Simulation-Based Decision Theory

Students acquire the competence to classify complex decision-making situations and to derive normative decision recommendations using appropriate optimization concepts.

The module first covers the fundamentals of decision theory (in particular decision-making under risk and under incomplete information) as well as the basics of stochastic simulation. In the second part of the course, core topics of appropriate modeling—especially of so-called subjective probabilities—are the focus. The holistic assessment of risk situations enabled by this approach, as opposed to a purely scenario-based perspective, is implemented through a wide range of software-supported practical applications.