Universität Wien
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040510 DK PhD-M: Management Decision Making (2008W)

10.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Continuous assessment of course work

Summary

1 Vetschera
2 Vetschera

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
Registration information is available for each group.

Groups

Group 1

Will be provided via the e-learning platform

max. 15 participants
Language: English

Lecturers

Classes

Currently no class schedule is known.

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 seven 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 Multicriteria decisions: additive models
7 Multicriteria decisions: Non-compensatory models

Assessment and permitted materials

Classroom work and exercises (20%)
Final exam (40%)
Research project or teaching assignment (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.

Group 2

Will be provided via the e-learning platform

max. 15 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Wednesday 21.01. 14:00 - 17:00 Seminarraum 2
  • Thursday 22.01. 14:00 - 17:00 Seminarraum 1
  • Friday 23.01. 09:00 - 12:00 Hörsaal 11
  • Friday 23.01. 14:00 - 16:00 Seminarraum 1
  • Wednesday 28.01. 14:00 - 17:00 Seminarraum 1
  • Thursday 29.01. 14:00 - 17:00 Seminarraum 1
  • Friday 30.01. 09:00 - 12:00 Hörsaal 11
  • Friday 30.01. 14:00 - 16:00 Seminarraum 1

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 seven 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 Multicriteria decisions: additive models

7 Multicriteria decisions: Non-compensatory models

Assessment and permitted materials

Classroom work and exercises (20%)

Final exam (40%)

Research project or teaching assignment (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.

Information

Reading list

Association in the course directory

Last modified: Mo 07.09.2020 15:29