701-1522-00L Multi-Criteria Decision Analysis
| Semester | Spring Semester 2022 |
| Lecturers | J. Lienert |
| Periodicity | yearly recurring course |
| Course | Does not take place this semester. |
| Language of instruction | English |
| Comment | The lecture will not take place in Spring Semester 2022. It will be offered next time in Spring Semester 2023. |
Courses
| Number | Title | Hours | Lecturers | |
|---|---|---|---|---|
| 701-1522-00 G | Multi-Criteria Decision Analysis Does not take place this semester. | 2 hrs | J. Lienert |
Catalogue data
| Abstract | This 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). |
| Learning objective | The 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 will learn to include uncertainty into decision models and test assumptions with sensitivity analyses. Participants 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. |
| Content | GENERAL DESCRIPTION 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. STRUCTURE The course consists of a combination of lectures, exercises in the class, exercises in small groups, and reading. 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. They will summarize this work in three graded reports. Additional reading from the textbook Eisenführ et al. (2010) is required. GRADING The group work consists of three written reports to be delivered at fixed dates during the semester with following grading: Report 1: 20%, Report 2: 40%, Report 3: 40%. |
| Lecture notes | No script (see below) |
| Literature | The 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 / Notice | The 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 credits | 3 credits |
| Examiners | J. Lienert |
| Type | graded semester performance |
| Language of examination | English |
| Repetition | Repetition only possible after re-enrolling for the course unit. |
| Additional information on mode of examination | GRADING: The group work consists of three written reports to be delivered at fixed dates during the semester with following grading: Report 1: 20%, Report 2: 40%, Report 3: 40%. |
Learning materials
| Additional links | Cluster Decision Analysis in Dept. Environmental Social Sciences (ESS), Eawag |
| Only public learning materials are listed. | |
Groups
| No information on groups available. |
Restrictions
| Places | 25 at the most |
| Waiting list | until 24.02.2022 |
| End of registration period | Registration only possible until 25.02.2022 |
