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Complex Systems Science

Complex Systems Science integrates all scientific disciplines.

By themselves, the traditional sciences such as physics, chemistry, biology, sociology, psychology, economics, etc. cannot be applied to complex systems. This is because policy-relevant systems tend to cut across many domains. For example, epidemics involve biology, medicine, transportation, geography, and much more.

The Science of Complex Systems that has emerged over the last half century is trans-disciplinary and combines knowledge from all relevant domains. This new science is enabled by modern information and communication technologies (ICT). Generally, exploring the behaviour of complex systems involves large amounts of data, qualitative and quantitative models, and powerful computers. Many of these models are based on mathematics, including the quantitative mathematics of far-from-equilibrium systems and the qualitative mathematics of networks which play a central role in social systems.

The diagram above shows the traditional discipline-based sciences vertically as highly specialised scientists drill down for in-depth subject-specific knowledge. In contrast, the methods of complex systems cut across these disciplines, integrating their theory and knowledge for systems that do not respect conventional boundaries.

Agent-based modelling

The methods of complex systems science are computer-enabled. They involve building models of complex systems, are usually based on mathematics and usually involve large and heterogeneous data streams. Agent-Based Modelling (ABM) is an important computational technique, with formal models of agent interaction being used to determine the state of all the agents at time t+1 given all the states at time t. Since these systems are usually sensitive to initial conditions, one ABM simulation run rarely provides any useful information. Instead, large numbers of simulations are conducted for a variety of initial conditions enabling a ‘map’ of possible future system behaviour to be produced.

As an example, consider a computer simulation of a flu epidemic. Each of the agents represents individual people interacting with others and being exposed to the flu from those who are infectious. Some of these agents may travel on aeroplanes taking the flu to other countries. By simulating the behaviour of millions of agents it is possible to investigate how the flu may travel worldwide, and what might be the impact of policy measures.

Agent based models can be used to investigate the behaviour of people in many ordinary situations such as a supermarket. Badly designed stores can make it inconvenient for customers, with the undesirable effect that they do not find what they want and the store sells less. This and many other problems have been investigated by agent based modelling [1].

As another example, consider all the vehicle drivers in a city. The emergent behaviour of the traffic such as congestion and traffic jams can be simulated using each of the driver as an agent, interacting with those in its vicinity. In this case it is necessary to know the network behaviour of the drivers, e.g. a driver may take his wife to the station, and drop off one child at school and another at nursery before driving himself to work. To support such simulation, a new technology of synthetic micro populations has been developed which takes all available data and disaggregates it to build entire populations of synthetic agents, where the synthetic micro population has the same statistical properties as the original data.


[1] Eric Bonabeau. Agent-based modeling: Methods and techniques for simulating human systems, Proc Natl Acad Sci U S A. 2002 May 14; 99(Suppl 3): 7280–7287. doi: 10.1073/pnas.082080899 PMCID: PMC128598.

[2] Nigel Gilbert, Klaus G. Troitzsch, 2005, Simulation for the Social Scientist, Open University Press, MacGraw-Hill, (Maidenhead), ISBN 13 978 0335 21600 0

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Systems Thinking and Complexity

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