Program Overview

Algorithmic Game Theory

The Algorithmic Game Theory course is a comprehensive study of mathematical models that explore the interplay between algorithms and strategic behavior. This course covers fundamental concepts from game theory and mechanism design, including Nash equilibria, the price of anarchy, auctions, market design, incentive compatibility, and online learning and dynamics.

Summary

The course delves into the study of mathematical models of the interplay between algorithms and strategic behavior, covering key concepts from game theory and mechanism design.

Content

The course content includes:

• Game theory
• Algorithms
• Mechanism design
• Auctions
• Nash equilibrium

Keywords

• Game theory
• Algorithms
• Mechanism design
• Auctions
• Nash equilibrium

Learning Prerequisites

The recommended courses for this program include:

• Algorithms
• Probability
• Linear Algebra
• Optimization

Learning Outcomes

By the end of the course, students must be able to:

• Formulate a game-theoretic model of the interaction of strategic agents
• Compare different equilibrium notions
• Analyze incentives in a game-theoretic model
• Quantify the efficiency of decentralized behavior using the concept of Price of Anarchy
• Reason about the concept of Nash equilibrium and understand how it can be used to model strategic behavior
• Design and analyze algorithms and mechanisms tailored for the interaction with strategic users
• Recognize the basic limitations of standard game theoretic models

Transversal Skills

The course aims to develop the following transversal skills:

• Communicate effectively, being understood, including across different languages and cultures
• Assess one's own level of skill acquisition, and plan their on-going learning goals
• Demonstrate the capacity for critical thinking

Teaching Methods

The course employs classical formal teaching interlaced with practical exercises.

Expected Student Activities

Active participation in exercise sessions is essential.

Assessment Methods

The assessment methods include:

• 30% midterm exam
• 70% final exam

Supervision

The course offers office hours and assistants, but no forum.

Resources

The course bibliography includes:

• Algorithmic Game Theory by Nisan, Roughgarden, Tardos & Vazirani (2007)
• Twenty Lectures on Algorithmic Game Theory by Roughgarden (2016)

In the Programs

The Algorithmic Game Theory course is part of the following programs:

• Management, Technology and Entrepreneurship Master semester 2
• Management, Technology and Entrepreneurship Master semester 4
• Financial engineering Master semester 2
• Financial engineering Master semester 4
• Management, Technology and Entrepreneurship minor Spring semester

Course Details

The course details are as follows:

• Semester: Spring
• Exam form: Written (summer session)
• Subject examined: Algorithmic game theory
• Courses: 2 Hour(s) per week x 14 weeks
• Exercises: 1 Hour(s) per week x 14 weeks
• Type: optional

Reference Week

The reference week schedule is provided, but the specific times for the course are not listed.

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