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2019/20 Taught Postgraduate Module Catalogue

LUBS5962M Effective Decision Making for Management

15 creditsClass Size: 200

Module manager: Nicola Bown
Email: n.j.bown@leeds.ac.uk

Taught: 1 Nov to 31 Dec View Timetable

Year running 2019/20

This module is not approved as an Elective

Objectives

This module aims to teach effective management decision making on the basis of evidence-based approaches from risk management, cognitive psychology, and behavioural economics. Participants will obtain an understanding of the thinking processes that underlie their own and others’ judgement and decision making, the judgmental errors and decision biases that commonly arise, and strategies for improving decisions.

Learning outcomes
Upon completion of this module students will be able to critically evaluate:
- different types of decisions and the theories used to explain how they are and should be made
- the thinking processes which underlie participants own and others’ judgement and decision making, including the distinction between System 1 and System 2 thinking
- different types of errors / biases in human judgement/decision making and how these can be overcome
- the influence of motivation, stress and emotion on judgement and decision making

Skills outcomes
Improved decision making skills


Syllabus

Indicative Content:
The normative theory of decision making, which focuses on maximisation of subjective expected utility (SEU) is presented as a starting point, and its validity as a description of how people make decisions is considered.

We then consider the impact of peoples’ limited processing capacity on the way they make decisions, leading to a discussion of two kinds of thinking (quick intuitive System 1 thinking and slow analytical System 2 thinking), what each is, and the implications of using each when making judgements and decisions

Prospect Theory is introduced as a theory which can explain some anomalies in human decision making, such as framing (where changing the description of a problem affects the decisions people make, even though the facts of the problem are unchanged). This leads into a discussion of mental accounting (how people categorise and evaluate financial activities), its influence on individuals’ financial decisions and its link to behavioural finance. We then consider situations where people are poor judges when gathering and interpreting decision information about risk and uncertainty, and the ways in which System 1 thinking can lead to biases in judgement and decision making.

Although Prospect Theory is based around putting a value on each potential choice the decision maker might make, and then comparing these, there are other theories of judgement and decision making which look at other bases for decision making and thus give fresh insights. We cover two of these; naturalistic decision making (NDM) which considers the way in which expertise can support decision making in dynamic environments. We then consider the role of motivational biases and the effects of emotion, stress and time pressure on judgement and decision making.

Teaching methods

Delivery typeNumberLength hoursStudent hours
Workshop52.0010.00
Lecture51.005.00
Lecture51.507.50
Private study hours127.50
Total Contact hours22.50
Total hours (100hr per 10 credits)150.00

Private study

Reading: 80 hours
Assessment prep: 47.5 hours
On non-teaching days you are expected to complete reading and assessment prep for this module.

Opportunities for Formative Feedback

Each block day will include a non-assessed pop quiz (via Kahoot or such like). These are usually 15-20 MQC in class quizzes with the opportunity to retry for revision purposes.
Students will have the opportunity to complete a practice timed essay during class time (formative feedback will a comprise a sample of anonymised essays)

Methods of assessment


Exams
Exam typeExam duration% of formal assessment
Standard exam (closed essays, MCQs etc) (S1)2 hr 100.00
Total percentage (Assessment Exams)100.00

Resit is by 2 hour exam for 100% of the module mark

Reading list

The reading list is available from the Library website

Last updated: 30/04/2019

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