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Unit outline_

QBUS6320: Management Decision Making

Semester 1, 2021 [Normal day] - Camperdown/Darlington, Sydney

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
Type Description Weight Due Length
Final exam (Take-home short release) Type D final exam Final exam
Written exam
50% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
Assignment Individual Assignment 1
Model construction and data analysis
10% Week 06
Due date: 16 Apr 2021 at 23:59

Closing date: 26 Apr 2021
N/A
Outcomes assessed: LO1 LO2 LO4 LO5
In-semester test (Record+) Type B in-semester exam Mid-semester exam
Covering weeks 1 - 5
30% Week 07
Due date: 19 Apr 2021 at 18:00
1.5 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
Assignment Individual Assignment 2
Data analysis and modelling
10% Week 12
Due date: 30 May 2020 at 23:59

Closing date: 10 Jun 2020
N/A
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
Type B in-semester exam = Type B in-semester exam ?
Type D final exam = Type D final exam ?

Assessment summary

  • Mid-semester exam: This assessment will be completed during the week 7 lecture time slot. It is an open book exam that will examine all course content from weeks 1-5 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 open 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.

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.

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.

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.

WK Topic Learning activity Learning outcomes
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 Prospect Theory Lecture and tutorial (3 hr)  
Week 13 Revision Lecture and tutorial (3 hr)  

Attendance and class requirements

Lecture recordings: All lectures will be presented live on Zoom 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 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.

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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

This section outlines changes made to this unit following staff and student reviews.

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

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

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