Outline

Overview

This introductory unit on decision analysis addresses the formal methods of decision making. These methods include measuring risk by subjective probabilities; growing decision trees; performing sensitivity analysis; using theoretical probability distributions; simulation of uncertain events; modelling risk attitudes; estimating the value of information; and combining quantitative and qualitative considerations. The primary goal of the unit is to demonstrate how to build models of real business situations that allow the decision maker to better understand the structure of decisions and to automate the decision process by using computer decision tools.

Unit details and rules

Academic unit	Business Analytics
Credit points	6
Prerequisites ?	BUSS1020 or DATA1001 or ECMT1010 or ENVX1001 or ENVX1002 or STAT1021 or ((MATH1005 or MATH1015) and MATH1115) or 6 credit points of MATH units which must include MATH1905
Corequisites ?	None
Prohibitions ?	QBUS2320 or ECMT2630 or ENGG1850 or CIVL3805
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Simon Loria, simon.loria@sydney.edu.au

Assessment

The census date for this unit availability is 31 August 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam	Final exam Closed book supervised pen and paper exam	45%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Contribution	Tutorial Engagement Submit tutorial pre work or in class modules and attend class.	10%	Multiple weeks	Not Applicable	AI allowed
Outcomes assessed:
Data analysis	Assignment 1 Decision tree analysis and reporting,with discussion on assumptions, methods and business insights.	10%	Week 06 Due date: 11 Sep 2026 at 23:59 Closing date: 23 Sep 2026	1000 word report with discussion	AI allowed
Outcomes assessed:
Written test	Mid-semester test Closed book, supervised, pen and paper test	20%	Week 08 Due date: 21 Sep 2026 at 09:10	1.5 hours	AI prohibited
Outcomes assessed:
Data analysis	Assignment 2 Monte Carlo simulation modelling and reporting with discussion of inputs, outputs and business insights.	15%	Week 12 Due date: 30 Oct 2026 at 23:59 Closing date: 11 Nov 2026	1,200 word report with discussion.	AI allowed
Outcomes assessed:

Assessment summary

Tutorial engagement: For at least 10 of the 13 tutorials submit to Canvas the assigned pre-work prior to class or in-class question/s during class AND attend class. Marks are distributed equally for both requirements.
Assignment 1: Based on a number of different business scenarios, this assignment requires students to analyse the data provided, build appropriate models using the unit software “Precision Tree”, and then report on and discuss the modelling process, outcomes and any meaningful business inferences.
Assignment 2: Focusses on building a simulation model for a business start-up, using the unit software “@Risk”. Students will be expected to report on and discuss model assumptions, inputs, outputs and business implications.
Mid-semester exam: This is a closed book exam covering concepts from weeks 1-6 of the unit. Questions will be short answer or extended response and may require students to build and interpret decision trees and undertake numerical analysis. Students will be expected to draw out of any analysis relevant business implications for decision makers.
Final exam: This is a closed book exam and can assess a student's understanding of any aspect of the unit from weeks 1-13. Questions could be short answer or extended response and will require students to build or interpret decision trees or simulation models as well as undertake numerical analysis. Students will be expected to draw out of any analysis relevant business implications for decision makers.

More detailed information for each assessment will be provided during the semester and shared 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 sydney.edu.au/students/guide-to-grades.

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.

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 assignments submitted after the due date will be penalised at the rate of 5% per day up to 50% in total as per Business School Guidelines.

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	Introduction to decision analysis	Lecture (2 hr)
Week 01	Introduction to decision analysis	Tutorial (1 hr)
Week 02	Decision trees and their application	Lecture (2 hr)
Week 02	Decision trees and their application	Tutorial (1 hr)
Week 03	Risk profiles and stochastic dominance	Lecture (2 hr)
Week 03	Risk profiles and stochastic dominance	Tutorial (1 hr)
Week 04	Decision probabilities and Bayes theorem	Lecture (2 hr)
Week 04	Decision probabilities and Bayes theorem	Tutorial (1 hr)
Week 05	Value of information	Lecture (2 hr)
Week 05	Value of information	Tutorial (1 hr)
Week 06	Theoretical probability models	Lecture (2 hr)
Week 06	Theoretical probability models	Tutorial (1 hr)
Week 07	Monte Carlo simulation	Lecture (2 hr)
Week 07	Monte Carlo simulation	Tutorial (1 hr)
Week 08	Mid Semester Exam	Lecture (2 hr)
Week 08	Mid Semester Exam	Tutorial (1 hr)
Week 09	Monte Carlo simulation extension	Lecture (2 hr)
Week 09	Monte Carlo simulation extension	Tutorial (1 hr)
Week 10	Utility theory and risk attitudes	Lecture (2 hr)
Week 10	Utility theory and risk attitudes	Tutorial (1 hr)
Week 11	Utility Theory and decision trees	Lecture (2 hr)
Week 11	Utility Theory and decision trees	Tutorial (1 hr)
Week 12	Prospect theory, biases and heuristics	Lecture (2 hr)
Week 12	Prospect theory, biases and heuristics	Tutorial (1 hr)
Week 13	Unit and Final Exam Review	Lecture (2 hr)
Week 13	Unit Final Exam Review	Tutorial (1 hr)

Attendance and class requirements

Lecture recordings: All lectures are recorded and will be available on Canvas for student use. 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.

Tutorial attendance: Tutorials are not recorded and attendance for at least 10 out of 13 tutorials is required.

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.

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

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. develop the ability to identify the values, objectives, attributes, decisions, uncertainties, consequences, and trade-offs in a real decision problem
LO3. apply the concepts learned in this unit (expected value, value of information, risk aversion, and tradeoffs between attributes) to identify good decisions and strategies
LO4. demonstrate the ability to represent a decision problem graphically and/or mathematically
LO5. develop the skills to determine the optimal decision mathematically
LO6. cultivate the aptitude for identifying which parameters have the most impact on the results of an analysis
LO7. Develop the expertise of explaining the results of decision analysis to managers and other non-specialists.

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.

To help obtain better learning outcomes from individual assignments, students will be required to report on and discuss in detail the modelling processes, outcomes and derived business inferences.

Additional information

Public Holiday arrangements: The week 9 lecture and tutorials are on Monday 5th October, which is a public holiday. They will be rescheduled for later in the same week or in week 8 depending on timetabling constraints at the time. Students will be advised as soon as timetabling changes are confirmed.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

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

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

QBUS3330: Methods of Decision Analysis

Semester 2, 2026 [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)

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