Students learn how to
* model real-life problems as mathematical optimization problems.
* design and implement algorithms to solve problems.
* formally proof properties of algorithms.
* evaluate and select algorithms based on properties of the algorithms.
* work in teams to solve a mathematical optimization problem.

Algorithmic Decision Making explores how mathematical and computational tools are used and designed to improve decision-making in different domains ranging from traditional optimization to strategic environments. The course introduces key concepts like (integer) linear programming, matching algorithms, and incentives in mechanism design and games. Students will learn to model real-world decision problems using the appropriate tools, implement and analyze algorithms, and evaluate their performance.

There are lecture notes available via Canvas.

Exchange students need to have obtained a BSc degree in Economics, International Business, Econometrics, or a related topic. Familiarity with the basic concepts of optimization and linear programming will be helpful. A solid basis in mathematics and calculus is also recommendable.