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Complexity: can it be simplified? (EN)

What do pandemics, 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 the movement of individual cells in a certain medium, it could not grasp the simultaneous interplay of many different interacting components. Complexity science uses novel mathematical and computational methods to better understand and predict complex processes. This relatively new approach has become widely popular during the past decennia, and is currently used in natural-, life-, and social sciences. Examples are: ecosystems, groups of both competing and cooperating monkeys, embryo development, the global climate, stock markets, epidemics, criminal networks and neural networks.

Typical characteristics of complex systems are: emergence, tipping points, phase transitions, non-linear processes, resilience, bifurcation, scale-freeness, and deterministic chaos. 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 going deeply into 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.

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 all 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

After this course the student is able to:

  • Show 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.
  • 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.
  • Understand of the meaning of, and relations between, these concepts, as well as basic principles of the models and methods wherein they are used.
  • Provide a critical evaluation of the usefulness of complexity science, as well as its concepts and methods for their own field of study.
  • Perform elementary modeling in, for example, Netlogo or another language

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}.

Recommended prior knowledge

This is a Bachelors course that can be followed without background in math or programming. However, formal modeling will be paid some attention to in the lectures, and elementary modeling skills in Netlogo will be taught and practiced. For Master students who want to follow this course, a computational (or math) model is obligatory in their group assignment (see below).

Teaching format and assessment 

The program takes place during the first semester and consists of two parts. First there is a series of lectures of 1,5 hours (including questions and discussion), given by researchers from the natural sciences on the general approach and methods, followed by an interim exam.  During the second part, students work in small groups on self-chosen projects, supervised by a teacher. They present their results at the final session halfway December and in a collective essay.

Timetable

See Datanose.

Course materials

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

Costs

  • Book, approximately 14 euro at online shops.
  • For external candidates, see website.

Number of participants

Max. 70

Registration

UvA-students can register themselves from early june (date follows), (look for code 5512COMP6Y in SIS) until a week before the start of the course.

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

Other parties, such as contract students, UvA staff or students from other institutions, interested can register from 9 June, through the registration form.

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

Complexity: can it be simplified? (EN)
Vorm Kortlopend, kortlopend
Studielast 6 EC,
Voertaal Engels
Toelatingseisen Open
Start September