Outline

Overview

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.

Unit details and rules

Academic unit	Civil Engineering
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Michael Harre, michael.harre@sydney.edu.au

Type	Description	Weight	Due	Length
Assignment	Article review	10%	Week 03	n/a
Outcomes assessed:
Assignment	Project proposal	25%	Week 06	n/a
Outcomes assessed:
Presentation	Project presentation	25%	Week 11	n/a
Outcomes assessed:
Assignment	Project report	40%	Week 14 (STUVAC)	n/a
Outcomes assessed:
= group assignment ?

Assessment summary

Article Review: summarise and critically evaluate a credibly-sourced agent-based modelling article in complex systems

Project proposal, presentation and report: students will work in a group to extend an agent-based model of a real-world complex system, applying feedback at various stages, and producing a report that communicates this development and future pathways (including how they would validate their model).

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

Penalty clause:

For every calendar day up to and including ten calendar days after the due date, a penalty of 5% of the maximum awardable marks will be applied to late work. The penalty will be calculated by first marking the work, and then subtracting 5% of the maximum awardable mark for each calendar day after the due date.

Example: Consider an assignment’s maximum awardable mark is 10; the assignment is submitted 2 days late; and the assignment is marked as 7/10. After applying the penalty, marks will be: 7 - (0.5 x 2) = 6/10.

For work submitted more than ten calendar days after the due date a mark of zero will be awarded. The marker may elect to, but is not required to, provide feedback on such work.

Refer to section 7A of Assessment procedures policy available at: http://sydney.edu.au/policies/showdoc.aspx?recnum=PDOC2012/267&RendNum=0

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

All assessments must be repeated or replaced with different assessment if missed due to special consideration. Extensions for the group Project Presentation will only be granted in the case where formal Special Consideration has been applied for and approved.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Weekly schedule

WK	Topic	Learning activity
Week 01	Basic concepts of "complex systems theory"	Lecture (2 hr)
Week 02	Basic concepts of "complex systems theory" (emergence)	Lecture and tutorial (3 hr)
Week 03	Methods for the quantitative analysis and modelling of collective emergent phenomena	Lecture and tutorial (3 hr)
Week 04	Agent-based modelling	Lecture and tutorial (3 hr)
Week 05	Agent-based simulation with NetLogo - 1	Lecture and tutorial (3 hr)
Week 06	Agent-based simulation with NetLogo - 2	Lecture and tutorial (3 hr)
Week 07	Introduction to cellular automata	Lecture and tutorial (3 hr)
Week 08	Cellular automata (game of life)	Lecture and tutorial (3 hr)
Week 09	Evolutionary computation and evolutionary algorithms	Lecture and tutorial (3 hr)
Week 10	Bio-inspired algorithms 1 (ant colony+ swarms)	Lecture and tutorial (3 hr)
Week 11	Bio-inspired algorithms 2 (Artificial Neural Networks)	Lecture and tutorial (3 hr)
Week 12	Introduction to game theory	Lecture and tutorial (3 hr)
Week 13	Advanced game theory (Evolutionary games)	Lecture and tutorial (3 hr)

Attendance and class requirements

You are expected to attend all classes for this unit (it is a face-to-face unit). While materials and lecture recordings will be made available these can sometimes fail to work as we would like them to, so it is the student’s responsibility to attend the class in person in order to get the most from this unit of study.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

All readings for this unit can be accessed through the Library eReserve, available on Canvas.

Introduction to the Modeling and Analysis of Complex Systems

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

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.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

No changes have been made since this unit was last offered.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

CSYS5010: Introduction to Complex Systems

Semester 1, 2020 [Normal evening] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect