Course Title: System Dynamics

Part A: Course Overview

Course Title: System Dynamics

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH2220

City Campus

Postgraduate

145H Mathematical & Geospatial Sciences

Face-to-Face

 Sem 2 2012,
Sem 2 2014

MATH2220

City Campus

Postgraduate

171H School of Science

Face-to-Face

 Sem 1 2019

Course Coordinator: Prof. John Hearne

Course Coordinator Phone: +61 3 9925 2284

Course Coordinator Email: john.hearne@rmit.edu.au


Pre-requisite Courses and Assumed Knowledge and Capabilities

Basic knowledge of mathematics and comfort with the practical use of computer packages is required.


Course Description

This course is about understanding systems. System dynamics (SD) provides the tools to model and analyse the dynamic behaviour of a complex system. In this way the relationship between the system structure and behaviour can be understood. Proposed changes in structure can be simulated and good management strategies identified. The methodology of system dynamics will be covered and extensive use made of software designed specifically for SD modelling. The course will cover applications from a wide range of problems such as supply chains, staff fluctuations, handling student stress, and ecology.


Objectives/Learning Outcomes/Capability Development

This course contributes to the following Program Learning Outcomes for MC004 Master of Statistics and Operations Research and MC242 Master of Analytics:

Personal and professional awareness

  • the ability to contextualise outputs where data are drawn from diverse and evolving social, political and cultural dimensions
  • the ability to reflect on experience and improve your own future practice
  • the ability to apply the principles of lifelong learning to any new challenge.

Knowledge and technical competence

  • an understanding of appropriate and relevant, fundamental and applied mathematical knowledge, methodologies and modern computational tools.

Problem-solving

  • the ability to bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of problems
  • an understanding of the relationship between the purpose of a model and the required level of complexity and accuracy.

Communication

  • the ability to effectively communicate both technical and non-technical material in a range of forms (written, electronic, graphic, oral), and to tailor the style and means of communication to different audiences.  Of particular interest is the ability to explain technical material, without unnecessary jargon, to lay persons such as the general public or line managers.

Information literacy

  • the ability to locate and use data and information and evaluate its quality with respect to its authority and relevance


On completion of this course you should be able to:

  1. Model a complex system;
  2. Explore the dynamic behaviour of a system;
  3. Critically discuss the importance of feedback in systems;
  4. Recommend strategies for improving system behaviour.


Overview of Learning Activities

Attend 3 hours of lectures and tutorial/computer laboratory sessions per week and 3 hours of directed laboratory work per week, which is unsupervised. You will also be expected to complete some written exercises, and both individual and group projects.


Overview of Learning Resources

You will have access to extensive course materials made available via myRMIT Studies including digitised readings, lecture notes and a detailed study program, external internet links and access to RMIT Library online and hardcopy resources. The software used for this course is available free for personal download and is also available in the labs.

Library Subject Guide for Mathematics & Statistics http://rmit.libguides.com/mathstats

 


Overview of Assessment

☒This course has no hurdle requirements.

Assessment Tasks:

Early Assessment Task: Written Exercises
Weighting 10%
This assessment task supports CLOs 1, 2

Assessment Task 2: Projects
Weighting 20%
This assessment task supports CLOs 1, 2, 3, and 4

Assessment Task 3: Lab Test
Weighting 20%
This assessment task supports CLOs 1, 2, 3, and 4

Assessment 4: Final Exam
Weighting 50%
This assessment task supports CLOs 1, 2, 3, and 4