Universität Wien
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390028 DK PhD-M: Management Decision Making (2021S)

5.00 ECTS (2.00 SWS), SPL 39 - Doktoratsstudium Wirtschaftswissenschaften
Continuous assessment of course work
MIXED

service email address: opim.bda@univie.ac.at
Moodle

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).

Details

max. 15 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

NOTE (Update 11.5.21): The FINAL EXAM takes place on-site.
To take part you will need to present a negative COVID-19 test result (or equivalency; more information about accepted tests to be found here: https://studieren.univie.ac.at/en/studying-exams/studying-on-site/ )

Students can apply for a deviating digital examination mode in the following cases (§ 13c of the University of Vienna study law; https://satzung.univie.ac.at/en/study-law/ ):
1. members of groups at increased risk in accordance with the COVID-19 groups at increased risk regulation of the Minister of Health;
2. students who work in healthcare and nursing;
3. students who live together with persons according to sub-paras. 1 and 2 or who care for persons according to sub-para. 1;
4. persons who face restrictions on freedom of travel (regionally/nationally) or are self-isolating (quarantine) on the date of the exam/completion of the partial achievement;
5. students with care obligations who cannot participate on site due to schools or kindergartens being closed, etc.

These students immediately notify the examiners/lecturers of the impossibility of taking a certain exam or completing a partial achievement on site, but no later than seven days before the exam date/date of the completion of the partial achievement.

  • Monday 08.03. 15:00 - 16:30 Digital (Kickoff Class)
  • Monday 15.03. 15:00 - 16:30 Digital
  • Monday 22.03. 15:00 - 16:30 Digital
  • Monday 12.04. 15:00 - 16:30 Digital
  • Monday 19.04. 15:00 - 16:30 Digital
  • Monday 26.04. 15:00 - 16:30 Digital
  • Monday 03.05. 15:00 - 16:30 Digital
  • Monday 10.05. 15:00 - 16:30 Digital
  • Monday 17.05. 15:00 - 16:30 Digital
  • Monday 31.05. 15:00 - 16:30 Digital
  • Monday 07.06. 15:00 - 16:30 Digital
  • Monday 14.06. 15:00 - 16:30 Digital
  • Monday 21.06. 15:00 - 16:30 Digital
  • Monday 28.06. 15:00 - 16:30 Hörsaal 3 Oskar-Morgenstern-Platz 1 Erdgeschoß

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 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 Decisions under incomplete information and sensitivity analysis

Assessment and permitted materials

Classroom work and exercises (20%)
Final exam (40%)
Note: The mode of the final exam (presence or online) will be decided on short notice, depending on the COVID situation
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. Lectures and discussions will be used to strengthen the students' understanding of the material.

Reading list

Lecture notes containing references will be available on Moodle

Association in the course directory

Last modified: Fr 12.05.2023 00:26