This course builds on Methods: Game Theory 1. It covers more advanced material required for the analysis of static and dynamic games of incomplete information. A game of incomplete information describes a situation in which a player is uncertain about the "types" of the other players (in particular, a player may not know exactly what the other players' payoff functions are). The course will present the solution concepts of Bayesian Nash equilibrium and Perfect Bayesian equilibrium. To illustrate the theory, the course will use applications from industrial organization and other fields.
Thanks to the course the students will be prepared for courses on Competition Policy or Innovation and Networks.
Required PrerequisitesKnowledge of strategic-form games and Nash equilibrium as well as extensive-form games and subgame perfection.
Recommended PrerequisitesMethods: Game Theory 1
- Static games with incomplete information and the Bayesian Nash equilibrium. (Typical application: Auctions)
- Dynamic games with incomplete information and the Perfect Bayesian equilibrium. (Typical application: Signaling model)
The course runs for 9 weeks. It consists of interactive lectures and homework assignments. There will be one lecture (2h) per week and a total of three homework assignments (problem sets). The homework assignments have to be prepared in groups and will be graded.
- Robert Gibbons, A Primer in Game Theory, Pearson Education.
- Martin Osborne, An Introduction to Game Theory, Oxford University Press.
- Eric Rasmusen, Games and Information, Blackwell Publishing.
- Steven Tadelis, Game Theory: An Introduction, Princeton University Press.