A quiet revolution – the science of complex systems
Key text
This topic is sponsored by the ARC Complex Open Systems Research Network.
If you haven't heard of complex systems don't worry, you are not the only one. Scientists have been quietly puzzling over the complex interactions that define so many things in our world.
Most of us at some stage have thought that the world is a complex place. Things are never straightforward or simple. We understand bits and pieces of the world around us, but when it comes to real life, things often happen in ways that we couldn't have predicted.
Most of us believe that we simply don't know enough about the thing in question – be that our home, our social network, the stock market or the weather.
Many scientists have noticed the same thing in their research and wondered why this might be the case. They realised that knowing about the parts of a system does not often tell us how things work as a whole. The sorts of systems that behave this way are called complex systems, and efforts to better understand them are creating a revolution in science.
It's a revolution because working with complex systems goes against traditional science practice. Until now, scientists have spent a lot of time breaking things down into ever smaller component parts – known as reductionism – to understand how each part works in isolation of other parts, only to find that this does not help to understand how the whole system works together.
Now, scientists are bringing the pieces of the puzzle together to look at the interactions between components of a system, to understand how the whole system works. Complex systems science is about new ways of learning and understanding: indeed, many believe the rise of complex systems science represents a paradigm shift in thinking.
What is a complex system?
So what is a complex system? A system is a group of two or more parts which interact to function as a whole. The root word systema means 'organised whole'. The parts of a system are interconnected, and every system is composed of subsystems nested within larger systems. For example, a person is part of a family, which is part of a community, state, nation and world.
Related site: The study of complex systems
Provides examples of complex systems.
(University of Michigan, USA)
The origin of the word complex means 'twisted together' as in the individual threads that make up a tapestry. This suggests the parts of the system are linked in ways that creates a whole, with properties above and beyond those of the parts in isolation.
Although there are a wide variety of systems that are complex, they all have two elements in common. They all exhibit emergence and self organisation.
Emergence
Emergence is the formation of complex but regular patterns from the interaction of the many simple parts of a system. The emergent collective behaviour of a system cannot be predicted merely by understanding its individual elements, or from understanding the interactions between these elements, but it can in principle be predicted by seeing how all the elements work together. It is this element of regularity in the emergent behaviour that distinguishes complex systems from complicated and chaotic systems.
Self organisation
Self organisation is closely related to emergence and refers to the ability of the system to organise itself. The emergent features of the system appear spontaneously. There is no one in control of the system.
We humans like to believe we are in control of the things we construct, and that such mastery should extend over the natural world as well (Box 1: Complex systems, resilience and ecological sustainability). But complex systems are forming around us all the time, often without us being aware that it's happening. Connect up a few computers, as happened when the internet was born, and you have a simple system that's predictable and controllable. Connect up several million computers around the planet and you have a complex system behaving in ways no one could have imagined – and nobody is in control. The same applies to power systems, telephone systems and stock markets (Box 2: Building reliable networks).
In addition to emergence and self organisation, complex systems often share other features in common.
Other features of complex systems
Related site: The study of complex systems
Lists some characteristics of complex systems.
(University of Michigan, USA)
Complex systems are usually very dynamic – their characteristics change over time. Changes are frequently non-linear – sometimes the impact caused by a change can be completely out of proportion to the initial disturbance. And the system rarely reaches long-term equilibrium. Complex systems can also exist in alternate stable states, in which they behave quite differently. The point at which they flip to the alternate state is the threshold or tipping point. A complex system in a stable state can be 'flipped' into another stable state by a disturbance that pushes it across the threshold.
The importance of local interaction in complex systems
How can we make sense of complex systems? In the past, working with systems involved making a number of assumptions, and using relatively simple mathematical models to predict the behaviour of the system. For example, epidemiologists often use models of disease transmission that assume that populations of people are well mixed and everyone can contact everyone else. But such models are often unable to predict how an outbreak will move through a community.
Simplified models of complex systems frequently overlook one key feature of those systems – local interaction. The parts of a system tend to only interact with a small subset of the whole system, known as a local neighbourhood. For example, during our everyday lives we interact with friends, neighbours and work colleagues, as well as people on the street, but never everyone in the world. If a flu outbreak reaches a country via an infected tourist arriving at the airport, it's unlikely that you will be infected by direct contact with that person. If the disease spreads, and you become infected, it will probably be via people you are regularly in contact with.
The main reason local interactions haven't been included in models in the past is because the maths involved is extremely difficult. With the rise of computing power, models based on local interactions have been compared with simple models based on average properties. To the surprise of many, the models that incorporate local interactions display behaviour that is a lot closer to what is observed in the real world.
Mapping complex systems
Complex systems such as metabolic pathways, ecosystems and the internet can all be represented as a network of nodes. The nodes of the internet are computers and they are said to be linked if there is any interaction between them. For computers this might be an exchange of information. For other networks the interaction might involve the exchange of materials, energy or diseases.
Scale-free networks
You can deduce a lot about how a complex system behaves by understanding its network structure. Metabolic pathways in cells, ecosystems and the internet are scale-free networks. They are resilient structures because the random removal of any particular node is unlikely to stop the network from functioning as a whole. On the other hand, if a node with many links was targeted and removed, it could create a large system-wide disturbance – which is just what you want to do for terrorist or drug-trafficking networks.
Related site: Mapping networks of terrorist cells
Looks at the difficulty in mapping covert networks.
(International Network for Social Network Analysis, Canada)
The science of complex systems is opening up amazing new possibilities across many fields of physical and social science (Box 3: It's a small world after all). It is providing clues on how life emerged on Earth, why civilisations rise and fall, how disease is transmitted, how to manage outbreaks of disease, and what do we need to do to mitigate the damaging cyclic booms and busts of economic systems.
Complex systems science in Australia
Related site: Themes
Summarises the five major themes of the ARC Complex Open Systems Research Network.
(ARC Complex Open Systems Research Network, Australia)
In Australia, complex systems science is now being taught and studied at most of our universities. There are two Australian Research Council-funded Centres: the Centre of Excellence for Mathematics and Statistics of Complex Systems and the Centre for Complex Systems. The Australian Research Council also supports the Complex Open Systems Research Network and CSIRO have their Centre for Complex Systems Science.
Life is complex, it always was and always will be. However, by acknowledging the true nature of complex systems and focussing on ways of better describing them, we're all better placed to manage in a complex world.
Boxes
1. Complex systems, resilience and ecological sustainability
2. Building reliable networks
3. It's a small world after all
Related Nova topic
Predicting natural events
Posted October 2006.