CC 501: Decision Analysis: Business Analytics II

In this course, we discuss methods and concepts that support and improve rational decision making in various areas of application. We will cover decisions under certainty and risk, decisions with single and multiple objectives, and decisions given incomplete information about probabilities or preferences. The course also covers descriptive decision theories such as Prospect Theory. An introduction into probability calculus including Bayes Theorem will be given. We will also discuss various visualization techniques such as influence diagrams and decision trees.

Learning outcomes
After completing the course students will know about rational decision processes, and how to structure and visualize decision problems. They will be able to use decision analysis techniques at an easy level to deal with multiple objectives, risk, intertemporal outcomes and incomplete information. Moreover, they will know about typical behavioral findings that conflict with the prescriptive methods.

Necessary prerequisites

Recommended prerequisites
The lecture generally assumes basic knowledge in mathematics calculus, optimization and statistics (mean, variance, standard deviation).

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. Dr. Danja R. Sonntag
Prof. Dr. Danja R. Sonntag
Frequency of offeringSpring semester & fall semester
Duration of module 1 semester
Range of applicationM.Sc. MMM, M.Sc. Bus. Edu., M.Sc. Bus. Math.
Preliminary course work
Program-specific Competency GoalsCG 1
LiteratureEisenführ, Weber, Langer: Rational Decision Making, 1st Edition, 2010, Springer.
Course outlineIntroduction
Multi-attribute value theory
Decision making under incomplete and inconsistent information
Decision making under risk
Monte Carlo simulation
Prospect theory