Instructor: Bo Waggoner
Format: in-person, Tu/Th 11:00am - 12:15pm, DLC 1B20
Course webpage: https://www.bowaggoner.com/courses/2025/csci6314/
This advanced graduate-level course will cover foundations and select advanced topics in Algorithmic Economics and Algorithmic Game Theory. Likely topics include game theory, equilibrium, algorithms for game playing and computational complexity thereof, mechanism design and auction theory, voting theory and computational social choice.
The course will be theoretical, mathematical, and proof-based. Preprequisites strongly encouraged include multivariable calculus, linear algebra, and probability; as well as undergraduate algorithms and complexity theory. No economic prerequisites are assumed, but some familiarity with game theory and/or microeconomics is beneficial.
The course will be primarily lecture and discussion-based. Initially, there will be accompanying homework problems. Later in the semester, students will study a topic of their choice in depth and produce a survey or project.
The final score will be calculated by a weighted average of the grades in each component. Course letter grades will be assigned based on the final score.
Students in general get one "freebie" for sickness or another unexpected emergency. The freebie gives a student a 50% grade bump on any homework or intermediate project assignment (not the final project submission). The intention of the freebie is to allow a student to submit incomplete work and get approximately one week's worth of work credited "for free".
Every student will get the freebie automatically applied by the instructor at the end of the semester to assignment where it will improve the student's grade the most. There is no need to notify the instructor that one intends to use the freebie. Additional accommodations beyond one freebie will only be given in extreme circumstances and typically the first recourse in such circumstances is to withdraw from the course due to inability to complete it.
For understanding general course material, students are encouraged to use external resources, including AI if it is helpful (but ask the instructor for advice here). For homework, students may use external resources to understand relevant content, but may not search for the answer to the specific homework question, nor ask AI for the answer, nor ask others outside the class for the answer.
Students are encouraged to work on solving the homework in groups. Students must write up homework solutions separately in their own words.
For the project, students may use AI if it is helpful. Students must include an "AI Statement" explaining how they used AI and whether they found it helpful. It is strongly recommended to avoid using AI-generated text for the final project, as it is unlikely to promote one's own learning and unlikely to obtain a good grade (consult the instructor for advice). Peer feedback that you give to other groups may not include AI-generated text.