Course ID:  M57015FK

Credits: 3

Objective: 

This course focuses on theoretical aspects of game theory and applications in economics and machine learning. We expect the students to learn solid theoretical foundation on game-theoretical related topics, such as solution concepts, fixed-point theorems, minimax principles, social choice, auctions, online learning algorithms, etc., and then catch-up the recent progress in this field by paper-studying and presentations.

Course Prerequisites: Algorithms, Calculus, Probability Theory.

Outline:

1. Introduction and Preliminaries 

2. Minimax Principles 

3. Equilibrium Concepts 

4. Social Choice 

5. A Sketch of Nash’s Theorem from Fixed Point Theorems 

6. Auctions & Mechanism Design Basics 

7. No-Regret Dynamics

Teaching Method:  Lecturing, Discussion, Paper Presentations

Reference:  

1. Twenty Lectures on Algorithmic Game Theory. Tim Roughgarden. Cambridge University Press. 2016. 

2. Self-prepared slides.