DS 574: Algorithmic Mechanism Design
Fall 2022
Boston University


Course Details

Instructor: Professor Kira Goldner (goldner@).
Office Hours: Tuesday 5–6pm and by appointment.
Office Location: 111 Cummington Mall, 138P.

Teaching Fellow: Peiran Xiao (pxiao@).
Office Hours: Wednesday 2:30–3:30pm and by appointment.
OH Location: 111 Cummington Mall, 141.

Lectures:
Tuesday/Thursday 3:30—4:45pm, CAS 426.

Important Links:

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.)
Resources listed are optional, and often multiple versions of the same material are listed so that you can find what is best suited to you.

Date Topic Resources
Sep 6 Overview and Policies, Intro to AGT Slides, Worksheet, Notes, R1.1-2
Sep 8 Incentive Compatibility Worksheet, Notes, R1.3
Sep 13 The Revelation Principle Worksheet, Notes, R1.4, H2
Sep 15 Myersonian Virtual Welfare Worksheet, Notes, R1.5, H3.3
Sep 20 Ironing Virtual Values and Quantile Space Worksheet, Notes, H3.3.3-4
Sep 22 Ironing Intuition, Multidimensional Settings and VCG Worksheet, Notes, R1.7
Sep 27 Ascending Auctions I Worksheet, Notes, R2.1-2
Sep 29 Ascending Auctions II & Walrasian Equilibria R2.3-5
Oct 4
Oct 6
Oct 11 NO CLASS (Monday schedule)
Oct 13
Oct 18
Oct 20
Oct 25
Oct 27
Nov 1
Nov 3
Nov 8
Nov 10
Nov 15
Nov 17
Nov 22
Nov 24 NO CLASS (Thanksgiving)
Nov 29
Dec 1
Dec 6
Dec 8



Huge gratitude to Jason Hartline and Tim Roughgarden for their publicly available materials which have made the development of this course possible.