Algorithmic Game Theory

Fall 2013
Professor: Aaron Roth
TA: Steven Wu
Time: Monday/Wednesday 3:00-4:30
Room: Towne 321

Overview: In this course, we will take an algorithmic perspective on problems in game theory. We will consider questions such as: how should an auction for scarce goods be structured if the seller wishes to maximize his revenue? How badly will traffic be snarled if drivers each selfishly try to minimize their commute time, compared to if a benevolent dictator directed traffic? How can couples be paired so that no two couples wish to swap partners in hindsight? How can you be as successful at betting on horse races as the best horse racing expert, without knowing anything about horse racing? How can we set prices so that all goods get sold, and everyone gets their favorite good?

Prerequisites: This will be a mathematically rigorous theory course for advanced undergraduates. Students should have taken, or be taking concurrently a course in algorithms (such as CIS 320), be mathematically mature, and be familiar with big-O notation. Prior coursework in game theory is helpful, but not necessary. Everything will be presented from first principles.

Goals and Grading: The goal of this course is to give students a rigorous introduction to game theory from a computer science perspective, and to prepare students to think about economic and algorithmic interactions from the perspective of incentives. Grading will be based on participation, problem sets, a midterm, and a final exam.

Textbook: A recommended textbook is Algorithmic Game Theory, which is also available for free on the web here (username=agt1user, password=camb2agt).

Office Hours and Discussion: Office Hours: Tuesday and Friday 4:00pm-5:00pm in GRW 565 (Steven) and Monday 4:30-5:30 in Levine 603 (Aaron)
We will be using Piazza to discuss class material and answer questions. The Piazza page for NETS 412 is Students are encouraged to ask questions about the material on Piazza so that everyone can benefit and contribute to their answers.

Topics Covered:
  1. Part 1: Game Theory and Game Dynamics
    1. Quick introduction to game theory: Zero sum and general sum games, Minmax strategies, Nash equilibrium, correlated equilibrium.
    2. Introduction to Linear Programming and LP duality. Linear programs as zero sum games.
    3. Game Dynamics: Weighted Majority Algorithm
    4. Game Dynamics: Bandit Algorithms
    5. Game Dynamics: converging to Nash equilibrium in zero sum games; Game dynamics converging to correlated equilibrium in general sum games
    6. Game Dynamics: Best Response Dynamics and Potential Games.
    7. Price of anarchy and price of stability: Definition, routing games, hoteling games
    8. More if time allows...
  2. Part 2: Assignment Problems and Mechanism Design
    1. Stable Matchings and the Deferred Acceptance Algorithm
    2. Market Equilibrium and Gross Substitute Preferences
    3. Auction basics: First price auctions, second price auctions, truthfulness
    4. Maximizing welfare: The VCG Mechanism
    5. Auctions and Approximation Algorithms
    6. Combinatorial Auctions
    7. Online Auctions
    8. Maximizing revenue: Prior Free Mechanism Design
    9. Online auctions for digital goods
    10. More if time allows...

Problem Sets and Exams:
  1. Problem set 1. Due Monday September 30.
  2. Problem set 2. Due Monday October 14.
  3. Practice Midterm. Real Midterm on Wednesday October 23.
  4. Problem set 3. Due Monday November 4.
  5. Problem set 4. Due Monday November 25.


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