WISO000868 Advanced topics in complex systems science I

Art
Seminar
Semesterstunden
2
Vortragende/r (Mitwirkende/r)
Haberl, Helmut , Gaube, Veronika , Pichler, Melanie
Organisation
Soziale Ökologie
Angeboten im Semester
Sommersemester 2026
Unterrichts-/ Lehrsprachen
Englisch
Lehrinhalt

This course invites students to deepen their knowledge of foundational approaches and applications of complexity science and to apply complexity science methods to a research question of interest. Complexity science itself emerged in the 1980s as a response to increasingly reductionist approaches and aims to reveal underlying generalizable patterns in a range of complex systems so as to understand fundamental principles governing biological, social, and economic systems. The core approaches highlighted in this course are modeling (statistical and mathematical models, inspired by physics, ecology, and evolution), simulation (agent-based models or “digital twins”), analysis (networks), and underlying methods (machine learning, statistical physics, nonlinear dynamics). Each semester focuses on a specific subset of complexity science methods and application areas.
Lectures introduce methods and application scenarios, developing an understanding of “how” and “why”. Students gain perspective through a review of relevant background and concepts.
Discussions engage students in a didactic exchange by which the concepts introduced in the lectures are further explored and refined.
Practical Exercises offer the chance to “do”, focusing on technical mastery using established examples.
Final Projects combine the knowledge and skills gained and encourage students to apply these in the context of a novel research problem or question related to their ongoing thesis research. Group work is encouraged. Students have three weeks to complete the final project.
Inhaltliche Voraussetzungen (erwartete Kenntnisse)

This is an intensive, fast-paced, advanced course. Students should have strong foundations in statistics; a foundational knowledge of physics (thermodynamics, classical mechanics) and graphs/networks; and comfort with Python as a coding language. Backgrounds in linear algebra and calculus are recommended, though the class will focus on discrete methods where possible.
Lehrziel

After completing this course, students will be able to: (1) Describe foundational concepts underlying key modeling, simulation, and analysis techniques in complexity science. (2) Define a concrete and tractable research question in complexity science. (3) Justify and debate appropriate modeling, simulation, or analysis methods to address a given research question. (4) Reproduce a complex systems model, simulation, and analysis from existing code and modify that code purposefully. (5) Implement a complex systems modeling, simulation, and analysis method de novo. (6) Interpret and validate the results, (7) Evaluate how complexity science can bridge diverse research domains.
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