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Learning Objectives

LEARNING OUTCOMES: the aim of this course is to introduce students to strategic reasoning through a formal training in game theory and a parallel set of lectures on applications to competition policy. Specifically, we will formally introduce the basic ingredients of non cooperative games and a number of equilibrium concepts used to solve them. These will be applied to studying strategic interaction among firms and to design incentive schemes to achieve a number of basic public policy goals.

At the end of the Course students should be able to understand and apply the logical approach of game theory to analyse the strategic environment that firms face in regime of oligopoly. In particular, we will study anti-competitive conducts put forth by firms and public policies to address them. Emphasis will be given to the role of algorithms and digital markets such as search engines, social media or online marketplaces.

At the end of the Course students should be able to apply the knowledge to identify potential anti-competitive practices.

Given a firm's conduct, the student should be able to (i) sketch its overall impact on economic surplus and its redistributive impact on the individual surplus of the parties involved and (ii) suggest potential interventions.

At the end of the course students should be able to analyse market practices and prepare presentations to discuss their effects on competition.




First part: Complete Information

Static games of complete information. The Nash equilibrium in pure strategies. Nash equilibrium in mixed strategies. Cournot model. Bertrand model. Dynamic games of complete information. Extensive form games and backward induction. Subgame perfect Nash equilibrium. Stackelberg model of duopoly. Repeated games. The Prisoner’s dilemma and coordination. The Folk theorem.

Second part: Incomplete Information

Static Games of incomplete Information. Bayesian Nash equilibrium. An auction game. Harsanyi’ interpretation of mixed strategies. Dynamic games of incomplete information. Perfect Bayesian equilibrium. Basics of signalling.


Gibbons, R. (1992), Game theory for applied economists, Princeton University Press.


Gibbons, R. (1992), Game theory for applied economists, Princeton University Press.
Osborne, M.J., 2004. An introduction to game theory (Vol. 3, No. 3), New York: Oxford university press.
(advanced) Martin J. Osborne and Ariel Rubinstein (1994), A course in game theory, MIT Press
Tadelis S. (2013), Game Theory: An Introduction, Princeton University Press

Teaching methods

The module is composed of classes and practice sessions.

Zero tolerance policy regarding academic dishonesty

Academic dishonesty includes cheating on exams, plagiarism, improper citation, recycled work, unauthorized assistance, or similar actions. Assignments and projects are specific to individual courses; presenting the same work in two different courses (including previous courses) is considered recycling and is unacceptable.

Students who commit an act of academic dishonesty will receive a failing grade.

Punctuality is mandatory.

Students must arrive in class on time and must not leave before the end of the class without a specific reason. It is the responsibility of the student to catch up on any missed work. Correct, active and responsible participation is insisted on. Proper behavior must be observed in class.

Exam Rules

The assessment method is entirely based on a written exam. The exam is composed of two or three exercises, possibly including theoretical questions. It requires students to be able to solve games covered in the module.
The grade for the exam is expressed out of thirty points.
Since EEBL students take the exam at the same time as the exam in Industrial Organization, the grade obtained in each of the two modules will have a 50% weight in determining the final grade for the course. In particular:
Final Grade = 0.5 x (Grade in Game Theory Module) + 0.5 x (Grade in Industrial Organization Module).
Students will have to obtain at least 18/30 in each of the two modules to pass the exam.