Department Environmental Social Sciences

Decision Analysis (DA)

A group of people discussing a decision


Dr. Judit LienertGroup Leader, Cluster: DA (Decision Analysis)Tel. +41 58 765 5574Send Mail

The cluster Decision Analysis aims at achieving a better understanding of difficult environmental decision problems and at contributing to open research questions in Multi-Criteria Decision Analysis (MCDA). The focus lies on problem structuring of complex decisions, integrating stakeholder preferences into the decision process (including behavioral aspects), and dealing with uncertainty. A main aim is to simplify preference elicitation and decision making processes to facilitate their application in real world decision making without compromising on theoretical soundness.

The Decision Analysis cluster combines social, engineering, and natural science knowledge to support complex real world decision processes in aquatic science and technology. There is a close interaction with scientists from other disciplines at Eawag.

The empirical focus lies on urban water management (e.g. sustainable water infrastructure planning, decentralized systems), ecosystem services, and the preservation of water resources (e.g. river management). In transdiscipinary projects, stakeholders are integrated into research in different steps of the decision making process.

What is Multi-Criteria Decision Analysis (MCDA)?

Difficult decisions

Decision problems can be difficult if the decision makers hope to achieve several conflicting objectives. For example, one objective could be ‘low costs’ while another objective is to achieve ‘good environmental performance’. However, the most environmentally-friendly technical option might also be the most expensive one. In environmental decisions, typically multiple stakeholders with differing interests and values are involved. Uncertainty, for instance about the longer-term consequences of decisions, further increases complexity.

Decision analysis supports systematic thinking to structure the decision problem and to better understand the preferences and values of those involved. It aims at integrating all available information: scientific data and ‘hard facts’ together with the subjective preferences of stakeholders. It improves the process of decision making by increasing its transparency. It aims at making an optimal decision that is well accepted by all parties.

Good decision processes based on Multi-Criteria Decision Analysis (MCDA)


Multi-Criteria Decision Analysis (MCDA) allows to analyze the ‘hard facts’, i.e. the predictions about the consequences of choosing a specific decision option. As an example, if engineers have to choose between different wastewater treatment technologies, it is important to know how well nutrients or micropollutants are removed, and how much each option costs. To make sound predictions about the outcome of decision options, modeling techniques or expert assessments can be used.


Predictions should include uncertainty, such as a lack of scientific knowledge. For instance, one might not exactly know how an ecosystem will react to a management intervention. Predictions are also uncertain due to simplifications in the used models, or because expert judgments are uncertain. Uncertainty increases over long time ranges because the future is not known. In such cases, it can be helpful to integrate scenario planning with MCDA, which allows to consider different possible futures.

Problem structuring

MCDA also analyzes ‘soft data’, namely the preferences of decision makers and stakeholders. A good decision process ensures that the important stakeholders and the people affected by the decision are involved. It can be useful to select participants with stakeholder analysis, which belongs to the large family of Problem Structuring Methods (PSM). Such methods can also help to ensure that there is a proper understanding of the decision situation before using MCDA and that the main objectives of all people involved are considered, possibly including presumed interests of future generations.


People have different value systems and therefore different preferences in a decision. This especially affects how people perceive the importance of objectives and how they decide under uncertainty. If not all objectives can be achieved, trade-offs between these conflicting goals have to be made. Preference elicitation techniques help to determine, for instance, how important the achievement of an objective is, and how strongly it can be traded-off with another objective. For example, improving the ecological value of a river to foster an endangered bird species may mean that people can no longer use the river bank for picknicks. Such trade-offs are based on the best and worst possible outcomes of each objective for the specific decision problem (e.g. the costs of the cheapest and most expensive option, or the most user-unfriendly/ -friendly technology). Preference elicition processes (interviews or group workshops) are demanding, because people tend to run into systematic biases when answering our questions. People may also be uncertain about their answers, and they may be risk-averse if the outcomes of the decision (i.e. the predictions) are uncertain.

MCDA modeling and results

Preference elicitation techniques capture the decision maker’s preferences as numbers. These preference parameters enter the decision analysis model together with the ‘hard facts’, the predictions. The result of the MCDA model is a ranking of the decision options from best to worst. If several stakeholders were interviewed, a different ranking may result for each stakeholder. Also, some decision options may be more uncertain than others. The MCDA process helps to select good compromise options that perform reasonably well for all stakeholders despite uncertainty. MCDA should be seen as an iterative process, and it is often useful to refine or construct new decision options based on the insights of the process. It is also very beneficial to discuss the results with stakeholders.

