Thanks for a great term!
Tuesday/Thursday, 2:303:55pm, Baxter 127
Adam Wierman, 258 Jorg, OFFICE HOURS: on request
John Ledyard, 102 Baxter, OFFICE HOURS: on request
Jason Marden, 335 Moore
Over the last few years there has been enormous activity at the interface of computer science, game theory, economics, and control. In this course, our goal is to survey some important of the important new areas that are emerging in this field. Some of the topics we will study include:
This course is intended for graduate students and will be organized as a topics course. Postdocs are also encouraged to attend lectures on topics of interest to them, and need not be registered to do so. Registered students will be expected to present multiple lectures in addition to completing homework assignments. It is expected that students are comfortable with the basics of game theory, graph theory, and probability theory.
Algorithmic Game Theory, Edited by Nisan, Roughgarden, Tardos, Vazirani
You will receive one homework every few weeks. These are meant to reinforce the material that we are learning during that time, so please start immediately. Please do not search the web for help on the homework problems. It is difficult to develop good homework problems, and thus you may come across similar problems if you search the web for help.
You are strongly encouraged to collaborate with your classmates on these problems, but each person must write up the final solutions individually. You should note on your homework specifically which problems were a collaborative effort and with whom.
The ideal presentation will cover a few results from either a book chapter or paper and then highlight interesting extensions and open problems that remain. Presentations should be 6080min in length and may be done individually or in groups of two. Presentations may use any combination projector/whiteboard desired. In order to prepare for the presentation, students are required to discuss the topic and book chapter/paper that will be presented with one of the professors outside of class.
The grade of the presentation will (in large part) be determined by the audience of the presentation. Every member of the audience will complete this form. The grade will be a weighted average of the audience and professor scores, where the professor scores count as 3 student scores.
Date 
Topic & References 
Coursework 

Jan 8 
Overview: Game theoretic approaches to distributed optimization and control [ppt] 

(in)Efficiency of games 

Jan 10 
A load balancing allocation game: Price of anarchy vs. price of stability 
HW1 out 

Jan 15 
Routing games: Models and Examples. 

Jan 17 
Routing games: Understanding and reducing the price of anarchy. 
HW1 due HW2 out 

Jan 22 
Student presentation: More than you wanted to know about Braess' paradox Julian and Mohamed [pdf1] [pdf2] 

Jan 24 
Student presentation: Dustin and Marjan Network formation games.


Strategic learning in games 

Jan 29 
Introduction to learning dynamics (I)  HW2 due  
Jan 31 
Introduction to learning dynamics (II)  HW3 out  
Feb 5 
Student presentation: Cory, Lina, and Sherwin [ppt1] [ppt2]


Feb 7 
Guest Lecture: Calibration tests Federico Echenique and Eran Shmaya


Feb 12 
Student presentation: Julian and Mohamed [pdf1] [pdf2]


Mechanism Design 

Feb 14 
Goals and models dominant strategy mechanisms, revelation principle, VCG mechanisms [pdf] 
HW3 due HW4 out 

Feb 19 
Bayesian mechanisms [pdf] 

Feb 21 
No Class  
Feb 26 
Nash mechanisms & a Student presentation Mahyar [pdf1] [ppt2]


Prediction Markets 

Feb 28 
Theory: markets, parimutual, polls, scoring rules experiments and applications [ppt] 
HW4 due  
Mar 4 
Student presentation: Dustin and Marjan [ppt1] [ppt2]


Sponsored search 

Mar 6 
Introduction [pdf1] [pdf2] [pdf3]


Mar 11 
Student presentation: Cory and Sherwin [ppt1] [ppt2]
