Undergraduate Course: Decision-Making under Uncertainty (BUST10013)

Course Outline
School	Business School	College	College of Humanities and Social Science
Credit level (Normal year taken)	SCQF Level 10 (Year 3 Undergraduate)	Availability	Available to all students
SCQF Credits	20	ECTS Credits	10
Summary	Methods for decision making under uncertainty: topics from stochastic programming, probabilistic dynamic programming, Markov processes and decision theory, with applications.
Course description	The course covers four modelling techniques: Markov Chains, Markov Decision Processes, Decision Analysis and Sequential Sampling.

Entry Requirements (not applicable to Visiting Students)
Pre-requisites	Students MUST have passed:	Co-requisites
Prohibited Combinations		Other requirements	Pre-requisite: Business Studies Honours entry. Note: For Economics with Management Science, and Mathematics and Business Studies programmes EITHER Mathematical Programming (BUST10011) OR Decision Making Under Uncertainty is a mandatory course in Year 4.

Information for Visiting Students
Pre-requisites	Visiting students should have at least 3 Business Studies courses at grade B or above (or be predicted to obtain this). We will only consider University/College level courses.

Course Delivery Information

Academic year 2014/15, Available to all students (SV1)		Quota: None
Course Start	Semester 1
Timetable	Timetable
Learning and Teaching activities (Further Info)	Total Hours: 200 ( Lecture Hours 20, Summative Assessment Hours 2, Revision Session Hours 2, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 172 )
Assessment (Further Info)	Written Exam 70 %, Coursework 30 %, Practical Exam 0 %
Additional Information (Assessment)	One project on Markov decision processes 30%; final degree exam 70% Visiting Student Variant Assessment One project on Markov decision processes 50% and one essay (min 3,000 words) 50%.
Feedback	Not entered
Exam Information
Exam Diet	Paper Name		Hours & Minutes
Main Exam Diet S2 (April/May)			2:00

Academic year 2014/15, Part-year visiting students only (VV1)		Quota: None
Course Start	Semester 1
Timetable	Timetable
Learning and Teaching activities (Further Info)	Total Hours: 200 ( Lecture Hours 20, Revision Session Hours 2, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 174 )
Assessment (Further Info)	Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment)	One project on Markov decision processes 30%; final degree exam 70% Visiting Student Variant Assessment One project on Markov decision processes 50% and one essay (min 3,000 words) 50%.
Feedback	Not entered
No Exam Information

Learning Outcomes
Objectives/Learning Outcomes Knowledge & Understanding On completion of the course students should: a) be able to discuss critically the practical use of the techniques covered; b) be able to use the modelling techniques covered to structure management problems; c) be able to solve models built using the techniques covered; d) be able to make inferences about a management problem based on the solution of a model built using the techniques covered. Cognitive Skills On completion of the course students should: a) demonstrate that they can identify which of the techniques covered is most suitable for a management problem; b) demonstrate that they can discuss the results of their analysis of a management problem in written reports. Key Skills On completion of the course students should: a) demonstrate that they can build and analyse a model of a real world management problem involving uncertainty; b) demonstrate their ability to apply their computer skills to support the analysis of a management problem involving uncertainty; c) demonstrate that they can present the findings of a quantitative analysis in a concise written report. Subject Specific Skills On completion of the course students should: a) have developed their modelling skills.

Reading List
Recommended: 1. F S Hillier & G J Lieberman, Introduction to Operations Research (McGraw-Hill). 2. W L Winston, Operations Research: Applications and Algorithms (Duxbury).

Additional Information
Graduate Attributes and Skills	Not entered
Additional Class Delivery Information	There will be 4 optional review tutorials. To accommodate students' schedules, students will sign up for either the Thursday or Friday series of DMU tutorials: Thursdays 15.10-16.00 on 6 November(Week 8), 13 November(Week 9), 20 November (Week 10), 27 November (Week 11) venue tba. Fridays 12.10-13.00pm on 7 November(Week 8), 14 November (Week 9), 21 November (Week 10), 28 November (Week 11) venue tba.
Keywords	DMU, decision analysis, Markov processes, probabilistic dynamic programming

Contacts
Course organiser	Dr Tom Archibald Tel: (0131 6)50 4604 Email: T.Archibald@ed.ac.uk	Course secretary	Ms Patricia Ward-Scaltsas Tel: (0131 6)50 3823 Email: Patricia.Ward-Scaltsas@ed.ac.uk