Research topics and methods

A strong research focus of the cluster Decision Analysis is how to better deal with the high complexity in real environmental decisions without making unjustified compromises regarding scientific rigor. Much research in Multi-Criteria Decision Analysis (MCDA) has focused on well defined decision problems. In the real world, decisions tend to be complex and ill defined, with many stakeholders involved and a range of uncertainties to consider.

Our research aims at improving the practicability and reliability of the entire decision making process. This includes all steps of MCDA (see: ‘What is MCDA?’). MCDA is an umbrella term for a number of methods; we focus on Multi-Attribute Value Theory (MAVT) and Multi-Attribute Utility Theory (MAUT).

We develop our research in applied real world projects. Decision makers and stakeholders are strongly involved in the different steps of the decision process. Our transdisciplinary approach is also useful for our case study partners: they gain more insight into their difficult decision problem thanks to our research.

Research topics and some research questions

Problem structuring (framing)

Structuring of the decision problem will strongly affect the outcome of the MCDA. Aim is to include all relevant aspects and important stakeholders and to set up the decision process in such a way that it is best suited to tackle the respective environmental problem. Some research questions are:

  • How to best combine MCDA with Problem Structuring Methods (PSM; e.g. stakeholder analysis, scenario planning, cognitive mapping, strategy generation table, SWOT analysis, Soft Systems Methodology)?
  • Systematization and guidance for best practices in setting up the decision problem.
  • What are good ways to structure objectives hierarchies; what are the pro’s and con’s of reducing complexity?
  • What are good attributes (‘indicators’) that measure the consequences of options in a scientifically precise way, but are at the same time understandable for stakeholders?

There are various sources of uncertainty. Uncertainty of (i) boundary conditions, e.g. future socio-economic development, (ii) correct framing of decision problems (see above), (iii) the (scientific/ expert) predictions, (iv) decision maker’s preferences, and (v) preferences for decisions under risk.

  • How to best include future scenarios in MCDA-preference elicitation and modeling? How to elicit the decision maker’s preferences if there are different possible futures?
  • How can we make decisions over time if future developments are highly uncertain?
  • When should we focus research efforts on the uncertainty of predictions in a specific decision? How strongly is the decision outcome affected by changed assumptions (sensitivity analyses)?
  • How can we use expert assessments to make predictions if outcomes of an objective are difficult to model and/ or highly uncertain and/ or difficult to understand?
  • How can we elicit utility functions in a practicable and understable way from stakeholders for decisions under risk (MAUT)?
Preference elicitation

A main aim of the cluster Decision Analysis is to find better applicable, simplified elicitation procedures that can readily be applied in complex environmental decision problems. Simplified elicitation will help to transfer research insights into practice and increase real world application of MCDA. However, it is well known from psychological research that humans readily run into biases, violating the axiomatic foundations of MAVT/ MAUT. Preferences can also change for various reasons, and over time.

Internationally, Behavioral Operations Research (BOR) has emerged as an important topic. With our research we wish to contribute to this field. We thus aim at developing elicitation procedures that avoid biases or support de-biasing, that are easily applicable and understandable, and that are reliable and trustworthy. Typical research questions are:

  • What are best methods to elicit marginal value functions and weights (or scaling constants)? How can we aid decision makers during elicitation (e.g. visual and verbal cues, indirect or direct elicitation)?
  • Which aggregation schemes better represent peoples’ preferences if the additive model is inappropriate? How can we elicit the model parameters?
  • Can we effectively reduce interaction with stakeholders by increasing modeling efforts at the beginning of the MCDA?
  • How can we deal with uncertainty (see above): elicit risk attitudes, elicit preferences given different future scenarios, and deal with the decision makers’ uncertainty about their own preferences?
  • How stable are preferences over time? What does this imply for real decision making?
  • How does face-to-face elictation compare to faster elicitation processes (e.g. population surveys, group decision making)?


