Course Details
Instructor: Professor Kira Goldner (goldner@).
Office Hours: Tuesday 3:30–4:30pm and by appointment.
Office Location: CCDS 1339 (665 Comm Ave).
Teaching Fellow: Peiran Xiao (pxiao@).
Office Hours: TBD
OH Location: TBD.
Lectures:
Tuesday/Thursday 1:30—3:15pm, KCB 107.
Important Links:
- Course Policies: Syllabus
- For communication: Piazza access code AMD
- For submitting homework: Gradescope course entry code ZZV4DV
Course Description: This course is an introduction to the interdisciplinary area of Algorithmic Mechanism Design: where computational perspectives are applied to economic problems, and economic techniques are brought to problems from computer science. We will explore a broad range of topics at the frontier of new research, starting with some of the fundamentals, such as welfare-maximizing auctions and types of Nash Equilibria. Throughout the semester, the class will also learn about prevalent topics such as (1) Data Science & Incentives, (2) Mechanism Design for Social Good, and (3) optimization and robustness in mechanism design.
The course is aimed at graduate students but will be accessible to motivated advanced undergraduate or masters students with some background in proofs (DS 122), algorithms (DS 320), and probability (MA 581).
Homework: Biweekly homeworks will be posted on Piazza when they become available. Homework must be typed up using LaTeX; here is a quick resource on LaTeX and here is a LaTeX template you may use for the homework. Here is another short guide to LaTeX. You may find it easier to use Overleaf.
Lecture Schedule
Below is a table with that will reflect what we cover in each lecture and will point to corresponding reading material. Lectures listed more than one date in advance are tentative topics. There is no required textbook for this course, as all materials are available online for free and we will switch between materials. Some shorthand for the reading material:
- R1.x = Tim Roughgarden's AGT lecture notes, lecture x. (Alternatively available in book form here.)
- R2.x = Tim Roughgarden's AMD lecture notes, lecture x.
- Hx = Jason Hartline's textbook "Mechanism Design and Approximation," chapter x.
- Kx = Anna Karlin's textbook "Game Theory, Alive," chapter x. (Also in book form.)
Date | Topic | Resources |
---|---|---|
Sep 5 | Overview and Policies, Intro to AGT | Slides, Worksheet, R1.1-2 |
Sep 7 | Incentive Compatibility | R1.3 |
Sep 12 | The Revelation Principle | R1.4, H2 |
Sep 14 | Myersonian Virtual Welfare | R1.5, H3.3 |
Sep 19 | Ironing Virtual Values and Quantile Space | H3.3.3-4 |
Sep 21 | Ironing Intuition, Multidimensional Settings and VCG | R1.7 |
Sep 26 | Ascending Auctions I | R2.1-2 |
Sep 28 | Ascending Auctions II & Walrasian Equilibria | R2.2-3,5 |
Oct 3 | Recap and Big Picture | |
Oct 5 | LP Duality I: Primals, Duals, Weak Duality | |
Oct 10 | NO CLASS (Monday schedule) | |
Oct 12 | LP Duality II: Strong Duality, Complementary Slackness, Welfare | |
Oct 17 | LP Duality III: Lagrangian Duality | CDW STOC '16 |
Oct 19 | LP Duality IV: Algorithmic Rev Max | |
Oct 24 | LP Duality V: Approximate Revenue Maximization | CDW STOC '16 |
Oct 26 | Projects & Prophet Inequalities | Projects, R1.6 | Oct 31 | Prior Independence: Bulow-Klemperer & Single Sample | R1.6, H5.2-3 |
Nov 2 | Gains from Trade in Two-Sided Markets | BGG '20 |
Nov 7 | Mechanism Design for Social Good: Health Insurance Markets | EGW '20 |
Nov 9 | Mechanism Design for Social Good: Kidney Exchange | |
Nov 14 | Mechanism Design for Social Good: COVID Testing and the Research-to-Practice Pipeline (Francisco J. Marmolejo Cossío) | LMCJRGGBVTAL '21, LMCMP '22 |
Nov 16 | Interdependent Values I | |
Nov 21 | Interdependent Values II | |
Nov 23 | NO CLASS (Thanksgiving) | |
Nov 28 | Selling Separately or Bundling | R2.18, BILW '14, CDW '16 |
Nov 30 | Carbon Auctions | |
Dec 5 | Project Presentations | |
Dec 7 | Project Presentations |
Huge gratitude to Jason Hartline and Tim Roughgarden for their publicly available materials which have made the development of this course possible.