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CSYS5010: Introduction to Complex Systems (2019 - Semester 1)

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Unit: CSYS5010: Introduction to Complex Systems (6 CP)
Mode: Normal-Day
On Offer: Yes
Level: Postgraduate
Faculty/School: Faculty of Engineering and Information Technologies
Unit Coordinator/s: Dr Harré, Michael
Session options: Semester 1, Semester 2
Versions for this Unit:
Campus: Camperdown/Darlington
Pre-Requisites: None.
Brief Handbook Description: 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.
Assumed Knowledge: None.
Lecturer/s: Dr Lizier, Joseph
Dr Harré, Michael
Nigmatullin, Ramil
Timetable: CSYS5010 Timetable
Time Commitment:
# Activity Name Hours per Week Sessions per Week Weeks per Semester
1 Lecture 2.00 1 13
2 Laboratory 1.00 1 13
3 Independent Study 7.00 1 13
T&L Activities: This unit of study comprises of regular lectures, as well as laboratory / tutorial sessions. These sessions will take place in a lab with access to relevant computer facilities. Depending on the syllabus, some weeks will comprise tutorials where students will solve problems with the help of tutors, and other weeks will comprise programming or software based laboratory experiments.

During lab sessions with a programming/software component, tutors will be present to assist students develop relevant programming or other computing skills.

Students will use independent study time to further develop their computing skills and to practise solving analytical problems.

Attributes listed here represent the key course goals (see Course Map tab) designated for this unit. The list below describes how these attributes are developed through practice in the unit. See Learning Outcomes and Assessment tabs for details of how these attributes are assessed.

Attribute Development Method Attribute Developed
Develop through lectures, case studies and tutorial examples, a working knowledge of techniques and tools that can be used for complex system and large scale system analysis and design Design (Level 3)
Develop through lectures and tutorial discussions an understanding of concepts related to complex systems analysis, such as agent-based modelling, bio-inspired algorithms and game theory, and how to apply these techniques to understand complex systems Engineering/IT Specialisation (Level 4)
Develop through lectures and tutorial discussions an understanding of:

1 - Multi agent system based simulations
2 - Using Netlogo
3 - Fluency in implementing and using bio-inspired algorithms and implementing other complicated algorithms
Maths/Science Methods and Tools (Level 5)
Develop through assignments and case studies an ability to critically dissect and understand the structure and function of complex systems, and the ability to describe this understanding efficiently and quantitatively Information Seeking (Level 3)

For explanation of attributes and levels see Engineering & IT Graduate Outcomes Table 2018.

Learning outcomes are the key abilities and knowledge that will be assessed in this unit. They are listed according to the course goal supported by each. See Assessment Tab for details how each outcome is assessed.

Information Seeking (Level 3)
1. Understand and analyse the dynamics of complex systems using intermediate critical analysis skills.
Maths/Science Methods and Tools (Level 5)
2. Analyse and evaluate models of complex systems using scientific programming and the 'Modelling Loop'.
3. Create, using a scientific modelling language such as NetLogo, multi-agent models of complex systems.
Engineering/IT Specialisation (Level 4)
4. Understand the nature, structure, function and evolution of complex systems and emergent behaviour in multiple different fields.
5. Select and apply different approaches to analysing complex systems in different domains (e.g. game theory, dynamical systems, genetic algorithms).
Design (Level 3)
6. Design and evaluate large systems that satisfy structural and functional criteria within given domains and contexts integrating complex systems approaches.
Assessment Methods:
# Name Group Weight Due Week Outcomes
1 Article Review* No 10.00 Week 3 1, 2, 4,
2 Project Proposal* Yes 25.00 Week 6 1, 2, 4, 5, 6,
3 Project Presentation* Yes 25.00 Week 11 1, 2, 3, 4, 5, 6,
4 Project Report* Yes 40.00 STUVAC (Week 14) 2, 3, 4, 5, 6,
Assessment Description: The assessments will consist of four assignments (10%, 25%, 25% and 40% each). The second through fourth of the assignments shall be attempted by groups of three, or as determined by lecturer, depending on student numbers.

Written assignments (without formally applying for special consideration) will be assessed a penalty of 7% per day until depletion. Extensions for the group Project Presentation will only be granted in the case where formal Special Consideration has been applied for and approved.

Then university has authorised and mandated the use of text based similarity detecting software TURNITIN for all text based written assignments.

