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MIS30010

Academic Year 2024/2025

Decision Analytics (MIS30010)

Subject:
Management Information Systems
College:
Business
School:
Business
Level:
3 (Degree)
Credits:
5
Module Coordinator:
Dr Clare Branigan
Trimester:
Spring
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

Much of business decision-making has a common base, for example dealing with risk or identifying optimal levels of service to provide. Decision Analytics helps to develop an appreciation of these common structures. This becomes most helpful later in one's career as one moves into general management when it becomes important to have an ability to get a quick understanding of the dynamics of many different and apparently unstructured situations.
The emphasis is on developing practical experience in solving business analytics problems by means of real decisions. We include techniques such as Expected Value of Perfect information, Markov Processes and Multi-Criteria Decision Making.
The course also includes elements of “Behavioural Decision Making” which is an academic discipline which seeks to apply the insights of psychologists to our decision making. The course readings provide many examples of how decision making is not always driven by “rational” consideration but by aspects of personal and market psychology. This module will provide us with the ground work to recognise our biases and errors of judgements, and make better decisions in the future.




About this Module

Learning Outcomes:

On completing this module students will be expected to be able to(i) work in groups as decision consultants helping other students make a real life decisions (ii) use their judgement about how to mix theory and action learning, using a quantitative focus (iii) explain how decision structures can improve decision making(iv) Understand how, by appreciating the cognitive biases to which you are prone, you can become a better decision-maker

Indicative Module Content:

Heuristics and Biases
Paradox of Choice
Bubble Markets, Herding, Group Think
Loss Aversion, Regret
Prospect Theory
Bayes Theorem
Markov decision process
Multi-criteria Decision Processes

Student Effort Hours:
Student Effort Type Hours
Specified Learning Activities

56
Autonomous Student Learning

28
Lectures

24
Practical

12
Total

120

Approaches to Teaching and Learning:
Research-led. Curriculum is structured around teaching subject content.

Students are expected to engage in Group Presentations using Role Play. Students are also expected to provide fellow students with feedback on their Presentations.

Requirements, Exclusions and Recommendations
Learning Recommendations:

This course is for the person who likes to use a mathematical approach to problem solving. Students should have reached the level of first year mathematics. It is very different from linear programming and does not require one to have done it. It focuses on how people make decisions and on the mathematical functions that are used to support people's choices.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy Invalid Option
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Exam (In-person): In class test Week 7 Graded No
45
 No
Group Work Assignment: In Class Presentation with your class group. These will be held over the class term. Week 2, Week 6, Week 8, Week 10, Week 11 Graded No
15
 No
Assignment(Including Essay): Reflective Journal of your class experiences Week 12 Graded No
40
 No

Carry forward of passed components Invalid Option
Yes
 

Remediation Type Remediation Timing
In-Module Resit Prior to relevant Programme Exam Board
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Feedback for the in class tests will be available to students within 2 week of test completion, Students can get feedback face-to-face directly after class or tutorials. Feedback for Group Presentations will be given directly after the Presentation.

“Thinking, Fast and Slow”, Daniel Kahneman, 2012

“Nudge: Improving Decisions About Health, Wealth, and Happiness,” Richard H. Thaler, Cass R. Sunstein, 2009

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Spring Lecture Offering 1 Week(s) - 20, 21, 23, 24, 25, 26, 29, 31, 32, 33 Mon 15:00 - 17:50
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