Definition of complex systems
A complex system is one made up of many components that have complex interactions and interdependencies. Examples of complex systems include critical infrastructure (such as transport networks, the electricity grid and financial systems), the Internet, biological systems and environmental ecosystems. These systems are characterised by uncertainty, and by emergent behaviour that is difficult to predict from an analysis of the individual parts.
Who works in complex systems
Students: Jim Lewis, Jannah Baker
Post-docs: Sandra Johnson, Paul Wu
Lecturers:
Professors: Kerrie Mengersen, Clinton Fookes, Gerard Ledwich
What expertise do we have?
- Systems engineering, concept of operations and other tools and methods for structuring the problem domain in line with user requirements
- Bayesian network, dynamic Bayesian network and other methods for analysis and prediction under uncertainty
- Eliciting and incorporating expert knowledge with data in a quantitative complex systems model
- Methods for analysing cause and effect in a complex system
- Methods for predicting system performance
- Agent based modelling and systems dynamics modelling
Case studies: concrete examples of what we have done
Environment:
- Dr Johnson- using social, economic, cultural and environmental criteria in defining management zones and baseline benchmarks for harbour management.
- Dr Wu – modelling the impact of dredging on marine species such as seagrass and finding windows in time (environmental windows) when the impact can be minimised.
Medical:
- Professor Mengersen – Bayesian network model of transmission of infectious diseases (MSRA) in hospitals. [Statistical Methods for Hospital Monitoring with R].
- Ms Baker – spatio-temporal modelling of chronic diseases and associated risk factors.
Business and operations:
- Mr Lewis – modelling electricity usage and policy impact for stakeholder engagement.
- Dr Johnson and Dr Wu – network and queue based modelling of airports to provide real-time operational decision support.
- Professor Fookes and Dr Wu – agent based modelling of passengers to support airport operations and design.
- Professor Ledwich – agent based modelling of complex electricity networks.
Aerospace:
- Dr Johnson – modelling the risk presented to people on the ground due to failures onboard Unmanned Aerial Vehicles.
Links to other capabilities
- Decision support systems, spatial-temporal modelling, applications, visualisations, computation
Contact
Sandra Johnson: sandra.johnson@qut.edu.au
Paul Wu: p.wu@qut.edu.au
Bayesian Research & Applications Group 