Universität Wien

390053 DK PhD-L: Advanced Stochastic Models (2021W)

4.00 ECTS (2.00 SWS), SPL 39 - Doktoratsstudium Wirtschaftswissenschaften
Prüfungsimmanente Lehrveranstaltung
GEMISCHT
Moodle

An/Abmeldung

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

Details

max. 15 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

  • Montag 25.10. 12:55 - 16:00 Digital
  • Montag 15.11. 13:00 - 16:00 Digital
  • Montag 06.12. 13:00 - 16:00 Digital
  • Montag 24.01. 13:00 - 16:00 Digital
  • Montag 21.02. 09:45 - 16:30 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock
  • Dienstag 22.02. 09:45 - 16:25 Hörsaal 17 Oskar-Morgenstern-Platz 1 2.Stock

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

Business research increasingly considers wicked problems and complex dynamic systems. Analytical models of such problems and systems quickly become untraceable and unsolvable. Given increasing computational power, simulation models provide an alternative tool. They can fuel studies tracing the long-term evolution of systems and comparing the outcomes of alternative scenarios. However, successfully applying simulation modeling for business research requires expertise on applicable simulation paradigms, approaches to model validation and the analysis of stochastic results.

Participants gain theoretical background knowledge in
- System dynamic, discrete event-based and agent-based simulation paradigms
- Analysis of stochastic simulation results
- The role of simulation validation and calibration
- Challenges of computational efficiency
They also gain hands-on experience in applying these concepts to case scenarios in implementing simulation models in Python SimPy and NetLogo.

Art der Leistungskontrolle und erlaubte Hilfsmittel

Presentation on own simulation project at the end of the course

Mindestanforderungen und Beurteilungsmaßstab

Prüfungsstoff

Participants need to be able to present their project in the context of models discussed, apply discussed modelling languages, propose relevant approaches to analysing stochastic input data and results, and present an implemented prototype.

Literatur

Recommended reading:

- Robinson, S. (2005). Discrete-event simulation: from the pioneers to the present, what next?. Journal of the Operational Research Society, 56(6), 619-629.
- Tako, A. A., & Robinson, S. (2012). The application of discrete event simulation and system dynamics in the logistics and supply chain context. Decision Support Systems, 52(4), 802-815.
- Chan, W. K. V., Son, Y. J., & Macal, C. M. (2010, December). Agent-based simulation tutorial-simulation of emergent behavior and differences between agent-based simulation and discrete-event simulation. Proceedings of the 2010 Winter Simulation Conference (WSC), (pp. 135-150). IEEE.
- Sargent, Robert G. "Verification and validation of simulation models." Journal of Simulation 7, no. 1 (2013): 12-24.

- Law: Simulation Modeling and Analysis (2014)

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Fr 12.05.2023 00:26