8 July 2016
on course website
Introduction to Complex Systems
Our world has an abundance of so-called complex systems. They are typically very large collections of interacting individual elements. Examples range across all levels of organisation and across the biological, physical, social and "in-silico" world. We can think of interactions amongst atoms in a molecule, molecules in a cell, neurons in a brain or even all cells in the human body, but also interacting species in an ecosystem, ; humans transmitting an infectious disease within a group, or sharing rumours, ideas, opinions, innovations; interacting cars in traffic; networks of co-dependent financial institutions, companies, governmental organisations or governments; the climate; and the World Wide Web.
Diverse though these examples are, they have a surprisingly large number of features in common. As a rule, these systems have variousproperties that are more than the sum of their parts, i.e., emergent phenomena that cannot be explained by studying elements in isolation. For example, a traffic jam is an emerging property of interacting cars on a road that gets busier that cannot be explained even by the most detailed knowledge about how an individual car works or is operated. In addition, complex systems such as a network of interacting species in an ecosystem, or banks in a financial network, may be robust (stable) against disturbance, but in contrast may also be close to a tipping point (bifurcation) beyond which they fully collapse. Finally, the connectivity of components in complex systems, for example a network of interacting humans in social cyber-space may be such that an opinion, message or image goes "viral" in an apparently sudden, unpredictable and uncontrollable manner.
In recent decades, the science of studying complex systems has started to evolve and mature. It has become clear that a new, more integrated way of thinking is essential for understanding many of the complex challenges that humanity faces. The above mentioned
set of shared features allow complex systems to be studied with a uniform collection of tools from mathematics and theoretical physics. Such a shared toolset paves the way for deriving general principles on the properties of complex systems across disciplines. Ultimately, the aim is to derive rules on how the dynamical behaviour of a complex system depends on the combined properties of individual elements, the nature of the interactions between elements, as well as the topology of interactions between elements, in order to understand and predict these systems and control them to have desirable properties. As an example, insight into which features of complex systems generate resilience against perturbations versus which properties enhance the sensitivity of the system and allow it to transition to a different equilibrium state is important for a broad range of questions on, for example, climate change, social-political change, disruptive innovations, infectious disease emergence and ecosystem collapse.
We focus on the four key aspects of complex systems examplified above: emergence, resilience, transitions and predictability and control. We demonstrate how they are recurring concepts in a broad range of areas ranging from the life sciences, social sciences, economics, as well as humanities; and thereby unify them at a deep level. We will stimulate the students to think in terms of these abstract unifying concepts across the diverse disciplines; illustrate the need for mathematical models to study and quantify these properties; and teach the students basic mathematical tools to study the behaviour of the constructed models.
Dr. Kirsten ten Tusscher
Reference level starts at end of bachelor for academic maturity, but otherwise the course is open to students from all backgrounds and subjects. The audience is expected to have an interest in and the capacity for understanding abstract notions and reasoning. A mathematical background is not required, but the lectures and practical work do lean heavily on a working knowledge of mathematics at end of secondary school level (linear algebra, functions, differential equations).
The aim of the course is:
i) to recognise complex systems related to societal, environmental, engineering and scientific problems and to learn their basic features
ii) to introduce a complex systems way of thinking and analysis
iii) to learn basic mathematical concepts and methods needed for complex system analysis, for example from dynamical systems theory and the theory of networks
iv) to get hands-on experience in studying complex systems.
EUR 350: Course + course materials + housing
EUR 150: Course + course materials
on course website