Explores the emergent behaviour of complex systems by focusing on interconnections of system components and on systems architecture, rather than the individual components themselves. This research area represents a novel scientific approach that works across traditional discipline boundaries. Examples of Complexity Science range from forecasting and decision-making processes, whole-system multi-scale models and data-intensive science, to fundamentally understanding complex behaviour itself.
Research Area Connections
Our portfolio is interdisciplinary and our research areas are intrinsically connected to one another. Based on grant data, this area is most strongly linked to the following other research areas:
Complexity is inherent in many large systems and aspects of Complexity Science are now integral to many other research areas (e.g. Non-Linear Systems, Continuum Mechanics, Mathematical Biology, and Operational Research), as well as to broader themes (e.g. systems engineering, systems biology and network science).
Several large EPSRC investments relating to this research area, notably a number of Centres for Doctoral Training (CDTs), will come to an end in the next few years. Graduates from the CDTs are now moving into other fields – a clear reflection that, having succeeded in stimulating thinking about Complexity Science and establishing this discipline in the research landscape, the area is now entering a new phase and the need for a dedicated research area is tapering off.
Together with the increasingly blurred boundaries with other research areas – Complexity Science has become embedded in many areas of science and engineering in recent years – this means we anticipate a reduction in this research area, with future funding focusing on addressing real-world problems (Evidence source 1). A major impact of funding in Complexity Science has been recognition of the importance of treating systems as a whole and thinking beyond traditional boundaries, and people trained in this mindset are clearly needed to tackle many real-world challenges.
With a review of this research area scheduled for 2017, by the end of the current Delivery Plan period we aim to have:
- Taken a forward look with regard to Complexity Science in the UK and how it is best supported within our portfolio
- Enabled the development of tools and techniques to tackle complex systems in the most appropriate way
- Supported people who investigate complex real-world systems to address emerging global challenges.
Over the past ten years, there have been a number of investments in Complexity Science, including a European Research Area Network (Complexity-NET) and a number of Systems Biology Centres and CDTs (Evidence source 2).
The Research Excellence Framework (REF) 2014 exercise noted that there has been a significant increase in activity in this field since Research Assessment Exercise (RAE) 2008, with some of the work being of high quality (Evidence source 3). Complexity Science is a highly interdisciplinary research area, with funding contributions from all four EPSRC Capability Themes (Mathematical Sciences, Information and Communication Technologies, Engineering and Physical Sciences), as well as from Healthcare Technologies and the Living with Environmental Change (LWEC) cross-Research Council programme. The multidisciplinary nature of activity is also highlighted by the continued success of the Systems Biology Centres co-funded with the Biotechnology and Biological Sciences Research Council (BBSRC).
Complexity Science and the tools it creates are critically important to understanding emerging topics such as the water-energy-food nexus, interdependency and the resilience of infrastructure systems. The focus should therefore be on identifying how tools and techniques from Complexity Science can continue to play an important role in challenge-led research (Evidence source 4).
As of April 2016, training accounted for almost half of this research area – largely due to several CDTs which end in 2018. Although Complexity Science was not a priority area in the last CDT call in 2013, several CDTs supported as a result contain aspects of the discipline, especially MathSys (University of Warwick) and the Risk and Uncertainty CDTs (University of Liverpool). There has been limited demand for the postdoctoral fellowship priority.
Currently, the scope of the Complexity Science area is not very well defined and many aspects may be better described as part of Non-Linear Systems, Continuum Mechanics, Mathematical Biology or Operational Research. We therefore plan to review Complexity Science in 2017 to better understand its impact as a research area and its identity within our research area taxonomy.
This area aligns with all Outcomes and Ambitions, but most particularly the following Ambitions in the Resilient and Connected Nation Outcomes:
R1: Achieve energy and security efficiency
Tools and methods developed for complex systems can be used to understand highly interconnected energy systems and aid decision-making processes.
R2: Ensure a reliable infrastructure which underpins the UK economy
Complexity Science is important to understanding interdependencies of different levels of infrastructure, through multi-scale modelling, and to ensuring their highest possible connectedness and resilience.
R5: Build new tools to adapt to and mitigate climate change
Our climate is a complex system responding to changes abruptly and in a highly non-linear way. Understanding the climate and its changes over time therefore relies on Complexity Science methodology.
C2: Achieve transformational development and use of the Internet of Things
The Internet of Things is an amalgamation of a huge number of heterogeneous components that interact in complex relationships, and their modelling and understanding require tools from Complexity Science.
C3: Deliver intelligent technologies and systems
The path towards intelligent technologies requires a systems approach with tools that focus on the connections between individual parts rather than the parts themselves.
- EPSRC, Mathematical Sciences Community Overview Documents (PDF), (2016)
- Analysis of EPSRC student, fellowship and grant data
- Research Excellence Framework (REF) 2014: Overview Report by Main Panel B and Sub-panels 7-15 (PDF), (2014)
- EPSRC, International Review of Mathematics (PDF), (2011)
- EPSRC, Engineering Grand Challenges: Reports on Outcomes of a Retreat, (2014)
- Deloitte, Measuring the Economic Benefits of Mathematical Science Research in the UK, (2012)
We aim to reduce this area as a proportion of the EPSRC portfolio.
Visualising our Portfolio (VoP)
Visualising our portfolio (VoP) is a tool for users to visually interact with the EPSRC portfolio and data relationships.
EPSRC support by research area in Complexity science (GoW)
Search EPSRC's research and training grants.