Spring 2021

Professor: Aaron Roth

TAs: Varun Gupta and George Noarov

Title: Tuesday/Thursday 1:30-3:00pm Philadelphia Time

Room: Wherever your imagination takes you

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 we find kidney-exchange cycles to incentivize people to participate in the exchange, without using money? How can we incentivize weather men not to lie to us about*WIND* and *RAIN*?
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 (5%), problem sets (45%), a midterm (25%), and a final project (25%).

Textbook: There is no required textbook. Several recommended books are Twenty Lectures on Algorithmic Game Theory, Algorithmic Game Theory, and The Ethical Algorithm (Chapter 3).

Office Hours and Discussion: Office Hours: See Piazza

We will be using Piazza to discuss class material, answer questions, and make announcements. The Piazza page for NETS 412 is piazza.com/upenn/spring2021/nets412. Students are encouraged to ask questions about the material on Piazza so that everyone can benefit and contribute to their answers.

Topics Covered:

TAs: Varun Gupta and George Noarov

Title: Tuesday/Thursday 1:30-3:00pm Philadelphia Time

Room: Wherever your imagination takes you

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 we find kidney-exchange cycles to incentivize people to participate in the exchange, without using money? How can we incentivize weather men not to lie to us about

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 (5%), problem sets (45%), a midterm (25%), and a final project (25%).

Textbook: There is no required textbook. Several recommended books are Twenty Lectures on Algorithmic Game Theory, Algorithmic Game Theory, and The Ethical Algorithm (Chapter 3).

Office Hours and Discussion: Office Hours: See Piazza

We will be using Piazza to discuss class material, answer questions, and make announcements. The Piazza page for NETS 412 is piazza.com/upenn/spring2021/nets412. Students are encouraged to ask questions about the material on Piazza so that everyone can benefit and contribute to their answers.

Topics Covered:

- Part 1: Game Theory and Game Dynamics
- Quick introduction to game theory: Zero sum and general sum games, Minmax strategies, Nash equilibrium, correlated equilibrium.
- Game Dynamics: Weighted Majority Algorithm
- Game Dynamics: Bandit Algorithms
- Game Dynamics: converging to Nash equilibrium in zero sum games; Game dynamics converging to correlated equilibrium in general sum games
- Game Dynamics: Best Response Dynamics and Potential Games.
- Price of anarchy and price of stability: Definition, routing games, hoteling games
- More if time allows...
- Part 2: Assignment Problems and Mechanism Design
- Stable Matchings and the Deferred Acceptance Algorithm
- Market Equilibrium and Gross Substitute Preferences
- Auction basics: First price auctions, second price auctions, truthfulness
- Maximizing welfare: The VCG Mechanism
- Auctions and Approximation Algorithms
- Combinatorial Auctions
- Online Auctions
- Maximizing revenue: Prior Free Mechanism Design
- Online auctions for digital goods
- Proper Scoring Rules and Prediction Markets
- More if time allows...

Problem sets will be turned in and graded via GradeScope. The course entry code is: 5VKV4V.

- Problem Set 1. Due via GradeScope at the start of class, Tuesday February 9.
- Problem Set 2. Due via GradeScope at the start of class, Tuesday February 23.
- Problem Set 3. Due via GradeScope at the start of class, Tuesday March 9.
- Problem Set 4. Due via GradeScope at the start of class, Tuesday March 30.
- Problem Set 5. Due via GradeScope at the start of class, Tuesday April 13.
- Problem Set 6. Due via GradeScope at the start of class, Tuesday April 27.

All lectures take place on Zoom, syncronously during class time. Recordings will be made available on Canvas.

Lectures:

- Lecture 1: Overview. (1/21)
- Lecture 2: Basic Definitions. (1/26)
- Lecture 3: Congestion Games and Best Response Dynamics. (1/28)
- Lecture 4: Characterizing When Best Response Dynamics Converges (2/2)
- Lecture 5: Sequential Learning 1: The Iterated Halving Algorithm (2/4)
- Lecture 6: Sequential Learning 2: The Polynomial Weights Algorithm (2/9)
- Lecture 7: Zero Sum Games and the Minimax Theorem (2/11)
- Lecture 8: Convergence to Equilibrium in Separable Multiplayer Zero Sum Games (2/16)
- Lecture 9: Correlated Equilibrium (2/18)
- Lecture 10: Sequential Learning 3: Minimizing Swap Regret (2/23)
- Lecture 11: Price of Anarchy and Price of Stability (2/25)
- Lecture 12: Pareto Optimal Exchange via Top Trading Cycles (3/2)
- Lecture 13: Stable Matchings (3/4)
- Lecture 14: Walrasian Equilibrium (3/16)
- Lecture 15: Auction Design and the VCG Mechanism (3/18)
- Lecture 16: Auction Design in Single Parameter Domains (3/23)
- Lecture 17: Approximation in Mechanism Design: Knapsack Auctions (3/25)
- Lecture 18: Maximizing Revenue (in Expectation) (4/1)
- Lecture 19: Posted Pricings: Profits and Prophets (4/6)
- Lecture 20: Random Sampling Auctions and Distribution Free Revenue Maximization (4/13)
- Lecture 21: Distribution Free Revenue Maximizing Online Auctions via Polynomial Weights (4/15)
- Lecture 22: The Multi Armed Bandit Problem and Profit Maximizing Pricings (4/20)
- Lecture 23: Mechanism Design via Differential Privacy (4/22)
- Lecture 24: Game Theory and Algorithmic Fairness (4/27)
- Lecture 25: Game Theory and Machine Learning: Online Multivalid Learning (4/29)