CC 501: Decision Analysis: Business Analytics II

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.

Learning outcomes
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/consultants in the field.
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/“solvers” (MS Excel). In the context of decision-making under uncertainty, students will learn how to deal with risk. Moreover, they will be aware of well-known behavioral findings that human intuition often conflicts with popular prescriptive models.

Necessary prerequisites

Recommended prerequisites
The lecture generally assumes basic knowledge of mathematics and statistics (high school graduation level).

Forms of teaching and learningContact hoursIndependent study time
Lecture2 SWS6 SWS
Exercise class2 SWS7 SWS
ECTS credits6
Graded yes
Form of assessmentWritten exam (90 min)
Restricted admissionno
Further information
Performing lecturer
Prof. Martin Glanzer, Ph.D.
Prof. Dr. Martin Glanzer
Frequency of offeringSpring semester & fall semester
Duration of module 1 semester
Range of applicationM.Sc. MMM, M.Sc. WiPäd, M.Sc. Wirt. Math.
Preliminary course work
Program-specific Competency GoalsCG 1
LiteratureEisenführ, 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.
Course outlineIntroduction
Multi-Attribute Value Theory
Decision Making in a Deterministic Setting
Decision Making Under Uncertainty
Measuring, Modeling, Managing Risk
Prospect theory