Professors
Basic knowledge about statistics and probability.
Basic knowledge about calculus.
This course is intended to develop a basic understanding of the different quantitative methods that allow to create models for decision making. Learning Outcomes of this subject are:
- Know terminology, notation and methods from quantitative research, concretely those related to inference.
- Able to analyze and summarize information from lectures and materials provided by the teacher.
- Choose the statistical method to solve any economic problem.
These are the topics that will be covered during the course:
First part
1.- Probability Review
2.- Conditional probability and Bayes' Theorem
3.- Decision Trees
Second part
4.- Multi-Criterion Decision Making
5.- Time discounting and Net Present Value
6.- Risk, Rationality and Utility
7.- Game Theory
Weekly teaching will consist of one lecturing session to explain basic concepts and group problem solving in class to apply knowledge to practical situations. Practice sessions are for problem solving and practical case.
Continuous assessment has the following evaluation structure:
Practical case - first part-Midterm 30%: Statement, resolution, and presentation
Practical case - second part-Final 30%: Statement, resolution, and presentation
Individual assignments grade 30%: 3 homework
Attendance, participation, and classwork 10%: Class deliverables
To incorporate the practical case and group grades to the evaluation scheme, the average of the grade of individual assignments must be 4 or above. The assessment of the practical case will be as follows:
1. Statement: originality and degree of application (10%)
2. Resolution of the exercise: (60%) - 20% each part
3. Conclusions (10%)
4. Explanation of the cases (20%)
"Decision Analysis for Management Judgment". Paul Goodwin and George Wright. Wiley 2009.