Syllabus

DATA-DRIVEN ECONOMICS (MSc. In Data Science) A. A. 2018/2019

(Teacher Responsible: Prof. Marco A. Marini, Jury Marcucci)

Overview of the first part of the course 

In this first part of the course some elements of Economics of Information will be introduced 

using the lens of Game Theory. Game Theory is a mathematical discipline mostly 

suitable for modeling the strategic interaction among agents. The course provides 

the basic ingredients for playing with normal, extensive and coalitional form games, 

repeated games, Bayesian games and for the analysis of social choice, welfare functions, mechanism design and auctions. Many examples will be introduced during the course,

 including a few classic games and applications.

Main Topics

Week 1: Introduction, use of game theory, applications and examples, formal definitions 

of normal, extensive and coalition form games, payoffs, strategies, pure and mixed strategy 

Nash equilibrium, dominant strategies. 

Week 2: Extensive form games and Nash equilibrium refinements: perfect information 

games, backward Induction, subgame perfect equilibrium, evolutionary stable strategies, 

trembling hand perfection.

Week 3: Finitely and infinitely repeated games, endogenous cooperation, Folk theorem, 

economic applications. 

Week 4: Bayesian Games, epistemic types, Bayesian Nash equilibrium in static 

and dynamic games.

Week 5: Application of Bayesian games, applications. Cooperative games, 

two-person bargaining game, transferable utility cooperative games, Core.

Prerequisites

You should be comfortable with some elementary notions of calculus and 

probability theory.

Course Material

Slides for weeks 1-5. They will made available on the website during the course.

Suggested Readings for the first half of the course:

For parts 1-6: One of the following:

(i) Political Game Theory: an Introduction, Nolan McCarty & Adam Meirowitz, 2007;

(ii) A Primer in Game Theory, Robert Gibbons; 

(iii) Essentials of Game Theory, K. Leyton-Brown and Y. Shoham, 2008; 

(iv) Multiagent Systems, Algorithmics, Game-Theoretic, and Logical 

Foundations, K. Leyton-Brown and Y. Shoham.

For class presentations: material suggested and distributed during the course.

Evaluation

Oral or Written exam (60 percent of the final grade),

Class presentation or essay (40 percent of your final grade).