European Master and Certification Program
in Risk Engineering and Management

Complex Systems Theory

Course code: 175575
Language of instruction: English
Lecturers: Dr. Peter Klimek (European Virtual Institute for Integrated Risk Management), Prof. Dr. Aleksandar S. Jovanovic (Steinbeis Advanced Risk Technologies GmbH), Ph.D. Reto Schneider (SWICA Gesundheitsorganisation)
Assessment: defined in the module

Short description

Facing ever-growing amounts of data brings about the challenge to transform this data into actionable knowledge. This course provides theoretical, computational, and algorithmic frameworks that are often summarized under the term “Complex System Theory”. The course will outline several different approaches to make complex and high-dimensional datasets accessible and amenable for visualization and further analysis, including network theory, statistics of strongly correlated systems, and the analysis of complex dynamical processes. With this equipped, we will understand why complex systems often introduce a new type of risk that is called “systemic risk”, namely the risk that an entire system will break down or cease functioning as a result of an initially relatively minor default or error.


At the end of the course the students are expected to have basic knowledge about:
• analytical and computational tools for dimensionality reduction of large and complex datasets
• how to use networks and other theoretical, computational, or algorithmic concepts (such as clustering of data) to represent, visualize, analyze, and understand big data
• To understand and quantify systemic risk in complex systems

Target Attendees / Participants

The course is dedicated to university students of Steinbeis European Master Program in Risk Engineering and Management, and similar programs.

Course Content by Units

1.Introduction to complex systems theory (1/6)

· What are complex systems and what is so “complex” about them?
· Famous examples: from the sandpile to epidemics
· The computer as game changer

2. Network Theory (1/3)

· How to represent large and unstructured data as networks
· Measures for networks: how to quantify critical elements and interconnections.
· Different classes of networks: small worlds and scale free networks
· Visualizations and clustering

3. Dynamical processes (1/3)

· How do cascades and ripple effects spread on networks?
· Diffusion and spreading of diseases, ideas, innovations, and other processes on networks.
· Statistics of complex dynamical processes: fat tails and burstiness.

4. Applications (1/6)

· Visiting hallmark examples of complex systems in economics and finance.

5. Review of main course issues and preparation for the final exam

Teaching Methods

The course
• is illustrated by number of examples,
• presents commonly used methods and tools, and
• provides exercises and preparation for the final exam.


1. M. E. J. Newman, Networks - An Introduction, Oxford University Press (2010)
2. M. O. Jackson, Social and Economic Networks, Princeton University Press (2008)
3. J.H. Holland, Hidden Order: How Adaptation Builds Complexity, Perseus Books (1995).
4. S.A. Kauffman: The Origins of Order. New York (1993).

For more information about the European Master and Certification Program in Risk Engineering and Management in general, go the Homepage.
For more information about the European Master Program in Risk Engineering and Management in general, go the Master Study page.
To see more courses in the curriculum, go to The curriculum page, or by date and topic go to the Calendar of Courses page.
Contact: via email or phone +49 711 1839 781 or +49 711 1839 647
(Course profile ID: XIII-E-R53:, generated on November 19, 2018)