701-1522-00L Multi-Criteria Decision Analysis
|Periodizität||jährlich wiederkehrende Veranstaltung|
|Kommentar||Number of participants limited to 25.|
|Kurzbeschreibung||This introduction to "Multi-Criteria Decision Analysis" (MCDA) combines prescriptive Decision Theory (MAVT, MAUT) with practical application and computer-based decision support systems. Participants apply the theory to an environmental decision problem (group work). Methods from philosophical analysis (argumentation analysis) are introduced to help systematize decisions under great uncertainty.|
|Lernziel||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 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. They should also understand the limitations of conventional decision analysis, and how philosophical approaches help to deal with policy decisions under great uncertainty.|
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. A lecture (by Gertrude Hirsch Hadorn) focusses on philosophical approaches to systematize decisions. Hereby, framing the decision, timing strategies, and setting goals are discussed, and reasoning about uncertainty is introduced.
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 to three written reports (50 %).
|Skript||No script (see below)|
|Literatur||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.
|Voraussetzungen / Besonderes||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 number of participants is limited to 25. Registration is based on a first come first serve basis; registration period ends by 23.02.2016.