Business Decision Analysis
Module objectives and intended study results:
The students
- develop an understanding of structured and evidence-based methods to make important decisions in complex managerial decision situations involving conflicting objectives, uncertainty, and individual preferences,
- become acquainted with typical decision-making situations,
- have the ability to structure and model decision problems, to apply appropriate solution methods and communicate their decision-making process,
- develop an understanding of the importance of subjective judgments often required in decision making and can counter common biases and pitfalls,
- are encouraged and empowered to independent and self-reliant work.
Content:
Many business decisions are hard, and decision-makers have to deal with a complex environment, high levels of uncertainty, multiple conflicting objectives, or even different perspectives. Good decisions are based on structured and evidence-based approaches. This course will deal with structuring decision situations with decision trees, formulating and solving linear programs to support multiple interdependent decisions, and including topics such as sensitivity analysis, the value of information, and multi-criteria decision making. Since the methods are quantitative, and we apply math and statistics methods, exercises are provided to practice the skills acquired in class.
We will apply an application oriented approach with new methods applied directly to examples in the lecture and reinforced in the exercises.
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Language: English
Prerequisites:
This lecture is part of the core curriculum in the 4th semester. It builds upon knowledge acquired in Mathematical Methods, Introduction to Probability and Statistics, and Introduction to Econometrics (early part of lecture).
Relevant (and expected) knowledge:
- Mathematical Methods II (2nd semester)
- Matrices and systems of linear equations
- Introduction to Probability and Statistics (2nd semester)
- Discrete and continuous random variables, probability distributions, PDFs, CDFs
- Frequency tables, expected value, variance, standard deviation, quantiles
- Statistical independence, conditional probability, Bayes theorem
- Special distributions: Binomial, Poisson, Uniform, Exponential, Normal
- Introduction to Econometrics (4th semester, early part of lecture)
- Simple Regression
Credits:
- Bachelor International Business and Economics (IBE) Compulsory Module
Registration: Not required.
Details: Further Information are available at eLearning.
Next time offered in: Each Summer Semester