Postgraduate Course: Stochastic Optimization (CMSE11500)
Course Outline
| School | Business School |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | Stochastic Optimization provides an introduction to state-of-the-art quantitative modelling and solution methods for problems of decision-making under uncertainty.
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| Course description |
Academic Description
Stochastic Optimization provides an introduction to state-of-the-art quantitative modelling and solution methods for problems of decision-making under uncertainty.
Stochastic Optimization is structured into five two-hour lectures and three one-hour tutorials. The last two-hour lecture is delivered in flipped-classroom format: it is a presentation session during which students will present to the class the outcome of a group assignment.
Stochastic Optimization is structured into four two-hour lectures and a two-hour presentation session during which students will present to the class the outcome of a group assignment.
Outline Content:
-Decision Analysis and Decision Trees; these are simple and yet effective tools for analysing problems of decision making under uncertainty.
-Introduction to Stochastic Dynamic Programming, a modelling and solution framework originally introduced in Bellman's seminal work.
-Foundations and properties of Markov Chains, a modelling tools for modelling stochastic systems featuring the so-called Markov property, i.e. the property that event probabilities at a given time only depend on the state of the system under scrutiny at that point in time
-Foundations and applications of Markov Decision Problems, a modelling and solution framework for problems of decision making under uncertainty featuring the Markov property.
Student Learning Experience
Lectures, tutorials, and group presentations.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
| Not being delivered |
Learning Outcomes
On completion of this course, the student will be able to:
- Operationalise Decision Analysis, Stochastic Dynamic Programming, Markov Chains & Markov Decision Processes to model and solve problems of decision making under uncertainty.
- Critically appraise suitability of a technique among those listed in LO1 to model and solve problems a given problem of decision making under uncertainty; assess underpinning ethical implications.
- Present a critical review of a study from the academic literature on decision making under uncertainty to a business audience.
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Reading List
Indicative Reading List:
Hillier & Lieberman, Introduction to Operations Research (7th Edition), McGraw-Hill, 2001
W. L. Winston, Operations Research: Applications and Algorithms (7th Edition), Duxbury Press, 2003
Gallager, Stochastic processes: theory for applications, book working draft, Ch 4
http://www.rle.mit.edu/rgallager/documents/6.262vbo4.pdf
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Additional Information
| Graduate Attributes and Skills |
Knowledge and Understanding
1. Describe the structure of a Decision Table and of a Decision Tree under a number of probability-independent and probability-dependent decision criteria
2. Describe the constituent elements of a Stochastic Dynamic Program
3. Describe the structure of a Markov Chain and of a Markov Decision Problem and the underpinning assumptions of these modelling frameworks
Practice: applied knowledge, skills and understanding:
1. Utilise Decision Tables/Trees, Stochastic Dynamic Programs and Markov Decision Problems to solve small problems of decision making under uncertainty
2. Identify the most appropriate tool/technique among those presented for modelling a specific problem of decision making under uncertainty
Communication, ICT and numeracy skills
1. Demonstrate the ability to read, understand and summarise the content of an article in the academic literature on decision making under uncertainty
2. Demonstrate the ability to operationalise probability theory to model and solve problems of decision making under uncertainty
Generic Cognitive Skills
1. Demonstrate the ability to summarise the content of a document in presentation format, e.g. PowerPoint slides.
2. Demonstrate presentation skills.
3. Demonstrate problem analysis and problem-solving skills
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| Keywords | Not entered |
Contacts
| Course organiser | Dr Roberto Rossi
Tel: (0131 6)51 5239
Email: |
Course secretary | |
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