This course presents methods and concepts that support and improve rational decision making in various areas of application. The focus is on decision support and prescriptive analytics tools. Discussion of basic descriptive theories ensure a thorough decision-theoretical embedding.
This course aims to teach students how to use prescriptive models to make better decisions, in particular in a business context. Students will (i) develop a structural approach to think about decision problems; (ii) get equipped with a basic prescriptive analytics toolkit; and (iii) be able to confidently discuss with experts/
Upon successful completion of the course, students will have a solid basic understanding of prescriptive analytics methods. They will be able to abstract a given decision problem into a mathematical model (both in deterministic settings and under uncertainty) and compute a recommended course of action inferred from their model. Depending on the model, the last step may require the application of software/
The lecture generally assumes basic knowledge of mathematics and statistics (high school graduation level).
|Forms of teaching and learning
|Independent study time
|Form of assessment
|Written exam (90 min)
Prof. Martin Glanzer, Ph.D.
Prof. Dr. Martin Glanzer
|Frequency of offering
|Spring semester & fall semester
|Duration of module
|Range of application
|M.Sc. MMM, M.Sc. WiPäd, M.Sc. Wirt. Math.
|Preliminary course work
|Program-specific Competency Goals
|Eisenführ, Weber, Langer: Rational Decision Making, 1st Edition, 2010, Springer.
McNamee, Celona. Decision Analysis for the Professional, 2008, SmartOrg Inc.
Hillier, Liebermann. Introduction to Operations Research, 2001, McGraw-Hill.
Camm, Cochran, Fry, Ohlmann. Business Analytics, 2021, Cengage.
Multi-Attribute Value Theory
Decision Making in a Deterministic Setting
Decision Making Under Uncertainty
Measuring, Modeling, Managing Risk