Outline

Overview

This unit aims to introduce the central concepts of systems thinking. Topics covered include micro-foundations of aggregate patterns, tools for systems modelling, scenario-based decision-making, network analysis and design thinking, data sources and new technologies to inform systems-orientated approaches. This unit provides students with an opportunity to use the systems approach to discover new ways of conceptualising complex enterprise problems and identifying innovative solutions to address complex, multi-dimensional and multi-scale issues.

Unit details and rules

Academic unit	Management Education
Credit points	6
Prerequisites ?	None
Corequisites ?	FMBA5001
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Dirk Hovorka, dirk.hovorka@sydney.edu.au

Assessment

The census date for this unit availability is 7 November 2025

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Portfolio or journal	Reflection Written assessment	40%	Formal exam period	1000 words	AI allowed
Outcomes assessed:
Written work	Weekly reflections Written task	20%	Multiple weeks	4 x 350 words	AI allowed
Outcomes assessed:
Written work	Casual loop diagrams Written assessment	10%	Week 02	1 diagram, 500 words	AI allowed
Outcomes assessed:
Written work	Collaboration process document Written assessment	0%	Week 02	4 pages	AI allowed
Outcomes assessed:
Presentation	Model a system and intervention Video delivery	30%	Week 06	5 minutes	AI allowed
Outcomes assessed:
= group assignment ?

Assessment summary

Weekly learning reflections: This assessment consists of participation in online discussion throughout the semester, in parallel to the lectures. Every week there will be a discussion topic relating previously introduced concepts to each team’s complex issue. Students will submit learning reflections to Canvas and to a weekly discussion thread on Canvas.
Causal loop diagrams: After introductory reading and class, students will explore suggested system phenomenon and then create a causal loop diagram and explanation.
Describe an intervention: In interdisciplinary groups, students will analyse current trends and dynamics in suggested phenomenon and identify potential opportunities for intervention. Students will present recommendations in a short video, supplemented by a written appendix that maps key causal drivers, business environment and exogenous forces..
Collaboration process document: This assignment consists of two documents that summarise the development of the team
assignment. Students will 1) prepare a team charter that outlines the groups’ approach and 2) submit their ‘meeting minutes’ to document regular meetings concisely. This assessment is not worth 'marks' by itself. Insead, NOT submitting it will result in a 10% reductionin the group project mark.
Reflection: In this assignment you will evaluate your own role and your team’s processes and dynamics during your development of your systems intervention. Building on relevant examples from your teamwork experience, analyse the ways of working with each other, including emergent norms, sources and flows of information, collaboration, joint learning and feedback. Applying suitable frameworks from the unit, you will critically reflect on your own role in the team and it’s dynamics and identify practical improvements for future team work.

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	Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Distinction	75 - 84	Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Credit	65 - 74	Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Pass	50 - 64	Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	1. Introduction 2. Examples of complex systems approaches	Workshop (4 hr)
Week 01	1. Emergence and self-organisation; 2. Introduction to modelling	Workshop (4 hr)
Week 02	1. Feedback systems thinking. 2. Causal loop models/landscapes. 3. Project Introduction	Workshop (4 hr)
Week 02	1. Systems dynamics; modelling process and tools 2. Places to intervene	Workshop (4 hr)
Week 03	1. Group Project Work	Workshop (4 hr)
Week 03	1. Design and its consequences.	Workshop (4 hr)
Week 04	1. Behavioural economics.	Workshop (4 hr)
Week 04	1. Social networks; 2. Teams as systems	Workshop (4 hr)
Week 05	1. Computer simulation and experiments	Workshop (4 hr)
Week 07	1. Wrap-up 2. Project videos on complex systems interventions	Workshop (4 hr)

Attendance and class requirements

Lecture recordings and attendance: Note that MBA classes held at the CBD Campus are not systematically recorded and 100% class attendance is expected for each unit of the MBA Program. If there are extenuating circumstances as to why you are not able to attend a particular class, please contact your unit coordinator as soon as possible, and also notify your group members (if the unit has a group work component). A unit requirement is 80% attendance, and those who drop below this level may not pass the unit.

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.

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. demonstrate a multi-level perspective to complex issues
LO2. understand key concepts of complex systems theory and their relevance in addressing complex problems
LO3. use suitable tools to understand health and related social and economic systems
LO4. integrate the expertise of multidisciplinary teams to identify novel solutions
LO5. apply theoretical concepts of complex systems thinking to a range of practical challenges.

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.

Additional justification for ST have been provided. Specific cases studies using ST were requested and have been included.

Additional information

Business School Policies

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

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

FMBA5004: Nature of Systems

Intensive October - November, 2025 [Block mode] - Castlereagh St, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links