CS 598: AI Methods for Market Design (Spring 2024)
- Lectures
- Fridays, 10:20 AM - 1:20 PM at Science & Engineering Resource Center (SEC), Room 205
- Instructor
- Xintong Wang (Email: xintong.wang[at]rutgers.edu)
- Office Hours
- Fridays, 2 PM – 3PM at CoRE 319
Announcements
- Jan 15: Welcome to CS598! You can find the course syllabus here.
- Jan 18 [update @ 8pm]: We will have our first lecture (1/19 10:20am) on Zoom (link in Canvas) due to the winter weather advisory.
- Jan 28: Please follow the guidelines on Canvas to indicate your paper presentation preferences by Wednesday 1/31, 23:59pm.
- Feb 1: Please check out the paper presentation assignment and guidelines on how to read and present research papers.
- Feb 20: Please check out HW1 and start early (due midnight, March 4, 2024). Here's a LaTeX template that you can use to type up your solutions. And here are some good introductions to LaTeX.
- Feb 26: Please find the guidelines for class project, together with some example ideas / projects here. Feel free to drop by office hours to discuss your project ideas / proposals with me. The project proposal is due 23:59pm, March 19, 2024, but you are encouraged to submit before spring break.
- March 23: Please sign up for a time slot here to discuss your course project with me on March 29 and for future office hours.
- April 8: Please check out HW2 and start early (due midnight, April 22, 2024).
Course Overview
Marketplaces use algorithms and sets of rules to allocate resources among self-interested participants, who hold necessary information and act to pursue their own goals. Market failure – the designed algorithms or rules fail to achieve certain objectives – can occur when the designer fails to elicit agent preferences or accurately model how agents respond to rules and influence each other to produce outcomes. The course introduces how to integrate modern AI and machine learning techniques and economic thinking (e.g., game theory) to (i) model strategic agent behavior, (ii) identify relation between rules in the marketplace and market failures, and (iii) redesign them to achieve broader, system-wide objectives. Cases of today’s marketplaces will be discussed, such as online platforms, ride-sharing systems, financial exchanges (centralized & decentralized).
Prerequisites: This course will assume fundamental knowledge in AI and machine learning (e.g., 01:198:440/461/462, 16:198:520/530/536, or equivalent) and mathematical maturity (comfortable with linear algebra, probability, and algorithm analysis). Students are expected to read and discuss research papers. Familiarity with economic/game theory will be helpful but not required. Please contact the instructor if you have questions regarding whether your background is suitable for the course.
Course Schedule (tentative)
Materials and Resources
Links to papers and reading materials will be posted before each lecture, and slides will be posted after lectures. We will draw on the following books for some of the lectures:
- A draft of Algorithmic Economics: A Design Approach, by David Parkes (Harvard) and Sven Seuken (U. Zurich). Relevant chapters of the book will be distributed on Canvas during the course.
- Twenty Lectures on Algorithmic Game Theory by Tim Roughgarden (Columbia). The book is not available online, but it is based on lecture notes for the course on Incentives in Computer Science.
Course Requirements and Grading
The course will involve one paper presentation, 2-3 problem sets, and a class project. We will follow the grading scheme below:
- Class participation (20%): Students are expected to read assigned chapters/papers before lecture and participate in class discussions. Based on initial understanding, students should complete simple comprehension questions / commentaries on papers before each class. Drop policy: two CQs / commentaries can be skipped.
- Problem sets (20%): There will be 2-3 problem sets, covering concepts discussed in lectures. You can work in pairs, if you wish.
- Paper presentation (15%): To complement lecture content, we will read and discuss a selected list of papers on recent advances. Each student (typically in a pair) should present and lead discussion on one paper. More information on paper bidding and presentation guidelines to follow.
- Final project (45%): The goal of the project is to allow you to explore independent interests, learn more about a particular area, and practice teamwork. Projects can be done in a group of 3, and may be analyzing an existing model or proposing new ideas, and primarily theoretical or empirical. More instructions to follow, and below is the tentative project deadlines:
- Project proposal (10%): March 8
- Presentation (15%): April 26
- Final report (20%): May 6
Acknowledgments
The course draws inspiration from: