CS 5854/ORIE 5138: Networks and Markets

  • Instructor: Prof. Nikhil Garg (he/him), ngarg@cornell.edu
  • TAs: Ulysse Hennebelle uh34@cornell.edu and Mikhail Fadin mf853@cornell.edu
  • Credits and Credit Hour Options: 3 hours, Letter Grade
  • Time and Location: Mondays/Wednesdays, 2:55PM - 4:10PM [In person, Bloomberg Auditorium]
  • Instructor office hours: Wednesdays, 4:20 - 5pm [Bloomberg 454]
  • TA office hours: Tuesday 3pm - 4pm [Bloomberg 475], Friday 5:30pm - 6:30pm Zoom

Important links

Course Description

This course considers computing challenges related to incentives, networks, crowds, and markets, with a focus on how these questions interact with algorithms and data-driven methods in applications such as online markets, social media, and civic systems. The course will cover the foundations of game theory and network theory, and then applications in matching markets, online platforms, recommendation systems, modern AI, and democratic systems. The course will be a combination of applied mathematical modeling of such systems and reading research papers related to these topics in practice.

Student Outcomes and course objectives: Students will be able to articulate challenges related to incentives and networks in markets and beyond, and will be able to apply mathematical modeling and data-driven methods to address these challenges. Students will also be able to read and understand research papers in this area, and will be able to present and critique such papers.

About the instructor

Nikhil is an assistant professor of Operations Research and Information Engineering at Cornell Tech, whose research is at the intersection of computer science, economics, and operations – on the application of algorithms, data science, and mechanism design to the study of democracy, markets, and societal systems at large. Things he’s worked on include 311 reporting systems, surge pricing, rating systems, how to vote on budgets, gerrymandering, stereotypes in word embeddings, and political polarization on Twitter. Outside of academia, Nikhil has been a data scientist at Uber, collaborated with Upwork and other freelancing marketplaces, and most recently led campaign data science at PredictWise during the 2020 US election cycle.

Prerequisites/Corequisites and Preparation summary

This course is primarily theoretical and conceptual, and requires some mathematical maturity and familiarity with mathematical notation and proof-based arguments. For example, students who have taken Modeling Under Uncertainty at Cornell Tech are likely to be well-prepared.

Class and Laboratory Schedule:

Lectures: 3 hrs/wk

Recitations: None required. Optional office hours with TAs and instructor

Textbook(s) and/or Other Required Materials:

None required to purchase. All readings will be distributed throughout the semester. All software used will also be open source/freely available.

Course communication

Course communication will primarily be over Ed Discussion, office hours, and email.

  • Ed Discussion: First resource for any question, whether regarding technical content or logistics. Please make your question visible to everyone, so that others may answer or benefit from your question. Instructors will aim to respond to questions in a 48-72 hour period, except for those of an urgent nature (e.g., typos on homeworks or lecture notes, clarifying course logistics, etc.). Among other things this means you should not wait until the last few days before an assignment is due to message us; we may not respond in time. You are encouraged to answer questions from other students, especially during the instructor “waiting period.”
  • Office hours: You are strongly encouraged to come to office hours for any reason. Office hours are the best way to ask in-depth technical questions, whether directly related to the course content or just things you’re interested in. TA office hours will often cover the homework questions in depth.
  • Email: Only for private questions and concerns, such as requests for accommodations or regrade requests. Please include “[networksmarkets]” in the subject line of any email. Technical questions will not be answered over email – please use Ed Discussion.

Please follow these norms in all communications with the instructors and other students.

Typical course topics covered

  • Game Theory (~3 weeks)
  • Networks (~3 weeks)
  • Matching Markets and Online Platforms (~3 weeks)
  • Recommendation Systems (~3 weeks)
  • Social Choice, Democracy, and Crowdsourcing (~3 weeks)

Assignments, Exams and Projects

  • Homework: 50%. 4-5 homeworks. Each HW is an equal part of the homework grade. Lowest score replaced with Quiz average (after dropping 2 lowest quiz scores).
  • In class assignments/quizzes: 25%. We’ll have in class assignments/quizzes approximately once a week. Lowest 2 scores dropped. For the 10 students who do the best in the in class games that that we play throughout the semester, their remaining lowest quiz grade will be replaced with a 100.
  • Paper annotations and review: 15%. We’ll be reading and discussing research papers throughout the semester. Students will complete structured annotations for each paper, and will also write a longer review memo for one paper.
  • Participation and Attendance: 10%. Attend online/in-person class and have meaningful participation in the class community. Complete the occasional class survey, as well as the final official course evaluation.

Basis of grade determination

Letter grading. Grades will be at least as generous as the following, with A+ given at the instructor’s discretion.

  • A: 94-100
  • A-: 90-93
  • B+: 85-89
  • B: 80-84
  • C: 70-80
  • F: < 70

Assignment due dates

HW Assignments: 2/10, 3/3, 3/24, 4/14, 5/5

In class assignments/quizzes (tentantive schedule. Students are expected to attend all classes): 2/4, 2/11, 2/25, 3/4, 3/16, 3/18, 3/25, 4/15, 4/22, 4/29

Written paper review: 5/11, 5pm

Paper annotation days will be throughout the semester.

Assignment rubrics

Homeworks: Each problem component will be graded separately, in roughly 3 levels. 0 points = no attempt, partial points = attempt but incorrect, full points = mostly correct. Homeworks will occasionally have bonus questions graded on the same rubric.

In class Quizzes: Same grading as homeworks.

Participation: 80% of participation grade will be based on attendance, as judged by in class sign-ins. Students who attend 80% of the days with sign-ins will receive full credit for that component, with the grade falling linearly from there. Remainder of participation based on filling out class surveys, final course evaluation, and in class/EdStem/office hours participation.

Paper annotation and review: About half the grade will be based on completeness of annotations, and half on quality of the review memo. Rubric for review memo will be provided closer to the date.

Assessment

Primary assessment is based on 4-5 homeworks, about 10 in-class assignments, and annotations for papers and a paper review memo. We will drop your lowest homework grade. The in class assignments will be short and are primarily for educational purposes. They should be straightforward given lecture attendance and homework completion. Your lowest two in class assignment grades will be dropped. We will not have makeup quizzes except for official absences verified by student services or SDS.

You will be allowed five total late days during the semester for homeworks. These late days allow you to turn in homework up to 24 hours late, with no penalty; you may also use all the late days on a single homework assignment (allowing you to turn it in up to 5 days late), but that will leave you with no late days for other homeworks. Note that late days can only be used in whole number increments; you cannot use “half” of a late day. Gradescope automatically calculates when an assignment is late. When submitting an assignment late, please mark at the top how many late days are used for this assignment and how many you have used before, if any. Late days cannot be used for quizzes or the project – only homework.

Regrade, late assignments, and extensions policy

Research has shown that regrade requests lead to disparities due to differences in who is comfortable asking for regrades. Thus, regrade requests will only be accepted for clear, unambiguous errors in grading, such as when an assignment (or part of it) is marked missing when it was submitted on time in the manner instructed. Unambiguous errors on our part also include marking wrong an answer that is completely correct (method is clear, it’s the right thing to do, gets the right answer). However, it does not include judgement calls, such as whether a “right enough” answer should receive full credit. In the case of grading errors, however, we encourage you to come forward – please email the TA and instructor as soon as possible and submit a regrade request on gradescope, within one week of receiving the incorrect grade. For the same reason, we will not accept requests for extensions on assignments besides the late days policy above. We are hoping that between dropping the lowest scoring homework and quiz and the late days policy, you will have enough flexibility to accommodate your scheduling needs during the semester – without inducing disparities due to differences in who is comfortable asking for regrades and extensions.

For in-class quizzes, if you have an official excused absence reason (as verified by student services or SDS), we will either schedule a makeup quiz or simply grade your remaining quizzes. For unexcused absences, you will receive a 0 on the quiz (and so it’ll be dropped as one of your 2 lowest grade drops). Please communicate ahead of time and work with SDS (https://studentaffairs.tech.cornell.edu/student-life-2/student-disability-services/) if you have a reason to miss class. There will be no exceptions to this policy – only Cornell approved excused absences will be accommodated. If you require more flexibility for an unexcused absence, please use your 2 lowest grade drops.

Attendance policy

Don’t attend if you’re feeling ill, and we’ll figure it out from there. Otherwise, regular attendance is expected in class and grading will partially be based on class participation. Live remote attendance is not an option, and we will generally not be recording lectures and making them available.

Academic Integrity

Each student in this course is expected to abide by the Cornell University Code of Academic Integrity. Any work submitted by a student in this course for academic credit will be the student’s own work. The policy can be found on the university’s website here: https://theuniversityfaculty.cornell.edu/academic-integrity/.

