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.

Details

Academic unit	Business Analytics
Unit code	QBUS6320
Unit name	Management Decision Making
Session, year ?	Semester 1, 2020
Attendance mode	Normal day
Location	Camperdown/Darlington, Sydney
Credit points	6

Enrolment rules

Prohibitions ?	None
Prerequisites ?	QBUS5001 or QBUS5002
Corequisites ?	None
Assumed knowledge ?	Basic Algebra, Probability, and Statistics
Available to study abroad and exchange students	Yes

Teaching staff and contact details

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

Type	Description	Weight	Due	Length
Final exam	Final exam Written exam	50%	Formal exam period	2 hours
Outcomes assessed:
Online task	In class task 1 Making a Decision and uploading answer to Canvas using Turnitin	0%	Week 04 Due date: 30 Mar 2020 at 16:45 Closing date: 30 Mar 2020	15 minutes
Outcomes assessed:
Online task	In class task 2 Drawing a decision tree and uploading answer to Canvas using Turnitin	2.5%	Week 05 Due date: 06 Apr 2020 at 16:45 Closing date: 06 Apr 2020	15 minutes
Outcomes assessed:
Online task	In class task 3 Determining Dominance and uploading answer to Canvas using Turnitin	2.5%	Week 06 Due date: 13 Apr 2020 at 16:45 Closing date: 13 Apr 2020	15 minutes
Outcomes assessed:
In-semester test	Mid-semester exam n/a	25%	Week 07 Due date: 20 Apr 2020 at 15:00	90 minutes + 15 minutes upload
Outcomes assessed:
Assignment	Assignment Data analysis and modelling	20%	Week 12 Due date: 24 May 2020 at 23:59 Closing date: 03 Jun 2020	n/a
Outcomes assessed:

In class tasks: These three simple tasks are designed primarily to allow students to practice the process scanning their handwritten work and uploading this work to Canvas using Turnitin in preparation for both the mid-semester and final exam, which will require students to follow the same time constrained upload process.
Mid-semester exam: This assessment will be undertaken during class time. It is an open book exam that will examine all course content from weeks 1-6 inclusive. There is no reading time but students will be given 15 minutes at the end of the exam to upload their answers. All answers are to be handwritten on paper, scanned and saved as a pdf file and then uploaded to Canvas using Turnitin. The questions will measure students’ knowledge of the major principles in decision making and their ability to provide a complete and comprehensible description of their essential characteristics, as well as their ability to complete standard analytical tasks, as discussed in weeks 1-6 of this unit.
Assignment: The assignment will involve 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 and design, application, and the 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 draw on more theoretical material from lectures.
Final exam: The final exam is open book and will assess all aspects of this unit from weeks 1-13. There will be no reading time but students will be given 15 minutes at the end of the exam to upload their answers. All answers are to be handwritten on paper, scanned and saved as a pdf file and then uploaded to Canvas using Turnitin. The exam will be marked for correctness, understanding of underlying material and clarity of exposition and presentation.

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 sydney.edu.au/students/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.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

Academic integrity

The Current Student website provides information on academic honesty, academic dishonesty, 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 dishonesty or plagiarism seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic dishonesty. If such matches indicate evidence of plagiarism or other forms of dishonesty, your teacher is required to report your work for further investigation.

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	Mid-semester exam week	Individual study (2 hr)
Week 08	Monte Carlo simulation	Lecture and tutorial (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	Introduction to Game Theory	Lecture and tutorial (3 hr)
Week 13	Revision	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

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

All readings for this unit can be accessed on the Library eReserve 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, decisions, uncertainties, consequences, and trade-offs in a real decision problem
LO3. apply concepts learned in this class (expected value, value of information, risk aversion, and tradeoffs between attributes) to identify good decisions and strategies
LO4. 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

Closing the loop

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

Additional information

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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

Details

Enrolment rules

Teaching staff and contact details

Assessment

Assessment criteria

Late submission

Special consideration

Academic integrity

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Graduate qualities

Outcome map

Closing the loop

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

QBUS6320: Management Decision Making

Overview

Details

Enrolment rules

Teaching staff and contact details

Assessment

Assessment criteria

Late submission

Special consideration

Academic integrity

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Graduate qualities

Outcome map

Closing the loop

Additional information

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect