AI, Games, and Markets  Fall 2024
Course Summary
This course will cover the basics of game theory and market design, with a focus on how AI and optimization enables largescale game solving and markets. We will cover the core ideas behind recent superhuman AIs for games such as Poker. Then, we will discuss how AI and game theory ideas are used in marketplaces such as internet advertising, fair course seat allocation, and energy markets. This is intended to be an advanced MS level and senior undergraduate course for students in Operations Research and Financial Engineering.
Admin stuff
Course Info
 Instructor: Christian Kroer
 Time: Mondays & Wednesdays 10:1011:25 am
 Location: 313 Fayerweather

 Professor Office hours:* Wednesday 910 am Mudd 314

 TA Office hours:* Wednesday 4:305:45 pm
 Courseworks site: courseworks site
Prerequisites
 Mathematical maturity; ability to follow proofs
 Linear algebra: vector and matrix algebra, eigenvalues
 Calculus: gradients, optimality conditions, Lagrange multipliers
 Optimization: linear programming, mixedinteger programming, some convex optimization (these can potentially be learned along the way). If you are coming from outside IEOR/have not taken classes on these topics, then you should take a look at e.g. this book to get a sense for LP, LP duality, convexity, etc.
Course Structure
The course will be lecturebased, with Christian Kroer giving the lectures. We may also include a few guest lectures from other researchers. At the end of the course there will be a few lectures of project presentations by students.
Readings will be from my textbook draft, which is freely available as a pdf (see link in textbooks section). Supplementary readings will also be suggested, though these are optional.
Students will complete a project, which should be done in groups of 24 students (project grading will be done proportional to group size). Special permission is needed to do a 1person project, and it must be an ambitious researchoriented project.
There will be two tests: a midterm and a final. Both will be during class, one early’ish in the semester, and one in late November/early December. Both tests will be relatively short, since there will only be 1h25m to complete each test.
Grading will be as follows:
 30% final project writeup
 15% homework (there will be 45 homeworks)
 20% midterm (midterm will be in class, probably Oct 14th)
 20% final (final will be in class, probably Nov 25th or Dec 2nd)
 5% Project proposal
 5% Project milestone report
 5% Final project presentation (I may drop presentations depending on how many groups there are)
Bonus credit: Find mistakes in my textbook! Anyone who discovers a mistake in the textbook will be awarded extra credit applied after grade cutoffs are calculated (if a mistake is discovered by more than one person then credit will be split proportionally). If you have improvement suggestions then please share those as well. I will also award extra credit for these if I end up incorporating them. In any case I would love to hear them.
Homework
Homeworks will be posted on courseworks.
Homework lateness policy:
 All homeworks due at midnight on stated date
 Everyone gets 3 late days
 If you are handing in late, you must email the TAs and me to say that you are using a late day. Include in the email how many you have used.
Course Content
Outline
A rough outline is as follows (this is grouped by topic, the presentation ordering will be different):
 Intro to game theory and market design
 Noregret learning
 Nash equilibrium
 Zerosum games, minimax theorem
 Imperfectinformation games and poker AIs
 Stackelberg equilibrium, applications to homeland security and wildlife protection
 Market design and the internet
 Internet advertising auctions
 Recommender systems
 Bias and fairness in machine learning and internet advertising
 Electricity markets
 Fair Resource Allocation
 Fair division via competitive equilibria
 Fair course seat allocation
 Allocating food to food banks
Textbooks
The primary text will be my book draft. I will also fequently mention complementary reading from the AGT book:
 AI, Games, and Markets draft (CK) by Kroer (free)
 Algorithmic Game Theory (AGT) by Nisan, Roughgarden, Tardos, and Vazirani (it’s free)
Additionally, we may use some sections of the following books. They are also recommended for supplementary reading:
 Handbook of Computational Social Choice (HCSC) by Brandt, Conitzer, Endriss, Lang, & Procaccia (it’s free, password: cam1CSC)
 Multiagent Systems (MS) by LeytonBrown & Shoham (it’s free)
 Twenty Lectures on Algorithmic Game Theory (TLAGT) by Tim Roughgarden (the individual notes can be found on Tim’s website under the course “Algorithmic Game Theory”)
 Introduction to Online Convex Optimization (Hazan) by Hazan (it’s free)
 A Modern Introduction to Onlinea Learning (Orabona) by Orabona (it’s free)
If you want to practice problemsolving in order to prepare better for the exam, you can find exercises in the following books:
 Algorithmic Game Theory (AGT) by Nisan, Roughgarden, Tardos, and Vazirani (it’s free)
 Networks, Crowds, and Markets by Easley and Kleinberg (I link to a free preprint of the book)
 Game Theory: Analysis of Conflict by Roger Myerson (ebook available through CLIO)
Project
Students will complete a halfsemester project on topics related to the course. This project can be applied, theoretical, or a mixture. Students are encouraged to formulate their own project proposals. That said, I will also provide some candidate project topics on courseworks.
Project rules:
 Teams should have 24 students. Solo projects require instructor permission and an ambitious scope.
 A onepage project proposal is due November 6th
 A 2page progress report is due November 22nd
 A 510 page whitepaper, formatted as a NeurIPS conference paper, is due December 22nd
 Each team must make a ~20m presentation of their project
Tentative Schedule
Date  Topic  Reading  

9/4  Course intro  CK Ch 1  
9/9  Intro to game theory  CK Ch. 2, AGT Ch 1, 2 (optional)  
9/11  Continue GT intro  CK Ch. 2  
9/16  Intro to auctions  CK Ch. 3, AGT Ch 9, 10 
Related Courses
Below is a list of related courses.
Instructor  Title  Year  School 

Gabriele Farina  Topics in Multiagent Learning  2023  MIT 
John P. Dickerson  Mechanism Design  2022  UMD 
Gabriele Farina & Tuomas Sandholm  Computational Game Solving  2021  CMU 
Christian Kroer  Economics, AI, and Optimization  2020  Columbia 
John P. Dickerson  Applied Mechanism Design for Social Good  2018  UMD 
Fei Fang  Artificial Intelligence Methods for Social Good  2018  CMU 
Yiling Chen  Topics at the Interface between Computer Science and Economics  2016  Harvard 
Vincent Conitzer  Computational Microeconomics: Game Theory, Social Choice, and Mechanism Design  2016  Duke 
Sanmay Das  MultiAgent Systems  2016  Wash U 
Ariel Procaccia  Truth, Justice, and Algorithms  2016  CMU 
Milind Tambe  Security and Game Theory  2016  USC 
Constantinos Daskalakis  Games, Decision, and Computation  2015  MIT 
Tuomas Sandholm  Foundations of Electronic Marketplaces  2015  CMU 
Tim Roughgarden  Algorithmic Game Theory  2013  Stanford 