Outline

Overview

This unit introduces models and tools for decision analysis and their application in managerial settings. The unit focuses on the use of formal decision methods for management decisions in business. The main goal is to show how these decision models can improve the decision process by helping the decision maker to understand the structure of decisions; use subjective probabilities for measuring risk; analyse the sensitivity of decisions to changing decision parameters; quantify outcomes in accordance with risk attitudes, and estimate the value of information. Special attention is paid to informal interpretations of formal decision approaches.

Unit details and rules

Academic unit	Business Analytics
Credit points	6
Prerequisites ?	QBUS5001 or QBUS5002
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Basic Algebra, Probability, and Statistics
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Simon Loria, simon.loria@sydney.edu.au
Lecturer(s)	Simon Loria, simon.loria@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2025

Details
Criteria

Type	Description	Weight	Due	Length
Supervised exam ?	Final exam Written exam	45%	Formal exam period	2 hours
Outcomes assessed:
Participation	Tutorial Engagement Submit tutorial pre-work and attend tutorials.	10%	Ongoing	N/A
Outcomes assessed:
Assignment	Individual Assignment 1 Report based on modelling and data analysis using Precision Tree software.	10%	Week 06 Due date: 04 Apr 2025 at 23:59 Closing date: 13 Apr 2025	1200 words
Outcomes assessed:
Supervised test ?	Mid-semester exam Covering weeks 1 - 6	20%	Week 08 Due date: 13 Apr 2025 at 09:40	1.5 hours
Outcomes assessed:
Assignment	Individual Assignment 2 Report based on modelling and data analysis using @Risk software.	15%	Week 12 Due date: 23 May 2025 at 23:59 Closing date: 01 Jun 2025	1500 words
Outcomes assessed:
= AI allowed ?

Assessment summary

Tutorial engagement: For at least 11 of the 13 tutorials submit the tutorial pre-work question and attend tutorials. Special consideration is "absense noted".
Mid-semester exam: This assessment will be completed during the week 8 lecture time slot. It is closed book exam that will examine all course content from weeks 1-6 inclusive. The questions will assess student knowledge of the major quantative and qualitative principles in decision making as well as their ability to complete standard analytical tasks related to decision making.
Assignments: The assignments will require using the methods and models discussed in lectures to solve decision-making problems that arise in the business world. Students should demonstrate sufficient understanding of the theoretical principles in this unit, including data collection, model selection, design and application, as well as an ability to draw meaningful inferences based on the data and model output. The assignments will involve analysis of data using computer tools, as well as drawing on more theoretical material from lectures.
Final exam: The final exam is closed book and will assess all course material from weeks 1-13. The questions will assess student knowledge of the major quantative and qualitative principles in decision making as well as their ability to complete standard analytical tasks related to decision making.

More 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) and automated writing tools

Except for supervised exams or in-semester tests, you may use generative AI and automated writing tools in assessments unless expressly prohibited by your unit coordinator.

For exams and in-semester tests, the use of AI and automated writing tools is not allowed unless expressly permitted in the assessment instructions.

The icons in the assessment table above indicate whether AI is allowed – whether full AI, or only some AI (the latter is referred to as “AI restricted”). If no icon is shown, AI use is not permitted at all for the task. Refer to Canvas for full instructions on assessment tasks for this unit.

Your final submission must be your own, original work. You must acknowledge any use of automated writing tools or generative AI, and any material generated that you include in your final submission must be properly referenced. You may be required to submit generative AI inputs and outputs that you used during your assessment process, or drafts of your original work. Inappropriate use of generative AI is considered a breach of the Academic Integrity Policy and penalties may apply.

The Current Students website provides information on artificial intelligence in assessments. For help on how to correctly acknowledge the use of AI, please refer to the  AI in Education Canvas site.

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:

5% per day up to a maximum of 10 days. No marks awarded after 10 days

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.

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	Introduction to decision analysis	Lecture and tutorial (3 hr)
Week 02	Decision trees and expected monetary value	Lecture and tutorial (3 hr)
Week 03	Risk and stochastic dominance	Lecture and tutorial (3 hr)
Week 04	Conditional probabilities and Bayes Theorem	Lecture and tutorial (3 hr)
Week 05	The value of information	Lecture and tutorial (3 hr)
Week 06	Theoretical probability models	Lecture and tutorial (3 hr)
Week 07	Monte Carlo simulation	Lecture and tutorial (3 hr)
Week 08	Mid-semester exam week	Individual study (3 hr)
Week 09	Monte Carlo simulation 2	Lecture and tutorial (3 hr)
Week 10	Utility theory	Lecture and tutorial (3 hr)
Week 11	Utility theory, decision trees and the value of information	Lecture and tutorial (3 hr)
Week 12	Prospect Theory	Lecture and tutorial (3 hr)
Week 13	AHP and revision	Lecture and tutorial (3 hr)

Attendance and class requirements

Lecture recordings: All lectures will be presented in person and recorded on Echo. Recording will be available on Canvas. Please note the Business School does not own the system and cannot guarantee that the system will operate or that every class will be recorded. Students should ensure they attend and participate in all classes.

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

Making Hard Decisions, Clemen and Reilly, South-Western, Cengage Learning (3rd Edition).

All readings for this unit can be accessed from the Library or via the link available on Canvas.

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 the types of problems that decision analysis can and can’t address.
LO2. Identify the values, objectives, attributes, decision, uncertainties, consequences, and trade-offs in a real decision problem.
LO3. Apply concepts like expected value, value of information and risk aversion to identify good decisions and strategies.
LO4. Graphically and mathematically represent and solve decision problems to determine optimal decisions.
LO5. Communicate decision analysis results to managers and other non-specialists.
LO6. Develop competency in the use of Lumivero Decision Suite software.

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.

Based on student feedback from other courses introduction a small but meaningful tutorial participation and engagement mark has helped students engage with the course material and software from the very beginning of the semester and given them greater confidence when completing assessments.

Additional information

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) and automated writing tools

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

Required readings

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

QBUS6320: Management Decision Making

Semester 1, 2025 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

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

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

Required readings

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