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Why is complexity science important now?



Fully-funded PhD places are available at the new Center for Doctoral Training in Next Generation Computational Modelling. The CDT pursues computational modelling research spanning engineering, computer science, mathematics, and the physical, natural and life sciences.


The rapid increase in interest in complexity science is being driven predominantly by new challenges and demands in technology. Various industries are becoming increasingly aware that traditional approaches to design and engineering are failing to keep up with the increasing scale of today’s systems.

"The management and design problems facing modern ICT [information and communication technologies] practitioners are critically concerned with ensuring reliability, usability, robustness, efficiency, effectiveness, security, and evolvability in the interconnected ICT systems upon which societies and economies increasingly rely. As our world becomes an ever more interconnected place, so-called “systems” ideas and perspectives become increasingly important. A central issue is the emergent behaviour of complex systems.'' - UK government's Foresight report on ICT and complexity. (Read a non-technical summary.)

Systems from telecommunications, to information storage and retrieval, to economic trading, are rapidly increasing in scale. Also, processes and transactions that used to be implemented manually are becoming automatic, and many systems are being connected together. These increases in scale and connectivity make managing the complex dynamics of such systems difficult. Traditional approaches to engineering try to remove complex dynamical behaviours and emergent phenomena that are difficult to control and manage - but they often happen anyway, producing errors and undesired behaviour that can bring the system crashing down. New approaches to design, engineer, manage and control complex systems are urgently needed. This produces a demand from industry to both drive new research at universities and seek new graduates trained to understand and deal with complexity. This creates an urgent need for a change in training:

"Open challenges that must be faced by the complex systems community include overcoming institutional and cultural obstacles to interdisciplinary and industrial involvement in complexity research. In the UK in particular, most computer science undergraduate degree programmes currently have a manifest lack of formal training in complexity ideas and techniques: especially simulation modelling methods, experiment design, and statistical data analysis.'' - ibid.

This need for new training in complexity science has now resulted in the development of new courses in complexity and related subjects, (see Courses).

Another driver in the recent upsurge of interest in complexity science is the availability of computing resources with sufficient power to model large scale complex systems and investigate new ways of approaching their design. Without this, using only formal mathematical methods and traditional design approaches, the design of large scale systems was forced to avoid complex dynamical behaviours because they were too difficult to predict. Computational modelling allows new approaches that were not previously testable.

The third main driver is systems biology (or "the new biology", as it is sometimes known). With the advent of high-throughput devices (e.g. genome sequencing and microarray data) the biological sciences are for the first time able to gather information about whole systems and begin to ask questions not just about one detail at a time, but about the complex interaction of the components together. There has been an enormous change in the way cellular and molecular biology, in particular, is being researched, and large amounts of funding from the biological research council is being directed to these new approaches (see Bioscience for society: a ten-year vision, BBSRC (2003) and UK Biosciences: The next ten years, BBSRC (2004)).

Complexity Science interfaces with systems biology in two different ways:

  1. As systems biologists gain new information about how biological systems work, and how they cope with and exploit emergent systems level phenomena, we gain new insight and inspiration for tackling complexity in engineered and technological systems.
  2. As we tackle complexity in technological systems and gain understanding of new approaches to modelling and controlling complexity in engineered systems, we can provide new insight that can help biologists understand complexity in natural systems.