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

Type	Description	Weight	Due	Length
Final exam (Take-home short release)	Final exam Take home short release written exam	50%	Formal exam period	2 hours
Outcomes assessed:
Assignment	Assignment 1 Written assignment	10%	Week 06 Due date: 17 Sep 2021 at 23:59 Closing date: 26 Sep 2021	Up to 1000 word with associated analysis
Outcomes assessed:
In-semester test (Take-home short release)	Mid-semester exam Take home short release written exam	30%	Week 07 Due date: 22 Sep 2021 at 11:00	80 minutes
Outcomes assessed:
Assignment	Assignment 2 Simulation modelling and written report	10%	Week 12 Due date: 05 Nov 2021 at 23:59 Closing date: 14 Nov 2021	1,500 words with numerical analysis
Outcomes assessed:
= Type D final exam ? = Type D in-semester exam ?

Assessment summary

Assignment 1: This assignment will require answering a series questions based on different business scenarios. The assignment will require students to use “Precision Tree” computer software (provided), as well as draw on theoretical material from lectures. Students will be expected to analyse the data provided, build appropriate models and demonstrate the ability to draw meaningful inferences based on the data and model output.
Assignment 2: The assignment will focus on building a simulation model for a business start-up. The model will require using the simulation software “@Risk” to apply techniques learnt in the unit to the problem at hand. Students will be expected to select the correct models and then write a report providing in-depth data analysis, a discussion of the business implications of their findings and outlining suggested courses of actions for decision makers and other stakeholders.
Mid-semester exam: This is an open book exam covering concepts from weeks 1-6 of the unit. Questions could be short answer or extended response and will require students to build decision trees and undertake numerical analysis. Students will be expected to draw out of their analysis relevant business implications for decision makers. The exam can be completed with using the course softare.
Final exam: This is an open book exam and will assess all aspects of this unit from weeks 1-13. Questions could be short answer or extended response and will require students to build decision trees and other models as well as undertake numerical analysis. Students will be expected to draw out of their analysis relevant business implications for decision makers. The exam can be completed with using the course softare.

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.

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 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	Introduction to decision analysis	Lecture and tutorial (3 hr)
Week 02	Decision trees and their application	Lecture and tutorial (3 hr)
Week 03	Risk profiles and stochastic dominance	Lecture and tutorial (3 hr)
Week 04	Decision probabilities and Bayes theorem	Lecture and tutorial (3 hr)
Week 05	Value of information	Lecture and tutorial (3 hr)
Week 06	Theoretical probability models	Lecture and tutorial (3 hr)
Week 07	Mid Semester Exam	Lecture and tutorial (3 hr)
Week 08	Monte Carlo simulation	Lecture and tutorial (3 hr)
Week 09	Monte Carlo simulation extension	Lecture and tutorial (3 hr)
Week 10	Utility theory and risk attitudes	Lecture and tutorial (3 hr)
Week 11	Utility Theory and decision trees	Lecture and tutorial (3 hr)
Week 12	Prospect theory, biases and heuristics	Lecture and tutorial (3 hr)
Week 13	Course and exam review	Lecture and tutorial (3 hr)

Attendance and class requirements

Lecture recordings: All lectures and seminars 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.

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. ripen 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.

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

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

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

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

QBUS3330: Methods of Decision Analysis

Semester 2, 2021 [Normal day] - Remote

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

Additional information

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect