Dr Galit Ashkenazi-Golan COL 3.08

This course is available on the CEMS Exchange, MBA Exchange, MPA in Data Science for Public Policy, MSc in Applicable Mathematics, MSc in Financial Mathematics and MSc in Operations Research & Analytics. This course is available as an outside option to students on other programmes where regulations permit.

It is not available to students who have taken Game Theory (MA330) or Game Theory I (MA301).

Students on the MPA in Data Science for Public Policy are required to seek permission to take this course. 

Familiarity with a rigorous treatment of mathematics (through definitions, theorems and proofs) is expected. Students must know basics of linear algebra (matrix multiplication, geometric interpretation of vectors), analysis (continuity, closed sets), and probability theory (expected value, conditional probability, independence of random events).

Concepts and methods of mathematical game theory. Nim and combinatorial games. Congestion games. Games in strategic form, dominated strategies, Nash equilibrium. Cournot quantity competition. Game trees with perfect information, backward induction. Commitment. Expected utility. Mixed equilibrium. Zero-sum games, maxmin strategies. Extensive games with information sets, behaviour strategies, perfect recall. Bargaining. Geometry of equilibria.

This course is delivered through a combination of classes and lectures totalling a minimum of 30 hours across Autumn Term.

Weekly exercises are set and marked.

Required text: B von Stengel, Game Theory Basics. Cambridge University Press, 2022.

Exam (90%, duration: 2 hours) in the January exam period.
Continuous assessment (10%).