Spring 2021 catalog is now live!
To determine if a course is remote, hybrid, or in-person use the catalog search tool to narrow results. Otherwise, when browsing courses, the section indicates teaching mode:
R = Remote
H = Hybrid
0 = In-person
Teaching modes (remote, hybrid, in-person) are subject to change at any point. Please pay close attention when registering. Depending on the timing of a teaching mode change, faculty also may be in contact with students.
This course focuses on topics in game theory and mechanism design from a computational perspective. We will explore questions such as: how to design algorithms that incentivize truthful behavior, that is, where the participants have no incentive to cheat? Should we let drivers selfishly minimize their commute time or let a central algorithm direct traffic? Does Arrow’s impossibility result mean that all voting protocols are doomed? The overarching goal of these questions is to understand and analyze selfish behavior and whether it can or should influence system design. Students will learn how to model and reason about incentives in computational systems both theoretically and empirically. Topics include types of equilibria, efficiency of equilibria, auction design, network games, two-sided markets, incentives in computational applications such as file sharing and cryptocurrencies, and computational social choice.
Format: lecture; Synchronous in-class lectures will be broadcast live to remote students via zoom and recorded for asynchronous viewing. Lecture content may additionally be supplemented with prerecorded videos, and scheduled class time used as exercise or review sessions.
Grading: yes pass/fail option,
no fifth course option
weekly problem sets and/or programming assignments, two midterm exams, and a final project.
CSCI 256 or permission of instructor
current or expected Computer Science majors
The course will consist problem sets and programming assignments in which quantitative/formal reasoning skills are practiced and evaluated.