Das Ziel des Projekts ist die Weiterentwicklung eines multikriteriellen Verfahrens zur Entscheidungsanalyse in der Siedlungswasserwirtschaft.
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Enhancing the elicitation of diverse decision objectives for public planning
Identifying objectives is essential for decision making, but individuals have difficulties stating their important objectives. In public and environmental decisions, the diverse views of stakeholders should be included, but eliciting a broad set of objectives is challenging. We (1) study the effectiveness of individual brainstorming for eliciting objectives in a real-world setting; (2) test three interventions to support individuals in generating objectives; (3) investigate which and how many stakeholders are necessary to generate a comprehensive set of objectives; and (4) develop a feasible elicitation procedure for practice. In an experimental test, 71 stakeholders participated in five decisions about regional wastewater infrastructure planning in Switzerland. Three interventions were tested with an online survey procedure: (i) providing category cues, (ii) a perspective-taking task, and (iii) providing a predefined master list of objectives. In simple brainstorming, participants stated few objectives (M = 3.3) associated with 2.9 different categories on average. Participants consistently missed objectives they later considered important. Providing a master list was the only intervention that substantially increased the number and breadth of objectives (M = 12 objectives in M = 5.3 categories). With the help of our survey, participants generated between 30 and 38 distinct objectives for each decision case. Between five and nine participants were sufficient to generate these; more participants did not contribute new objectives. Most decision makers need help generating their objectives; combining simple brainstorming with a master list is a straightforward improvement that does not require a facilitator. An online process is promising for involving a large group of stakeholders.
Identifying non-additive multi-attribute value functions based on uncertain indifference statements
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.
Haag, F.; Lienert, J.; Schuwirth, N.; Reichert, P. (2019) Identifying non-additive multi-attribute value functions based on uncertain indifference statements, Omega: the international journal of management science, 85, 49-67, doi:10.1016/j.omega.2018.05.011, Institutional Repository
Methods to inform the development of concise objectives hierarchies in multi-criteria decision analysis
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.
Marttunen, M.; Haag, F.; Belton, V.; Mustajoki, J.; Lienert, 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, doi:10.1016/j.ejor.2019.02.039, Institutional Repository
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