This undergraduate course explores the interaction between the disciplines of economics and computer science. In one direction, we will see how computational thinking gives a new perspective on areas of economic theory such as game theory, mechanism design, and social choice. In the other direction, we will discuss how economic approaches can address timely questions in computer science and artificial intelligence.
Instructor: Han Shao, hanshao@umd.edu, office hours: Mon 2-3, IRB 5132
TA: Shashaank Aiyer, saiyer1@umd.edu, office hours: Tue 12:30-1:30, AVW 4117
This will be a mathematically rigorous theory course for advanced undergraduates. Students should have taken a course in algorithms, or be taking one concurrently, and be mathematically mature. All homeworks and exams will consist of proof-based questions.
Grading will be based on participation (5%), homeworks (35%), midterm (30%), and final exam (30%).
We will use Piazza for Q & A. Please post your questions here.
There is no required textbook. The recommended book is Algorithmic Game Theory. If you are interested in learning more about no-regret learning, you can refer to the online learning bible: Prediction, Learning, and Games.
Course materials are built based on courses developed by Ariel Procaccia and Aaron Roth.
This is a temporary schedule and subject to change.