INTOP4006 Applied Risk Analysis
- Number of credits10
- Teaching semester2023 Autumn
- Language of instructionEnglish
- CampusLillehammer
- Required prerequisite knowledge
Students are recommended to have a basic understanding of numerical/statistical methods, data analysis, operational research, and programming.
The PhD course “Applied risk analysis” provides a comprehensive overview of advanced operational research methods on how to deal with risk.
Learning Outcome
- Be able to identify what opportunities various companies/firms might have to apply quantitative risk analysis for decision support
- Understand the critical differences (strengths and weaknesses) among different types of model approaches
- Understand and be able to explain key concepts such as; (1) risk, (2) risk aversion, (3) risk efficiency, (4) stochastic dominance, and (5) subjective and objective probabilities
- Formulate decision problems within companies/firms
- Model real decision problems using appropriate software
- Implement the obtained results for (imaginary/real) companies/firms
- Be able to critically evaluate the economic significance of the results of different models
- Be able to propose risk analysis models and implement these, using the appropriate software, in companies/firms
The course is mainly based on self-study, but the introduction of the course will be the course BUS230 Operations Research at NMBU. Important parts of the course will be to get an intuitive understanding of the different models and practical exercises how they can be implemented in applied work. Additionally, the students will be trained in writing academic papers.
The introduction of the course will be the course BUS230 Operations Research at NMBU.
The paper in the course will be written under supervision of and in cooperation with the course responsible.
The course will include following sections/parts
- Decision analysis: outline and basic assumptions
- Probabilities for decision analysis
- Attitudes to risky consequences
- Integrating beliefs and preferences for decision analysis
- Decision analysis with preferences unknown
- Linear programming
- Risk programming
- Stochastic programming
- Decision analysis with multiple objectives
- Strategies decision makers can use to manage risk
- Risk considerations in policy making
All sections/parts will be covered, with main focus on programming (part 6, 7 and 8).
In order to take the exam, the students must write a scientific paper within the topics of the course. It is expected that the paper is of high quality and “almost” publishable (in a scientific journal with referee). This paper will also be part of the foundation for the oral exam.
At the end of the course, there will be an oral exam with an external examiner. This oral exam and the paper count each 50% of the grade. Grade: Passed/Not Passed.
| Form of assessment | Grading scale | Grouping | Duration of assessment | Support materials | Proportion | Comment |
|---|---|---|---|---|---|---|
Written assignment | Passed - not passed | 50% | ||||
Oral examination | Passed - not passed | 50% |