Complexity: can it be simplified?

Complex systems


What do economic crises, traffic jams, consciousness, the climate and flocks of birds have in common? They can all be described as complex systems. These systems are characterized by a certain pattern, or regularity, at the collective level, which is driven by a multitude of interacting components that in their turn are affected by the collective dynamics. In other words, not only is the whole more than the sum of its parts, but changing system behavior also has feedback on the individual components. Complex systems are self-organizing, largely beyond central control, often adaptive but under certain conditions self-destructive.

Whereas classic science provided insight into isolated phenomena, for example individual cells of an organism, it could not grasp the simultaneous interplay of many different cells, or components in general. To overcome this limitation, complexity science uses novel mathematical and computational methods, which make it possible to better understand and predict complex processes. Examples are: ecosystems, groups of both competing and cooperating monkeys, embryo development, the global climate, stock markets, epidemics, criminal networks and neural networks.

This relatively new approach has become widely popular during the past decennia, and is currently used in natural-, life-, and social sciences. Its concepts and methods are applicable to many different complex systems, and even made it into the popular press. Cases in point are: emergence, tipping points, phase transitions, non-linear processes, resilience, bifurcation points, scale-free, chaos and entropy.  Complexity thinking thereby puts into question taken-for-granted assumptions about causality, linear dynamics, reductionism, objective knowledge and determinism, which can now be replaced by a more adequate understanding of our world.

This course provides a unique opportunity to acquire insights into complex systems from a range of different disciplines, and to learn about the models that are used to represent and examine these systems. It is intended for a broad audience, and shows that a general understanding of complexity is possible without understanding the technicalities of the models. There is also a sense of urgency: given the current problems of our world, it is crucially important that students learn about complexity, and acquire the skills to use its insights in their future professions, be it business, government, journalism or science.


Learning objectives

  • The student shows insight into commonalities of different processes studied by researches from natural, life-, and social sciences, and can think through complex phenomena in terms of multitudes of simultaneous interactions, in contrast to ‘either this cause or that cause’ thinking. 
  • The student can describe complex phenomena, at least from their own field of study, in terms of complex processes, thereby using key concepts from complexity science and showing basic insights in the pertaining methods.
  • The student can describe the meaning of, and relations between, these concepts, as well as the basic principles of the models and methods wherein they are used.
  • The student can provide a critical evaluation of the usefulness of complexity science, as well as its concepts and methods for their own field of study.

Lectures, workshops and practical sessions

The program consists of a series of lectures of 1,5 hours (including questions and discussion), given by researchers from natural sciences, life sciences and humanities. The series starts at September and continues until the beginning of December when a closing forum will take place with several researchers and the students. The lectures will be taught in English. At the start, a few students will briefly introduce the guest lecturer, and groups of students prepare a number of questions for the final part of the lecture.

During the first month of the course there will be three one-hour workshops before the lectures, with a focus on discussing key notions from complexity science and analyzing (relatively simple) examples of networks with Python software. This workshop is meant to provide basic skills and self-confidence to students lacking a technical background, and participation is on a voluntary basis. 


This course is a joint effort by the IIS and the Institute for Advanced Study, coordinated by Jeroen Bruggeman {} with assistance from Suzanna van Baardewijk {}.


The students will read some general articles on complexity as well as a specific article by each guest lecturer before the corresponding lecture. The literature will be available in a digital reader.


Lectures: Wednesday from 19.30 to 21.00

September 6th:          Peter Sloot (computational sciences)

September 13th:        Mike Lees (networks and modeling)

September 20th:        Charlotte Hemelrijk (biology)

September 27th:        Dick Swaab (neurobiology)

October 4th:               Peter Schall (physics)

October 11th:            No lecture because of interim exam

October 18th:             Henk Dijkstra (climatology)

October 25th:             No lecture because of exams for other courses

November 1st:           Cars Hommes (economics)

November 8th:           Henkjan Honing (musicology and computational science)

November 15th:        Han van der Maas (psychology)

November 22nd:        Jeroen Bruggeman (sociology)

December 6th:           Closing forum with Peter Sloot and several others

Workshops: Wednesday from 18.00 to 19.00

September 13th

September 20th

September 27th


Interim exam: 5-11 October

Final exam: 6-13 December[61877]


Halfway through the course there will be an interim exam. The course will be concluded with a take-home exam consisting of (half) open questions.


UvA-students can register themself from Thursday 15 June 2017 (look for code 5512COMP6Y in SIS) until a week before the start of the course. If you have any trouble while registering please contact:

Other parties, such as contract students or students from other institutions, interested can register from 1 June 2017.



Check the website.

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Published by  Institute for Interdisciplinary Studies