Decisions under uncertainty

• Analyse a specific decision problem to point out uncertainties and understand what effect these uncertainties can have on the decision.
• Describe the procedure in each of the presented methods for decision problems characterised by uncertain information.
• Explain strengths and weaknesses for the different methods.
• Relate the different methods to each other; including listing common features and differences.
• Argue for when each of the presented methods should be applied.
• Explain how the different methods can complement one another to gain further insight.
• Apply each of the presented methods on a specific decision problem characterised by uncertain information.
• Deduce recommendation based on the application of the presented methods for decision problems characterised by uncertain information.

Students will be introduced to different quantitative methods for decision problems characterised by uncertain information. These methods include Markov Theory, simulation and optimisation under uncertainty. The methods will be illustrated through different cases.

02105/02450, Algorithms and datastructures (02105). A pre-requisite to the course is basic programming skills, preferably in Julia.

Lectures, exercises, projects