Experimental Methods I - Agent Based Modelling ("Experimental Methods 1.01 alt")

Winter and Summer Term: 040228 Master Level

This course is an alternative option for Experimental Methods I. 

COURSE DESCRIPTION:

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.

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.

Important information for students planning on completing the entire specialization Strategic Management, i.e. those who want to enroll for the Major as well:
This course is NOT compatible with “Experimental Methods – Laboratory Experiments”. That means that if you complete this course (“Experimental Methods I: Agent Based Modelling in Organisations”), you are expected to continue with “Experimental Methods II: Agent Based Modelling in Organisations”, which is the corresponding follow-up class in the Major. You should NOT switch between those two streams, as the topics discussed are entirely different and you will not be able to participate meaningfully in any follow-up course if you haven’t completed the corresponding Minor-class.

Pre-requisites:

Admission to the Master's program

2016 Curriculum: Successful completion of the Minor

Language: This course is held in English

Assignments and assessment:

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

Attendance:

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% (2.25 hours) of scheduled classes without any consequences. Exceeding this limit, however, will result in failing the class.