Data collection
  • Literature surveys
  • Expert assessment
  • Application and development of models for predictions
  • Problem structuring: workshops
  • Preference elicitation: face-to-face interviews, group workshops, (online) surveys, experiments
  • Evaluation of the process, e.g. questionnaires
Data analysis
  • Multi-criteria decision analysis (MCDA), specifically Multi-Attribute Value Theory (MAVT) and Multi-Attribute Utility Theory (MAUT); focus on flexibility (e.g. different aggregation models) and uncertainty (e.g. global sensitivity analyses)
  • Problem Structuring Methods (PSM), e.g. stakeholder analysis, scenario planning, cognitive mapping, strategy generation table, SWOT analysis, Soft Systems Methodology
  • Predictions: expert knowledge, modeling, and combinations (Bayesian networks)
  • Literature reviews
  • Statistical analyses (e.g. regression models)
Inter- and transdisciplinary research

The cluster Decision Analysis interacts closely with scientists from other disciplines at Eawag (engineers, chemists, ecotoxicologists, ecologists, and other social scientists from ESS), and with stakeholders in the applied projects.

Current Projects

We study how game implementation can support stakeholder involvement and preference modelling in MCDA.
We aim at facilitating the use of Multi-Criteria Decision Analysis in urban water management practice.
Project aim is participative decision support for the long-term transition to innovative wastewater infrastructures.
We aim at improving the use of MCDA in complex environmental problems.

Completed projects

Goal is an improved planning procedure for sustainable water supply and wastewater infrastructure management that links into the existing Swiss governance structures.
Removal of pharmaceuticals from hospital wastewater is well accepted by stakeholders if trade-off between good performance of an option and its costs is reasonable.
MCDA allows integrating ecological assessments with the prediction of consequences of river rehabilitation, and the preferences of experts about trade-offs
The population has a high willingness to pay for reducing risks of wastewater flooding or combined sewer overflows


Dr. Judit LienertGroup Leader, Cluster: DA (Decision Analysis)Tel. +41 58 765 5574Send Mail
Fridolin HaagPhD Student, Cluster: DATel. +41 58 765 5610Send Mail
Dr. Alice AubertPostdoc, Cluster: Decision AnalysisTel. +41 58 765 5688Send Mail
Philipp BeutlerProject collaborator, Cluster: DATel. +41 58 765 5285Send Mail
Philipp BeutlerProject collaborator, Cluster: DATel. +41 58 765 5285Send Mail


Sustainable Water Infrastructure Planning (SWIP)

Zheng, J.; Egger, C.; Lienert, J. (2016) A scenario-based MCDA framework for wastewater infrastructure planning under uncertainty, Journal of Environmental Management, 183(3), 895-908, doi:10.1016/j.jenvman.2016.09.027, Institutional Repository
Lienert, J.; Duygan, M.; Zheng, J. (2016) Preference stability over time with multiple elicitation methods to support wastewater infrastructure decision-making, European Journal of Operational Research, 253(3), 746-760, doi:10.1016/j.ejor.2016.03.010, Institutional Repository
Scholten, L.; Schuwirth, N.; Reichert, P.; Lienert, J. (2015) Tackling uncertainty in multi-criteria decision analysis – an application to water supply infrastructure planning, European Journal of Operational Research, 242(1), 243-260, doi:10.1016/j.ejor.2014.09.044, Institutional Repository
Lienert, J.; Scholten, L.; Egger, C.; Maurer, M. (2015) Structured decision-making for sustainable water infrastructure planning and four future scenarios, EURO Journal on Decision Processes, 3, 107-140, doi:10.1007/s40070-014-0030-0, Institutional Repository
Lienert, J.; Scholten, L.; Egger, C.; Maurer, M. (2015) Additional information for "Structured decision-making for sustainable water infrastructure planning and four future scenarios", 48 p, Institutional Repository
Maurer, M.; Lienert, J. (2014) Wasserinfrastrukturen nachhaltig in eine unsichere Zukunft führen, Eawag Newsletter [dtsch. Ausg.], 1-8, Institutional Repository
Scholten, L.; Scheidegger, A.; Reichert, P.; Mauer, M.; Lienert, J. (2014) Strategic rehabilitation planning of piped water networks using multi-criteria decision analysis, Water Research, 49(1), 124-143, doi:10.1016/j.watres.2013.11.017, Institutional Repository
Scholten, L.; Egger, C.; Zheng, J.; Lienert, J. (2014) Multikriterielle Entscheidungsanalyse. Neue Ansätze für langfristige Infrastrukturplanung in der Wasserver- und -entsorgung, Aqua & Gas, 94(5), 62-69, Institutional Repository
Scholten, L.; Scheidegger, A.; Reichert, P.; Maurer, M. (2013) Combining expert knowledge and local data for improved service life modeling of water supply networks, Environmental Modelling and Software, 42, 1-16, doi:10.1016/j.envsoft.2012.11.013, Institutional Repository
Lienert, J.; Schnetzer, F.; Ingold, K. (2013) Stakeholder analysis combined with social network analysis provides fine-grained insights into water infrastructure planning processes, Journal of Environmental Management, 125, 134-148, doi:10.1016/j.jenvman.2013.03.052, Institutional Repository

