Universität Wien FIND

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390048 DK PhD-M: Management Decision Making (2011S)

Continuous assessment of course work

Will be provided via the e-learning platform

Registration/Deregistration

Details

max. 15 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

Friday 04.03. 10:00 - 12:00 Hörsaal 7
Friday 11.03. 10:00 - 12:00 Hörsaal 7
Friday 18.03. 10:00 - 12:00 Hörsaal 7
Friday 25.03. 10:00 - 12:00 Hörsaal 7
Friday 01.04. 10:00 - 12:00 Hörsaal 7
Friday 08.04. 10:00 - 12:00 Hörsaal 7
Friday 15.04. 10:00 - 12:00 Hörsaal 7
Friday 06.05. 10:00 - 12:00 Hörsaal 7
Friday 13.05. 10:00 - 12:00 Hörsaal 7
Friday 20.05. 10:00 - 12:00 Hörsaal 7
Friday 27.05. 10:00 - 12:00 Hörsaal 7
Friday 03.06. 10:00 - 12:00 Hörsaal 7
Friday 10.06. 10:00 - 12:00 Hörsaal 7
Friday 17.06. 10:00 - 12:00 Hörsaal 7
Friday 24.06. 10:00 - 12:00 Hörsaal 7

Information

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 with incomplete information
7 Multicriteria decisions: additive models
8 Multicriteria decisions: Non-compensatory models

Assessment and permitted materials

Classroom work and exercises (20%)
Final exam (40%)
Term paper (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

Material is provided via the e-learning platform

Association in the course directory

Last modified: Mo 07.09.2020 15:46