CC 501: Decision Analysis: Business Analytics II

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

Examiner Performing lecturer

Prof. Dr. Danja R. Sonntag Prof. Dr. Danja R. Sonntag