2024FA_COMP_SCI_332-0_SEC1 Online Markets
Synopsis: Online markets are causing significant changes to society. Examples include eBay, airBnB, tinder, Uber, stackexchange, and Amazon. This class gives an introduction to the science of online markets combining topics from game theory and economics with topics from machine learning and algorithms. The two main topics of interest are how individuals in these marketplaces optimize their strategies and how the market designer optimizes the rules of the market place so that, when individuals optimize their strategies, desired market outcomes are achieved. Lectures will develop intuition for these topics through mathematical analysis. Student work will be a mix of exercises, quizzes, short projects, and peer reviews.
Prerequisites: CS 212 (Discrete Math) and CS 214 (Data Structures) or CS 336 (Algorithms) or ECON 380-1 (Game Theory).
Instructor: Jason Hartline
Office hours: Wednesday, 11am, Mudd 3015
Lectures: MW 9:30-10:50am in 2122 Sheridan Rd Classroom 250.
Discussion: Piazza.
Teaching Assistants: Chang Wang
Problem Solving Session: F 9:30-10:50am in 2122 Sheridan Rd Classroom 250.
Office hours:
- Monday, 1pm, Mudd 3532,
- Thursday, 4pm, Annenberg G31
Grading: 35% projects, 15% peer review, 15% problem sets, 10% exercises, 10% quizzes, 15% final.
In-class Exercises: Each class will contain two in-class exercises. One at the start of class and one in the middle of class. Students should do in-class exercises with their neighbor, but submit their answers separately. These exercises are assigned in class, and due one hour after class. Grades will be 50% participation and 50% correctness. Grades will not show up in Canvas with this participation adjustment until the end of the course. The lowest four individual exercise grades will be dropped.
Problem Solving Session: Students are encouraged to attend optional weekly problem solving sessions on Fridays 9:30-10:50am lead by the TA. The TA will demonstrate how to solve the week's problems (30 minutes) and you will have the remaining time to get started on the week's problems with the TA present to help.
Problem Set and Project Policy: Problem sets and Projects are to be done in pairs. Both students must contribute to the solution of all problems. One copy of the assignment should be turned in. Both students will receive the same grade. Project report guidelines.
Peer Review: Projects will be peer reviewed. Peer review is part of the dialogue we have about course content, and serves to provide students with quick feedback about their projects. Peer review logistics. Peer review rubric and scoring guidelines.
Quiz Policy: Quizzes will be short and intended to assess understanding of facts and concepts in the course. Questions will primarily be true/false, multiple choice, or numeric answers. Quizzes are open notes, but to be done individually and without other Internet resources. See full details on logistics and policies of quizzes.
Accessibility and Accommodations: Northwestern University is committed to providing the most accessible learning environment as possible for students with disabilities. Should you anticipate or experience disability-related barriers in the academic setting, please contact AccessibleNU to move forward with the university’s established accommodation process (e: accessiblenu@northwestern.edu; p: 847-467-5530). If you already have established accommodations with AccessibleNU, please let me know as soon as possible, preferably within the first two weeks of the term, so we can work together to implement your disability accommodations. Disability information, including academic accommodations as part of a student’s educational record, is confidential under FERPA regulations.
Other exceptions to published policies: No exceptions to published policies will be made without the recommendation of the dean.
Tentative Schedule:
- Week 0 (Sept 25): Allocating a Resource
- Week 1 (Sept 30; Oct 2): Game Theory
- Week 2 (Oct 7,9): Online Learning
- Week 3 (Oct 14, 16): Learning and Game Theory
- Week 4 (Oct 21, 23): Welfare and Revenue
- Week 5 (Oct 28, 30): Optimal Auctions
- Lecture 9: Randomized welfare analysis, second-price with reserve [notes] [slides]
- Lecture 10: pricing revenue, virtual values, optimal reserves [notes] [slides]
- Lecture 11: revelation principle, optimal auctions, virtual welfare maximization [notes] [slides]
- Lecture 12: optimal auctions (review), learning to price, learning to auction [notes] [slides]Week 6 (Nov 4, 6): Learning Auctions and Econometrics
- Project 3: Learning in Games
- Week 7 (Nov 11, 13): Online Allocation
- Week 8 (Nov 18, 20): Matching Markets
- Week 9 (Nov 25, 27): Online Matching and Applications
- Lecture 17: Matching mechanisms, duality (continued), externality pricing. [notes] [slides]
- Lecture 19: Online markets in practice (Guest lectures by Onno Zoeter from Booking.com and Brendan Lucier from Microsoft Research)
- Week 10 (Dec 2, 4): Matching mechanisms.
- Week 11: Final Exam, Thursday, 12/12/2024: 3PM-5PM. (Cumulative, Review materials [notes])
Course Summary:
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