This course focuses on integrating qualitative empirical methods with social simulation, specifically agent-based modelling. Students develop both quantitative and qualitative methodological skills to prepare for the collaborative design of a mixed-methods group project.

During the winter term portion of this mixed-methods track, the primary focus shifts to implementing the group projects designed in the preceding summer term.

In the quantitative component, students are guided through the agent-based development cycle: model design, coding, empirical validation, simulation experiments, scenario analysis, sensitivity analysis, documentation, and code dissemination. Students receive group-based coaching to refine their models and programming structures throughout the semester, iteratively aligning them with findings from the qualitative empirical component. Additionally, they learn to conduct simulation experiments and perform corresponding data analysis. Through homework assignments and individual exercises, students master NetLogo’s “Behavior Space” to run simulations with varying parameterizations and apply basic data analysis scripts in R to analyze and visualize data across multiple repeated simulation runs, thereby demonstrating the model’s robustness and stochastic nature.

In the qualitative component, students further develop their research projects by collecting and analysing additional empirical data. They refine their theoretical assumptions by grounding them in empirical evidence and systematically cross‑checking them against previous findings (also from the summer term). Regular feedback sessions between students and teachers support the discussion and refinement of theoretical assumptions and analytic techniques. Students learn how different qualitative approaches generate distinct insights that inform the modelling process. This component fosters a holistic understanding of the research topic and supports students in deciding which theoretical insights to implement in the agent‑based model, while critically reflecting on these decisions.

The course culminates in the production of a comprehensive mixed-methods research paper, which is written and presented by each project group.

After successful completion of this course, students will be able to:

• develop or deepen a research question (based on the state of the art and on theories)
• specify a mixed methods research strategy in more depth (empirical design)
• implement an agent-based model along the agent-based development cycle: model design, coding, empirical validation, simulation experiments, scenario analysis, sensitivity analysis, documentation, and code dissemination
• conduct simulation experiments using NetLogo's behavior space
• analyse and visiualize simulated data with basic R scripts
• to analyse, interpret and try to link the varying results from quantitative and qualitative methods
• to present research results (including a quality assessment)
• to write a research report

Students are expected to:

• prepare the literature for discussions in class
• actively participate in discussions and assignments in class
• submit a research design
• to focus on a research question with bottom-up modelling and simulation techniques
• to use Netlogo for modelling and simulation
• to use provided R scripts for data anlaysis
• to analyse and interpret computaionally generated data
• to collect empirical data
• to interpret qualitative data and refine the research questions and design
• to submit a research report

80% attendance of the class is required! If you miss a class, please inform us in advance!

Design of Teaching

• drafting and applying a research design
• group coachings for modelling, data analysis and mixing methods
• workshops to interpret qualitative data
• peer-feedback to other research designs
• presentations, discussions

Exigencies 

• Active participation in class, group assignments, presentation in class, final seminar paper on own project
• Practical exercises in the framework of a group work dealing with a special topic will enhance the students’ understanding and ability to critically assess empiricalstudies and to apply those methods which are adequate for answering specificquestions.
• The purpose of this course is to provide students with knowledge in specific techniques for agent-based modelling and simulation with different topologies.
• Eventually students should be familiar with the methodological foundations of mixing quantitative and qualitative methods. They learn how to collect and interpret qualitative data and how to use this data for empirical foundations of the model. 

• individual and group tasks (qualitative assignments) (20%)
• group tasks (quantitative assignments 20%)
• group Tasks (mixed methods)
  
  • final research paper (50%)
    
  • poster presentation (10%)

SEEP courses do not allow creation of assignments, exam answers or other assessed work using generative AI (e.g. ChatGPT).  All such work is expected to be the original work by the student concerned and is assessed as such.  Work copied from a generative AI source is equivalent to plagiarism and will be treated as such.

Please log in with your WU account to use all functionalities of read!t. For off-campus access to our licensed electronic resources, remember to activate your VPN connection connection. In case you encounter any technical problems or have questions regarding read!t, please feel free to contact the library at readinglists@wu.ac.at.