Inhalt

This course on Agent-Based Modeling and Simulation (ABMS) provides an in-depth exploration of both theoretical and practical aspects of the field. Designed for students with a foundational understanding of programming, mathematics, and computational models, the course equips participants with the knowledge and skills to develop, simulate, and analyze agent-based models. Throughout the course, students will explore fundamental concepts, key theories, and the principles of ABMS. Practical sessions will focus on implementing models using Python and the Mesa library, covering essential topics such as agent behaviors, complex systems, emergent phenomena, and game theory. The course also emphasizes model validation, verification, and calibration, as well as simulation optimization techniques. Advanced topics include multi-agent systems, performance scalability, and the integration of data. We will explore example models from relevant application areas, including smart manufacturing, supply chain digitalization, and other fields where ABMS can provide significant insights. The curriculum will feature practice-oriented student projects, allowing participants to apply the course’s learning to real-world problems and present their findings. Ethical considerations and future directions in ABMS are also discussed, ensuring a well-rounded educational experience. 

Competence Certificate

Depending on the number of course participants, the exam will be offered as an oral exam (20 min) or as a written exam (60 min). The exam takes place every semester and can be repeated at every regular examination date.

Learning Objectives

• Knowledge:
  By the end of the course, students will be able to:
  • Explain fundamental concepts of Agent-Based Modeling & Simulation (ABMS) and its applications in various fields.
  • Describe components and structures of Agent-Based Models (ABMs), including agents, environments, and interactions.
  • Differentiate between distinct types of simulation models and understand when to use ABMs.
  • Discuss principles of model validation, verification, and calibration in the context of ABMS.
  • Understand basics of complex systems and emergent behavior in the context of ABMs.
  • Understand basics of Game Theory, Simulation Optimization, and Machine Learning within the domain of ABMS.
  • Analyze theoretical frameworks and methodologies for developing ABMs. o    Evaluate ethical implications of ABM in research and application.
• Competences:
  By the end of the course, students will be able to:
  • Design and implement ABMs using Python with the Mesa library.
  • Develop, debug, and test ABMs to ensure they accurately represent systems being modeled. 
  • Use Python and relevant libraries to simulate and visualize agent behaviors and system dynamics.
  • Analyze and interpret results of simulations to draw meaningful conclusions.
  • Communicate modeling results effectively through written reports and oral presentations.
  • Apply ethical considerations in the development and use of AB simulation models.

Prerequisites

• Basic Programming Knowledge
  • Understanding of fundamental programming concepts such as variables, loops, conditionals, and functions (preferably in Python).
• Basic Mathematics and Statistics
  • Familiarity with basic mathematical concepts (algebra, functions) and statistical concepts (mean, median, standard deviation).
• Basic Understanding of Computational Models
  • General knowledge of what models are and their purposes in different scientific fields.

Form of Instruction

Lectures and exercises. A detailed course plan will be published before the start of semester.

• Wilensky, U., & Rand, W. (2015). An introduction to agent-based modeling: Modeling natural, social, and engineered complex systems with NetLogo. Massachusetts: MIT Press.
• Grimm, V., & Railsback, S. F. (2012). Agent-based and individual based modeling: a practical introduction. New Jersey: Princeton University Press.
• North, Michael J., Macal, Charles M, and Oxford University Press. Managing Business Complexity Discovering Strategic Solutions with Agent-Based Modeling and Simulation. New York: Oxford UP, 2007. Oxford Scholarship Online.