River Management

Langhans, S. D.; Lienert, J. (2016) Four common simplifications of multi-criteria decision analysis do not hold for river rehabilitation, PLoS One, 11(3), e0150695 (27 pp.), doi:10.1371/journal.pone.0150695, Institutional Repository
Reichert, P.; Langhans, S. D.; Lienert, J.; Schuwirth, N. (2015) The conceptual foundation of environmental decision support, Journal of Environmental Management, 154, 316-332, doi:10.1016/j.jenvman.2015.01.053, Institutional Repository
Langhans, S. D.; Lienert, J.; Schuwirth, N.; Reichert, P. (2013) How to make river assessments comparable: a demonstration for hydromorphology, Ecological Indicators, 32, 264-275, doi:10.1016/j.ecolind.2013.03.027, Institutional Repository

Urban water management: How to handle hospital wastewater

Schuwirth, N.; Reichert, P.; Lienert, J. (2012) Methodological aspects of multi-criteria decision analysis for policy support: a case study on pharmaceutical removal from hospital wastewater, European Journal of Operational Research, 220(2), 472-483, doi:10.1016/j.ejor.2012.01.055, Institutional Repository
Escher, B. I.; Baumgartner, R.; Koller, M.; Treyer, K.; Lienert, J.; McArdell, C. S. (2011) Environmental toxicology and risk assessment of pharmaceuticals from hospital wastewater, Water Research, 45(1), 75-92, doi:10.1016/j.watres.2010.08.019, Institutional Repository
Lienert, J.; Koller, M.; Konrad, J.; McArdell, C. S.; Schuwirth, N. (2011) Multiple-criteria decision analysis reveals high stakeholder preference to remove pharmaceuticals from hospital wastewater, Environmental Science and Technology, 45(9), 3848-3857, doi:10.1021/es1031294, Institutional Repository

Urban water management: Urine source separation

Larsen, T. A.; Maurer, M.; Eggen, R. I. L.; Pronk, W.; Lienert, J. (2010) Decision support in urban water management based on generic scenarios: the example of NoMix technology, Journal of Environmental Management, 91(12), 2676-2687, doi:10.1016/j.jenvman.2010.07.032, Institutional Repository
Borsuk, M. E.; Maurer, M.; Lienert, J.; Larsen, T. A. (2008) Charting a path for innovative toilet technology using multicriteria decision analysis, Environmental Science and Technology, 42(6), 1855-1862, doi:10.1021/es702184p, Institutional Repository

Transdisciplinary Research

Renner, R.; Schneider, F.; Hohenwallner, D.; Kopeinig, C.; Kruse, S.; Lienert, J.; Link, S.; Muhar, S. (2013) Meeting the challenges of transdisciplinary knowledge production for sustainable water governance, Mountain Research and Development, 33(3), 234-247, doi:10.1659/MRD-JOURNAL-D-13-00002.1, Institutional Repository

Economic valuation

Veronesi, M.; Chawla, F.; Maurer, M.; Lienert, J. (2014) Climate change and the willingness to pay to reduce ecological and health risks from wastewater flooding in urban centers and the environment, Ecological Economics, 98, 1-10, doi:10.1016/j.ecolecon.2013.12.005, Institutional Repository


ETH Zürich, Department of Environmental Sciences

ETH Zürich, Institute of Environmental Engineering, Chair of Ecological Systems Design