CS 598: Economics and Computation (Fall 2025)

Lectures
Weds, 3:50 PM - 6:50 PM at Tillett Hall (TIL), Room 257
Instructor
Xintong Wang (xintong.wang[at]rutgers.edu. Please add CS598 in email subject.)
Garrett Seo (gks43[at]scarletmail.rutgers.edu)
Office Hours
Xintong: TBD at CoRE 319
Garrett: TBD

Announcements

  • Sep 2: Welcome to CS598: Economics and Computation! You can find the course syllabus here.

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)

Date Lecture Readings Pre-class CQs /
Peer Evaluation
09/03 Introduction [slides] Economic reasoning and artificial intelligence (via Shibboleth)
Chap 1 of Algorithmic Economics by Parkes and Seuken		 Class survey
09/10 Intro to game theory [slides] Chap 2 and Chap 4 of Algorithmic Economics CQs for Game Theory
09/17 Eq. computation [slides]
09/24 Auction design [slides]
10/01 Mechanism design [slides]
10/08 Mechanism design II [slides]
10/15 Online ad markets [slides]
10/22 Matching [slides]
10/29 Midterm (tentative)
11/05 Information elicitation [slides]
11/12 Prediction markets [slides]
Crypto economics [slides]
11/19 GT aspects in learning I [slides]
11/26 Thxgiving break
12/03 GT aspects in learning II [slides]
12/10 Project presentations

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, problem sets, a midterm and a class project. We will follow the grading scheme below:

  • Class participation (10%): 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. Drop policy: two CQs / commentaries can be skipped. Absolutely no GenAI.
  • Problem sets (20%): There will be 2-3 problem sets, covering concepts discussed in lectures. You can work in pairs, if you wish.
  • Midterm (10%)
  • 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 2-4, 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%): October 22
    • Presentation (15%): December 10
    • Final report (20%): December 15

Acknowledgments

The course draws inspiration from: