.g., a good mood or environmentally friendly behavior. For future success of NoMix-projects, we recommend authorities follow an integral strategy. Lay people will then find the NoMix-concept appealing and support this promising bathroom innovation.

We assessed the ecotoxicological hazard potential of 42 pharmaceuticals from 22 therapeutic groups, including metabolites formed in humans. We treated each parent drug and its metabolites as a mixture of similarly acting compounds. If physicochemical or effect literature data were missing, we estimated these with quantitative structure-activity relationships (QSAR). Additionally, we estimated micropollutant removal efficiency of urine source separation using pharmaceutical information. On average, 50% of a parent drug was metabolized, and 70% was excreted with urine, albeit with large variations among drugs. Metabolism reduced the toxic potential of all but eight drugs. The subsequently modeled risk quotient was mostly below the threshold of one. However, ibuprofen and its metabolites in a mixture could pose an ecotoxicologal risk; and possibly also acetylsalicylic acid, bezafibrate, carbamazepine, diclofenac, fenofibrate, and paracetamol. Lipophilicity and sale quantities of parent drugs alone were insufficient to estimate their ecotoxicological risk. Urine separation could decrease the ecotoxicological risk of some, but not all drugs. The estimated risk quotients were equal in urine and feces, again with large variations among compounds. Because of scientific limitations of the model and inconsistent literature data the results are somewhat uncertain. However, this new approach allows first tier screening of single drugs, thus supporting decision-making.