701-1522-00L  Multi-Criteria Decision Analysis

SemesterSpring Semester 2020
LecturersJ. Lienert
Periodicityyearly recurring course
Language of instructionEnglish
CommentNumber of participants limited to 25.


701-1522-00 GMulti-Criteria Decision Analysis
An den folgenden Daten: 17.03., 07.04., 21.04., 05.05., 12.05., findet die LV im Computerraum NO D 39 statt.
Am 21.04. und 05.05.2020 findet die LV sowohl im ML H 43 als auch im NO D 39 statt.
2 hrs
Tue08:15-10:00ML H 43 »
08:15-10:00NO D 39 »
J. Lienert

Catalogue data

AbstractThis introduction to "Multi-Criteria Decision Analysis" (MCDA) combines prescriptive Decision Theory (MAVT, MAUT) with practical application and computer-based decision support systems. Aspects of descriptive Decision Theory (psychology) are introduced. Participants apply the theory to an environmental decision problem (group work).
ObjectiveThe main objective is to learn the theory of "Multi-Attribute Value Theory" (MAVT) and "Multi-Attribute Utility Theory" (MAUT) and apply it step-by-step using an environmental decision problem. The participants learn how to structure complex decision problems and break them down into manageable parts. An important aim is to integrate the goals and preferences of different decision makers. The participants will practice how to elicit subjective (personal) preferences from decision makers with structured interviews. They should have an understanding of people's limitations to decision-making, based on insights from descriptive Decision Theory. They will use formal computer-based tools to integrate "objective / scientific" data with "subjective / personal" preferences to find consensus solutions that are acceptable to different decision makers.
Multi-Criteria Decision Analysis is an umbrella term for a set of methods to structure, formalize, and analyze complex decision problems involving multiple objectives (aims, criteria), many different alternatives (options, choices), and different actors which may have conflicting preferences. Uncertainty (e.g., of the future or of environmental data) adds to the complexity of environmental decisions. MCDA helps to make decision problems more transparent and guides decision makers into making rational choices. Today, MCDA-methods are being applied in many complex decision situations. This class is designed for participants interested in transdisciplinary approaches that help to better understand real-world decision problems and that contribute to finding sustainable solutions. The course focuses on "Multi-Attribute Value Theory" (MAVT) and "Multi-Attribute Utility Theory" (MAUT). It also gives a short introduction to behavioral Decision Theory, the psychological field of decision-making.

The course consists of a combination of lectures, exercises in the class, exercises in small groups, reading, and one mandatory exam. Some exercises are computer assisted, applying MCDA software. The participants will choose an environmental case study to work on in small groups throughout the semester. Additional reading from the textbook Eisenführ et al. (2010) is required.

There will be one written examination at the end of the course that covers the important theory (50 % of final grade). The group work consists of two written reports (50 %).
Lecture notesNo script (see below)
LiteratureThe course is based on: Eisenführ, Franz; Weber, Martin; and Langer, Thomas (2010) Rational Decision Making. 1st edition, 447 p., Springer Verlag, ISBN 978-3-642-02850-2.

Additional reading material will be recommended during the course. Lecture slides will be made available for download.
Prerequisites / NoticeThe course requires some understanding of (basic) mathematics. The "formal" parts are not too complicated and we will guide students through the mathematical applications and use of software.

The course is limited to 25 participants (first come, first served).

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits3 credits
ExaminersJ. Lienert
Typegraded semester performance
Language of examinationEnglish
RepetitionRepetition only possible after re-enrolling for the course unit.
Additional information on mode of examinationGRADING:
There will be one written examination at the end of the course that covers the important theory (50 % of final grade). The group work consists of two written reports (50 %).

Learning materials

Additional linksCluster Decision Analysis in Dept. Environmental Social Sciences (ESS), Eawag
Only public learning materials are listed.


No information on groups available.


Places25 at the most
Waiting listuntil 20.02.2020
End of registration periodRegistration only possible until 21.02.2020

Offered in

Doctoral Department of Environmental SciencesHuman-Environment SystemsWInformation
Geomatic Engineering MasterMajor in PlanningWInformation
MAS in Sustainable Water ResourcesElective CoursesWInformation
Environmental Sciences MasterModeling and Statistical AnalysisWInformation