Complexity: can it be simplified?



Complex systems

This is a Bachelors course that Master students can follow by completing a larger assignment.

Content

What do economic crises, traffic jams, consciousness, the climate, immune systems 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 provides 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, 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.

To decide if you will like this course, this clip is useful: https://youtu.be/eJAs9Qr359o

Learning objectives

  • The student has insight into commonalities of different processes usually studied separately by researches from natural life-, and social sciences, and can think through complex phenomena in terms of multitudes of simultaneous interactions, in contrast to mono-causal thinking.  This will give the students a more adequate understanding of the dynamic reality they are a part of.
  • 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 having some insight in the pertaining methods.
  • The student understands the meaning of, and relations between, these concepts, as well as 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 two parts, first a series of lectures of 1,5 hours (including questions and discussion), given by researchers from the natural sciences. The series starts at September 5 with lectures on the general approach and methods, followed by an interim exam.  During the second part, students work in small groups on self-chosen projects, weekly supervised by a teacher. They present their results at the final session, December 12, and in a collective essay.

Coordination

This course is a joint effort by the IIS and the Institute for Advanced Study, coordinated by Jeroen Bruggeman j.p.bruggeman@uva.nl with assistance from Pia Michel pia.michel@student.uva.nl

Literature

Melanie Mitchell (2011) Complexity, a guided tour. Oxford University Press.

Schedule

You can find the timetable via Datanose

Examination

Halfway through the course, there will be an interim exam.  

Apply

UvA-students can register themself from June 14  until June 28 through the registration form and with your application you need to send a motivation letter of maximum 500 words include name, study and BA/MA which Datanose will automatically send to pia.michel@student.uva.nl

After 5 July you will receive an e-mail about your application.

If you have any trouble while registering please contact: Keuzeonderwijs-iis@uva.nl 

Other parties, such as contract students or students from other institutions, interested to follow the lectures only can contact Keuzeonderwijs-iis@uva.nl 

Costs

Book, approximately 14 euro at online shops.


Mode
Short-term
Conditions for admission
Open
Credits
6 ECTS,
Language of instruction
English
Starts in
September

Published by  Institute for Interdisciplinary Studies