040163 UE Experimental Methods II: Agent Based Modelling in Organisations (MA) (2020W)
Prüfungsimmanente Lehrveranstaltung
Labels
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Mo 14.09.2020 09:00 bis Mi 23.09.2020 12:00
- Anmeldung von Mo 28.09.2020 09:00 bis Mi 30.09.2020 12:00
- Abmeldung bis Sa 31.10.2020 12:00
Details
max. 50 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
- Freitag 30.10. 09:45 - 13:00 Digital
- Donnerstag 05.11. 09:45 - 13:00 Digital
- Donnerstag 12.11. 09:45 - 13:00 Digital
- Donnerstag 19.11. 09:45 - 13:00 Digital
- Donnerstag 26.11. 09:45 - 13:00 Digital
- Donnerstag 03.12. 09:45 - 13:00 Digital
- Donnerstag 17.12. 09:45 - 13:00 Digital
- Freitag 22.01. 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 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.
Art der Leistungskontrolle und erlaubte Hilfsmittel
10% - In-class Participation
25% - Homework Assignments
25% - Final Exam
40% - Final ProjectNote that for the Final Project, students will work together in groups.
The Final Exam will be a 2-hour take-home exam.
More detailed information will be provided during the course.
25% - Homework Assignments
25% - Final Exam
40% - Final ProjectNote that for the Final Project, students will work together in groups.
The Final Exam will be a 2-hour take-home 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%]
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