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Unit Overview

Description

This unit equips students with tools to model, analyse, and interpret complex systems in science and nature. The focus is on building models—such as those for disease spread, flocking birds, or online networks—using frameworks like agent-based models, cellular automata, fractals, reaction–diffusion systems, and game theory, and on understanding how local interactions give rise to emergent, system-wide behaviour.

Key questions include: What makes a good model of a complex system? How do modelling assumptions shape outcomes? What level of abstraction is appropriate? How can we validate or generalise results? Students explore these questions using both qualitative and quantitative approaches, with an emphasis on numerical (rather than analytical) modelling.

Concepts from complex networks (MATH3002) and dynamical systems (MATH3021) are introduced as needed—prior familiarity is recommended but not required. A central feature of the unit is a modelling project implemented in Python — no prior experience with Python is expected.

Complex systems are found in biology, economics, engineering, social science, and the environment, so whatever a student's background, they will find relevant and meaningful applications of complex systems thinking.

Credit
6 points
Offering
(see Timetable)
AvailabilityLocationMode
Semester 2UWA (Perth)On-campus

The timetable for this teaching period is not currently available. Please see the Important Dates page for the timetable release date and other key date information.

Details for undergraduate courses
  • Level 3 option in the Mathematics; Mathematics major sequences
  • Level 3 elective
Outcomes

Students are able to (1) identify and describe the characteristic features of complex systems, including emergence, feedback, nonlinearity, and sensitivity to initial conditions

; (2) develop and analyse models of complex systems using frameworks such as agent-based models, cellular automata, fractals, reaction–diffusion systems, and game-theoretic interactions; (3) investigate how system-level behaviour arises from local interactions, including phenomena such as pattern formation, synchronisation, and critical transitions

; (4) evaluate modelling assumptions and abstraction levels, and assess their impact on simulation results and the generalisability of conclusions; and (5) implement numerical models of complex systems in Python, and use simulation outputs to interpret and communicate system dynamics.

Assessment

Indicative assessments in this unit are as follows: (1) in-class tests; (2) project; and (3) examination. Further information is available in the unit outline.



Student may be offered supplementary assessment in this unit if they meet the eligibility criteria.

Unit Coordinator(s)
Dr Shannon Algar
Unit rules
Prerequisites
MATH2021 Differential Equations
Advisable prior study
MATH2064 Numerical Methods (ID 8376)
Contact hours
lectures: 3 hours per week
workshops: 1 hour per week
  • The availability of units in Semester 1, 2, etc. was correct at the time of publication but may be subject to change.
  • All students are responsible for identifying when they need assistance to improve their academic learning, research, English language and numeracy skills; seeking out the services and resources available to help them; and applying what they learn. Students are encouraged to register for free online support through GETSmart; to help themselves to the extensive range of resources on UWA's STUDYSmarter website; and to participate in WRITESmart and (ma+hs)Smart drop-ins and workshops.
  • Visit the Essential Textbooks website to see if any textbooks are required for this Unit. The website is updated regularly so content may change. Students are recommended to purchase Essential Textbooks, but a limited number of copies of all Essential Textbooks are held in the Library in print, and as an ebook where possible. Recommended readings for the unit can be accessed in Unit Readings directly through the Learning Management System (LMS).
  • Contact hours provide an indication of the type and extent of in-class activities this unit may contain. The total amount of student work (including contact hours, assessment time, and self-study) will approximate 150 hours per 6 credit points.