* means this assessment must be repeated or will be replaced with a different assessment if missed due to special consideration

IMPORTANT: There may be statistically defensible moderation when combining the marks from each component to ensure consistency of marking between markers, and alignment of final grades with unit outcomes and grade descriptors.
Assessment Feedback: Feedback for assignments will be through Blackboard e-learning portal, though which these assignments will be submitted.
Grade Type Description
Standards Based Assessment Final grades in this unit are awarded at levels of HD for High Distinction, DI (previously D) for Distinction, CR for Credit, PS (previously P) for Pass and FA (previously F) for Fail as defined by University of Sydney Assessment Policy. Details of the Assessment Policy are available on the Policies website at . Standards for grades in individual assessment tasks and the summative method for obtaining a final mark in the unit will be set out in a marking guide supplied by the unit coordinator.
Policies & Procedures: See the policies page of the faculty website at for information regarding university policies and local provisions and procedures within the Faculty of Engineering and Information Technologies.
Prescribed Text/s: Note: Students are expected to have a personal copy of all books listed.
Recommended Reference/s: Note: References are provided for guidance purposes only. Students are advised to consult these books in the university library. Purchase is not required.

Note that the "Weeks" referred to in this Schedule are those of the official university semester calendar

Week Description
Week 1 Lecture: Introduction to Complex Systems Modelling
Week 2 Lecture/Tutorial: Agent Based Modelling: ABMI
Week 3 Lecture/Tutorial: Agent Based Modelling: ABMII
Assessment Due: Article Review*
Week 4 Lecture/Tutorial: Agent Based Modelling: ABMIII
Week 5 Lecture/Tutorial: Agent Based Modelling: ABMIV
Week 6 Lecture/Tutorial: Dynamical Systems I
Assessment Due: Project Proposal*
Week 7 Lecture/Tutorial: Dynamical Systems II
Week 8 Lecture/Tutorial: Evolution and Genetic Algorithms
Week 9 Lecture/Tutorial: Game Theory
Week 10 Lecture/Tutorial: Computation, Information, Order and Randomness
Week 11 Lecture/Tutorial: Presentations
Assessment Due: Project Presentation*
Week 12 Lecture/Tutorial: Bio-inspired Algorithms
Week 13 Lecture/Tutorial: Wrap up
STUVAC (Week 14) Assessment Due: Project Report*

Course Relations

The following is a list of courses which have added this Unit to their structure.

Course Year(s) Offered
Graduate Diploma in Complex Systems 2017, 2018, 2019
Master of Complex Systems 2017, 2018, 2019
Graduate Certificate in Information Technology Management 2018, 2019
Graduate Diploma in Information Technology Management 2018, 2019
Master of Data Science 2018, 2019
Master of Engineering 2015, 2016, 2017, 2018, 2019
Master of Information Technology 2018, 2019
Master of Information Technology Management 2018, 2019
Master of Professional Engineering (Accelerated) (Chemical & Biomolecular) 2019
Master of Professional Engineering (Accelerated) (Civil) 2019
Master of Professional Engineering (Accelerated) (Fluids) 2019
Master of Professional Engineering (Accelerated) (Geomechanical) 2019
Master of Professional Engineering (Accelerated) (Mechanical) 2019
Master of Professional Engineering (Accelerated) (Structural) 2019
Master of Professional Engineering (Chemical & Biomolecular) 2015, 2016, 2017, 2018, 2019
Master of Professional Engineering (Civil) 2017, 2018, 2019
Master of Professional Engineering (Fluids) 2017, 2018, 2019
Master of Professional Engineering (Geomechanical) 2017, 2018, 2019
Master of Professional Engineering (Mechanical) 2015, 2016, 2017, 2018, 2019
Master of Professional Engineering (Structural) 2017, 2018, 2019
Master of IT/Master of IT Management 2018, 2019

Course Goals

This unit contributes to the achievement of the following course goals:

Attribute Practiced Assessed
Information Seeking (Level 3) Yes 9%
Maths/Science Methods and Tools (Level 5) Yes 30%
Engineering/IT Specialisation (Level 4) Yes 40.5%
Design (Level 3) Yes 20.5%

These goals are selected from Engineering & IT Graduate Outcomes Table 2018 which defines overall goals for courses where this unit is primarily offered. See Engineering & IT Graduate Outcomes Table 2018 for details of the attributes and levels to be developed in the course as a whole. Percentage figures alongside each course goal provide a rough indication of their relative weighting in assessment for this unit. Note that not all goals are necessarily part of assessment. Some may be more about practice activity. See Learning outcomes for details of what is assessed in relation to each goal and Assessment for details of how the outcome is assessed. See Attributes for details of practice provided for each goal.