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390020 DK PhD-M: Management Decision Making (2012W)

Continuous assessment of course work


Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).


max. 15 participants
Language: English


Classes (iCal) - next class is marked with N

Friday 05.10. 10:00 - 12:00 Hörsaal 9
Friday 12.10. 10:00 - 12:00 Hörsaal 9
Friday 19.10. 10:00 - 12:00 Hörsaal 9
Friday 09.11. 10:00 - 12:00 Hörsaal 9
Friday 16.11. 10:00 - 12:00 Hörsaal 9
Friday 23.11. 10:00 - 12:00 Hörsaal 9
Friday 30.11. 10:00 - 12:00 Hörsaal 9
Friday 07.12. 10:00 - 12:00 Hörsaal 9
Friday 14.12. 10:00 - 12:00 Hörsaal 9
Friday 11.01. 10:00 - 12:00 Hörsaal 9
Friday 18.01. 10:00 - 12:00 Hörsaal 9
Friday 25.01. 10:00 - 12:00 Hörsaal 9


Aims, contents and method of the course

The course covers main areas of decision theory at an advanced level. It is structured into the following eight modules
1 Introduction to preference modeling: Relations and scales
2 Multidimensional evaluation Dominance and efficiency
3 Decisions under risk: Introduction to expected utility theory
4 Applications and extensions to expected utility theory
5 Dynamic decision problems and the value of information
6 Decisions under incomplete information and sensitivity analysis
7 Multicriteria decisions: Additive models
8 Multicriteria decisions: Non-compensatory models

Assessment and permitted materials

Classroom work and exercises (20%)
Final exam (40%)
Research project or survey paper (at the student's choice) (40%)

Minimum requirements and assessment criteria

As a PhD course, this course goes beyond a practical knowledge of methods of decision analysis. Students should be able to understand the inherent logic of models of decision analysis and their relation to fundamental assumptions about rationality as well as the inherent limitations implied by these assumptions. This should enable students to select and apply the appropriate methods for their own research work.

Examination topics

The course uses a blend of e-learning based self instruction and classroom teaching. Teaching notes and training material are provided in advance on the e-learning platform, students are expected to study this material before class. Classroom lectures and discussions will be used to strengthen the students' understanding of the material.

Reading list

Association in the course directory

Last modified: Mo 07.09.2020 15:46