Universität Wien

040228 UE Experimental Methods I: Agent Based Modelling in Organisations (MA) (2020S)

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Prüfungsimmanente Lehrveranstaltung

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").

Details

max. 30 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

  • Freitag 20.03. 11:30 - 14:45 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Montag 23.03. 09:45 - 13:00 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Montag 30.03. 11:30 - 14:45 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Freitag 03.04. 08:00 - 11:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Montag 20.04. 15:00 - 18:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Mittwoch 29.04. 11:30 - 14:45 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Montag 04.05. 09:45 - 13:00 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Montag 11.05. 09:45 - 13:00 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Freitag 26.06. 09:45 - 13:00 Digital

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

Agent Based Modelling, and Computational Methods in general, can provide valuable insights into the functioning of organisations. In academic research, simulations have long been used as a complementary tool besides traditional empirical work and laboratory experiments to produce propositions for empirical validation, model real-world scenarios and generate artificial datasets.

This class is an introductory course to modelling techniques in the context of organizational studies and should serve as a basis for the corresponding follow-up course. Whereas this second class will be focused on actual implementation of simulations, the first course will lay out the foundations for this by focusing on understanding the main ingredients needed in computational modelling, developing ideas on how to get from a research question to an actual model (mainly on the basis of pseudocode) and providing an introduction to the programming language Python which will be used for implementation later on.

Students will learn when computational models can be used, how they can be usefully employed and what the main advantages and limitations of this method are. Furthermore, participants will be familiarized with existing work in this domain and will have to prepare presentations based on peer-reviewed publications in some of the highest ranked journals in the field.

Art der Leistungskontrolle und erlaubte Hilfsmittel

10% - Homework Assignments
25% - In-class Participation
25% - Paper Presentation
40% - Final Exam

Mindestanforderungen und Beurteilungsmaßstab

Please note that attendance during the first session is absolutely mandatory.
Missing the first session without prior written notice to the lecturer (at least 24 hours before the start of the session) providing a relevant reason/proof (e.g. doctor’s notice in case of illness) will result in deregistration from the course. In such cases the missing student’s place will be given to the person next in line on the waiting list (if this person is present in the first session).

In general, students are allowed to miss up to 10% (2.5 hours) of scheduled classes without any consequences. Exceeding this limit, however, will result in failing the class.

The grading scheme will look as follows:
5 – [0%;50%)
4 – [50%;62.5%)
3 – [62.5%;75%)
2 – [75%;87.5%)
1 – [87.5%;100%]

Prüfungsstoff

Students are expected to have understood all topics discussed and presented in class.

Literatur

Relevant literature will be discussed in class.

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Fr 12.05.2023 00:12