You are encouraged to study together and to discuss information and concepts covered in lecture and the sections with other students. You can give “consulting” help to or receive “consulting” help from such students for homeworks and the class project. Except in the case of group homeworks or projects, this permissible cooperation should never involve one student having possession of a copy of all or part of work done by someone else. For group assignments, no group should have in their possession work done by another group. On the top of each assignment, please list everyone with whom you discussed the assignment. Class quizzes must be completed wholly independently, with no help of any kind between students.

Should copying occur, both the student(s) who copied work from another student(s) and the student(s) who gave material to be copied will both automatically receive a zero for the assignment. Penalty for violation of this Code can also be extended to include failure of the course and University disciplinary action.

During in class assignments, you must do your own work. Talking or discussion is not permitted, nor may you compare papers, copy from others, or collaborate in any way, or use any electronics. Any collaborative behavior during the examinations will result in failure of the exam, and may lead to failure of the course and University disciplinary action.

Academic Misconduct. A faculty member may impose a grade penalty for any misconduct in the classroom or examination room. Examples of academic misconduct include, but are not limited to, talking during an exam, bringing unauthorized materials into the exam room, and disruptive behavior in the classroom.

ChatGPT, Github Copilot, and LLM policy. Students are not allowed to use such tools, except when explicitly stated. We reserve the right to change this policy during the semester.

Students with Disabilities

Your access in this course is important to us. Please give us your Student Disability Services (SDS) accommodation letter early in the semester so that we have adequate time to arrange your approved academic accommodations. If you need immediate accommodations for equal access, please speak with us after class or send an email message to us and/or SDS at sds_cu@cornell.edu. If the need arises for additional accommodations during the semester, please contact SDS. You may also feel free to speak with the Student & Academic Affairs team at Cornell Tech who will connect you with the university SDS office. If you have, or think you may have a disability, please contact Student Disability Services for a confidential discussion: sds_cu@cornell.edu, 607-254-4545, sds.cornell.edu. You must request your SDS accommodation letter no later than 3 weeks prior to needing it.

  • Students currently registered with SDS: Once you request your accommodation letter and it is approved by SDS, it will be emailed to both you and your instructors. Processing time can be up to 48-hours.
  • Students not registered with SDS: The registration process for new accommodations can take up to three weeks. Once you are approved by SDS for accommodations, you will be able to request your accommodation letter for this course.
  • If you are approved for accommodations later in the semester: you must request your accommodation letter as soon as possible.

Religious Observances

Cornell University is committed to supporting students who wish to practice their religious beliefs. Students are advised to discuss religious absences with their instructors well in advance of the religious holiday so that arrangements for making up work can be resolved before the absence. Students are encouraged to anticipate their religious/spiritual needs early in the semester, and at least two weeks before the observance, leaving plenty of time for the professor and student to reach a reasonable accommodation.

Mental Health & Well-being

Your health and wellbeing are important to us, and you should always feel free to reach out to us for support. There are services and resources at Cornell designed specifically to bolster student mental health and well-being. Remember, your mental health and emotional well-being are just as important as your physical health. If you or a friend are struggling emotionally or feeling stressed, fatigued, or burned out, there are many campus resources available to you:

Cornell Tech students: This link provides a list of resources for Cornell Tech students: https://mentalhealth.cornell.edu/get-support/tech. You can additionally also contact studentwellness@tech.cornell.edu with concerns.

Academic Freedom and Building Trust in the Classroom

Each person in this class is expected to respect the principles of academic freedom for instructors and classmates and will maintain the privacy of the classroom environment. This commitment to building respect and trust in the classroom means members of this class will not: record, photograph, or share online any interactions that involve classmates or any member of the teaching team. Students will also respect the intellectual property rights of the instructor, and will not share or otherwise make accessible any course materials to anyone not enrolled in the course, without the instructor’s written permission. This policy is not meant to restrict students’ ability to use classroom recordings in ways beneficial to their learning. Students who may benefit from recorded lectures and lecture playback, including students who use English as an additional language or who have accommodations from SDS, should speak to the course instructor to maintain transparency and trust in the classroom. Students approved to record lectures are expected to maintain the respect and privacy of the learning environment, as stated above. Students will also not enable anyone not enrolled in the course to participate in any activity that is associated with the course. Exceptions to this require the instructor’s written permission.

Attestation

By registering for this class and accessing course materials through Canvas, students agree to abide by University, College, Department, and Course policies.