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During 2021 we will continue to support students who need to study remotely due to the ongoing impacts of COVID-19 and travel restrictions. Make sure you check the location code when selecting a unit outline or choosing your units of study in Sydney Student. Find out more about what these codes mean. Both remote and on-campus locations have the same learning activities and assessments, however teaching staff may vary. More information about face-to-face teaching and assessment arrangements for each unit will be provided on Canvas.

Unit of study_

CSYS5010: Introduction to Complex Systems

Globalisation, rapid technological advances, the development of integrated and distributed systems, cross-disciplinary technical collaboration, and the emergence of "evolved" (as opposed to designed) systems are some of the reasons why many systems have begun to be described as complex systems in recent times. Complex technological, biological, socio-economic and socio-ecological systems (power grids, communication and transport systems, food webs, megaprojects, and interdependent civil infrastructure) are composed of large numbers of diverse interacting parts and exhibit self-organisation and/or emergent behaviour. This unit will introduce the basic concepts of "complex systems theory", and focus on methods for the quantitative analysis and modelling of collective emergent phenomena, using diverse computational approaches such as agent-based modelling and simulation, cellular automata, bio-inspired algorithms, and game theory. Students will gain theoretical knowledge of complex adaptive systems, coupled with practical skills in computational simulation and forecasting using a range of modern toolkits.

Code CSYS5010
Academic unit Civil Engineering
Credit points 6
Prerequisites:
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None
Corequisites:
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None
Prohibitions:
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None

At the completion of this unit, you should be able to:

  • LO1. understand and analyse the dynamics of complex systems using intermediate critical analysis skills
  • LO2. analyse and evaluate models of complex systems using scientific programming and the 'Modelling Loop'
  • LO3. Create, using a scientific modelling language such as NetLogo, multi-agent models of complex systems
  • LO4. understand the nature, structure, function and evolution of complex systems and emergent behaviour in multiple different fields
  • LO5. select and apply different approaches to analysing complex systems in different domains (e.g. game theory, dynamical systems, genetic algorithms)
  • LO6. design and evaluate large systems that satisfy structural and functional criteria within given domains and contexts integrating complex systems approaches.

Unit outlines

Unit outlines will be available 2 weeks before the first day of teaching for the relevant session.