Universität Wien

040163 UE Experimental Methods II: Agent Based Modelling in Organisations (MA) (2022W)

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

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

  • Dienstag 04.10. 08:00 - 11:15 PC-Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 11.10. 08:00 - 11:15 PC-Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 18.10. 08:00 - 11:15 PC-Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Donnerstag 10.11. 13:15 - 16:30 PC-Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 15.11. 08:00 - 11:15 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 22.11. 08:00 - 11:15 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 29.11. 08:00 - 11:15 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 17.01. 08:00 - 11:15 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß

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 builds on and extends concepts and topics from Experimental Methods I: Agent Based Modelling in Organisations. Accordingly, the successful completion of Experimental Methods I is a prerequisite for attending this course. Students will draw on knowledge gained in the first course and are expected to be able to follow more advanced discussions building on these contents.

The classes will mainly focus on two aspects: a) understanding established types of computational models used in organizational studies in more detail and b) hands-on work with Python code. The idea is to get a solid overview of the most important model types, what their strengths and weaknesses are, and for which questions they can be usefully employed. Furthermore, students will continue to work on their proficiency with respect to Python, a widely-used programming language.

This course is meant to be interactive and is built around the idea of a laboratory setup as is typical for social sciences. The setup necessitates certain software and IT equipment and in order to provide every student with the same opportunity to successfully participate in the course, it is typically held in one of the PC-labs at the OMP 1.
Due to the current situation related to Covid-19, however, it may be necessary that some (or all) sessions are held in an online format. The decision to do so will depend on university guidelines and ongoing developments. If sessions will be conducted remotely, the same timeslots will be used as planned for on-site meetings. Given capacity restrictions of the PC-labs and also in order to maintain a high level of quality and fairness, the number of participants admitted to register for the class will be limited, regardless of the sessions being online or in presence.

Art der Leistungskontrolle und erlaubte Hilfsmittel

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

Note that for the Final Project, students will work together in groups.
The Final Exam will be a 2-hour open-book exam.
More detailed information will be provided during the course.

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: Mo 03.10.2022 10:29