Universität Wien
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040047 SE Topics in Evidence-Based Decision Making (MA) (2018W)

8.00 ECTS (4.00 SWS), SPL 4 - Wirtschaftswissenschaften
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. 30 participants
Language: English


Classes (iCal) - next class is marked with N

Thursday 25.10. 15:00 - 18:15 Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Stock
Thursday 08.11. 15:00 - 18:15 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
Thursday 15.11. 15:00 - 18:15 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
Thursday 22.11. 15:00 - 18:15 Seminarraum 12 Oskar-Morgenstern-Platz 1 2.Stock
Thursday 29.11. 15:00 - 18:15 Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Stock
Thursday 06.12. 15:00 - 18:15 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
Tuesday 11.12. 15:00 - 16:30 Seminarraum 13 Oskar-Morgenstern-Platz 1 2.Stock
Thursday 13.12. 15:00 - 16:30 Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Stock
Tuesday 18.12. 13:15 - 14:45 Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Stock
Thursday 10.01. 15:00 - 18:15 Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Stock
Thursday 17.01. 15:00 - 18:15 Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Stock
Thursday 24.01. 15:00 - 18:15 Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Stock
Wednesday 30.01. 15:00 - 16:30 Seminarraum 1 Oskar-Morgenstern-Platz 1 Erdgeschoß


Aims, contents and method of the course

Content: Digital services permeate almost every aspect of life, reshaping business transactions and social interactions alike. This course investigates how data-driven approaches of decision making and decision analysis can shed light on old and new questions in economics and business. The purpose of this course is aimed to introduce students to recent research trends, both in terms of methods and content, and to discuss critically how they can be applied for decision making in firms and in organizations.
The course consists of two parts. In part 1, we review basic concepts of causal inference through randomized experiments, requirements for successful digital experimentation, and statistical concepts of prediction. In part 2, we investigate how these concepts can help generate insights regarding behavior for a number of applied economic and business questions. The fields of research from which applications are selected include behavioral economics, game theory, organizational behavior, strategic management, and others (see reading list).

Organization of the course: In part 1, each student presents the solution to one homework exercise in front of the class (about 20’) using a live demonstration in ‘R’. In part 2, we focus on applications. Each student selects one or two papers from the reading list on which s/he acts as the ‘expert’. The reading list is handed out in the first session. It is therefore imperative to participate in the first session. Students who cannot (for a good reason) participate in the first session should send me an e-mail one week before the first session.
Classroom discussion in part 2 of the course is organized as follows: The topic/paper is introduced in a very concise manner (extended abstract; max 5’) by one student. The student who acts as ‘expert’ then provides a concise presentation of the paper (presentation; about 20’) using slides. We then discuss questions of technical detail as well as questions on context and interpretation.
A successful ‘expert’ is able to summarize each section/paragraph of her paper in his or her own words at any time during the discussion, guides the discussion and is able to answer most questions from fellow students and the instructor. Participants are expected to read all papers, prepare questions, and to contribute their own thoughts and views on the paper. Participation is essential. Critical thought, controversy and debate is welcome (once we are clear about what the paper says).

Assessment and permitted materials

Minimum requirements and assessment criteria

Requirements: Participants should have taken an introductory course to the field of experimental economics, for example the MA course “Behavioral and Experimental Economics” (UK040832). Students with comparable backgrounds can also be admitted but need to provide evidence that their knowledge is comparable (bring table of contents and grade of classes taken elsewhere to the first session). In addition, a sound knowledge of microeconomics, game theory, and microeconometrics is required.

Examination topics

Grading: Successful completion of this course earns students 8 ECTS credits. Because your contribution to the class is essential, your attendance at every class is expected. Unexplained and unexcused absences will negatively affect your grade.
Grades are determined as follows:
1. (10%) Presentation of one exercise.
2. (10%) Presentation of one extended abstract.
3. (30%) Presentation of one paper.
4. (20%) Active participation in discussion.
5. (30%) Hand in two term papers each consisting of an extended abstract (about 1 page) summarizing the paper in your own words and your comments and own thoughts (max. 3 pages). Best shot counts.
Note: deadline for handing in written material (exercise, extended abstract, presentation, term papers) at latest 24 hours before the course by e-mail to me.

Reading list

Reading list / moodle

Association in the course directory

Last modified: Mo 07.09.2020 15:28