PMGT5886: Semester 2, 2022

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Units PMGT5886 Semester 2 2022 [Normal evening]

Unit outline_

PMGT5886: System Dynamics Modelling for PM

Semester 2, 2022 [Normal evening] - Camperdown/Darlington, Sydney

Overview

Students should achieve an understanding of dynamical systems methods applied to complex adaptive systems (CAS). CAS is a new approach to engineering and management that studies and models how relationships between parts give rise to collective and dynamic system-level behaviours, for example, in communication and transport networks, megaprojects, social and eco-systems. Effectively implemented, the methods can dramatically improve a manager's effectiveness in today's complex and interconnected business world, by helping to predict and evaluate indirect effects of actions and policies. This course provides managers with many practical quantitative tools to enhance individual, team, and organisational learning, change, and performance.

Unit details and rules

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

Teaching staff

Coordinator	Mikhail Prokopenko, mikhail.prokopenko@sydney.edu.au
Lecturer(s)	Sheryl Chang, sheryl.chang@sydney.edu.au
Lecturer(s)	Mikhail Prokopenko, mikhail.prokopenko@sydney.edu.au
Tutor(s)	Sheryl Chang, sheryl.chang@sydney.edu.au

Type	Description	Weight	Due	Length
Assignment	Individual Assignment An individual project: a model in Vensim, a report (3000 words maximum)	50%	Week 07 Due date: 18 Sep 2022 at 23:59	N/a
Outcomes assessed:
Assignment	Group report A group project: a model in Vensim, a report (3000 words maximum)	30%	Week 12	N/A
Outcomes assessed:
Presentation	Group presentation A group project: a presentation of the model developed in Group Report	20%	Week 12	N/A
Outcomes assessed:
= group assignment ?

Assessment summary

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.

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.

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
Multiple weeks	Independent study guided by the online content and lectures (Week 1-12). You are expected to undertake 8-10 hours per week of independent study in addition to the workshops.	Independent study (96 hr)
Week 01	Introduction and Background	Workshop (2 hr)
Week 02	System dynamics: Modelling challenges and Vensim software	Workshop (2 hr)
Week 03	Causal loops and stock-flows diagrams	Workshop (2 hr)
Week 04	Modelling for project management	Workshop (2 hr)
Week 05	System Dynamics of Decision-making	Workshop (2 hr)
Week 06	Guest Lecture (Measuring the impacts of disaster)	Workshop (2 hr)
Week 07	Game-theoretic modelling and PM	Workshop (2 hr)
Week 08	System dynamics modelling case study	Workshop (2 hr)
Week 09	Assignment 1 review; case studies	Workshop (2 hr)
Week 10	Complex dynamical systems: Predator-Prey model	Workshop (2 hr)
Week 11	Advanced Predator-Prey model, Overview and Q & A	Workshop (2 hr)
Week 12	Group Presentations	Workshop (2 hr)

Attendance and class requirements

Workshops: Attendance is compulsory and your participation will contribute to the course assessment.
Independant study: It is expected that students will spend an average of 8 hours on additional work per week of the semester. This time will include course readings (textbook and articles), researching and writing assignments, learning software tools, and reviewing workshop materials. In periods where assignments need to be prepared the required workload may be greater than average.
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.

Text-matching software for Assignment Submission:
As part of the assessment process, text matching software such as Turnitin will be used to identify plagiarism and/or be used for providing feedback.

Confidential Peer Evaluation:
As part of the group contribution assessment process, collaborative & self-peer evaluation tools (e.g. SparkPlus, CATME, etc.) may be used, either on a confidential or non-confidential basis, to understand contributions and interactions amongst group members. Marks may be adjusted for an individual team member, following on from the peer evaluation process.

Mark Moderation:
Mark moderation: 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.

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. recognise complex scenarios
LO2. understand the differences between project management style, situations, and context
LO3. build the bridge between system theory and project management
LO4. analyse the impact of different scenarios and how they are impacted by the stakeholders decisions
LO5. map the the skills needed in a complex project and identify possible gaps
LO6. gather and reflect on lessons learnt at the end of the group and individual assignment

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.

Removed 10% participation score Increased Assignment 1 score from 40% to 50%

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

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

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