About this course
This course presents the fundamental principles of agent-based modeling, empowering students to construct simulations that mirror the behaviors and interactions of individual agents within complex systems.
Through a blend of theoretical discussions and hands-on exercises, students gain background knowledge in designing models that adapt and evolve over time, mimicking the complexities inherent in biological, ecological, social, and technological systems.
The course emphasizes the integration of agent-based models as a powerful tool for understanding emergent phenomena, pattern formation, and system-level dynamics. Students engage with real-world case studies, sharpening their skills in model calibration, validation, and interpretation.
By the course's conclusion, students emerge with a profound understanding of how agent-based modeling serves as a versatile and insightful approach to unraveling the dynamics of complex systems in a wide range of scientific disciplines.
Learning outcomes
After successful completion of this course students are expected to be able to:
- Formulate research questions, i.e., a theoretical or empirical question to investigate
- Designing microscopic models to describe the behaviour and dynamics of complex systems (i.e., using agents, and their interactions)
- Create simulations through coding agent-based models that operationalize the research questions
- Validate the model using sensitivity analysis of the variable space and validation against theory or empirical data
- Analyze the performance of the developed agent-based models
Required prior knowledge
Assumed Knowledge:
Preferably: basic programming experience, for instance, INF22306 Programming in Python, INF20806 Applied Information Technology or BIF50806 Practical Computing for Biologists