CS 598: Economics and Computation (Fall 2026)

Lectures
Thurs, 5:40 PM - 8:40 PM at Biomedical Engineering Building (BME), Room 102

Instructor
Xintong Wang (xintong.wang[at]rutgers.edu. Please add CS598 in email subject.)

Office Hours
Xintong: Thursdays 4:30-5:30pm at CoRE 319
TA: TBD

Announcements

  • Sep 2: Welcome to CS598: Economics and Computation!

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 /
HWs
09/03 Introduction [slides] Economic reasoning and artificial intelligence (via Shibboleth)
Chap 1 of Algorithmic Economics by Parkes and Seuken
09/10 Intro to game theory [slides] Chap 2 and Chap 4 of Algorithmic Economics
09/17 Intro to game theory (continued)[slides]
Eq. computation [slides] 		Chap 3 of Algorithmic Economics
09/24 Auction design [slides] Chap 6 of Algorithmic Economics (6.7 is optional)
10/01 Mechanism design [slides] Chap 7 (Sec 7.1-7.2) of Algorithmic Economics
10/08 Mechanism design II [slides] Chap 7 (Sec 7.4) of Algorithmic Economics
10/15 Online ad markets [slides]
Matching (one-sided & two-sided) [slides] 		Chap 10 of Algorithmic Economics
Chap 12 of Algorithmic Economics
10/22 Matching (Kidney-paired donation) [slides] Chap 12 of Algorithmic Economics
10/29 Proposal presentation & project Discussion
11/05 Information elicitation [slides] Chap 15 of Algorithmic Economics
11/12 Prediction markets [slides] Chap 16 of Algorithmic Economics
11/19 Crypto economics [slides]
11/26 Thxgiving break
12/03 Game-theoretic aspects in ML [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 (15%): 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 can be skipped. Absolutely no GenAI.
  • Problem sets (15%): There will be 2-3 problem sets, covering concepts discussed in lectures. You can work in pairs, if you wish.
  • In-class quizzes (20%): There will be 3 in-class quizzes, checking concepts discussed in lectures and problem sets. Drop policy: the lowest score will be dropped.
  • Course project (50%): 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
    • Project presentation (5%): October 29
    • Presentation (15%): December 10
    • Final report (20%): December 15

Acknowledgments

The course draws inspiration from: