Schedule
We will aim to fill in lecture topics at least 1 week in advance. Assignment due dates are final, unless there are exceptional unforeseen circumstances.

EventDateDescriptionCourse Material

Lecture01/22
MondayLecture 1  Course Introduction[slides] 
Assignment01/23
TuesdayHomework #1  Game theory and matching markets released! 
Lecture01/24
WednesdayLecture 2  Intro to game theoryReading EK Chapter 6

Lecture01/29
MondayLecture 3  Intro to mechanism design and auctions[slides] [handwritten] 
Lecture01/31
WednesdayLecture 4  Games and auctions continuedReading R Lecture 2
Optional Reading

Lecture02/05
MondayLecture 5  Intro to centralized markets[slides] [handwritten] 
Due02/05 02:55 ET
MondayIn class quiz: games and mechanisms 
Lecture02/07
WednesdayLecture 6  Matching markets continued 
Lecture02/12
MondayLecture 7  Matching markets in practiceOptional Reading
 The Redesign of the Matching Market for American Physicians: Some Engineering Aspects of Economic Design
 The New York City High School Match
 Deferred acceptance algorithms: History, theory, practice, and open questions, international Journal of game Theory 36 (3), 537569
 Modeling Assumptions Clash with the Real World: Transparency, Equity, and Community Challenges for Student Assignment Algorithms

Due02/12 02:55 ET
MondayIn class quiz: games and mechanisms 2 
Lecture02/14
WednesdayGuest 1  Parag Pathak Guest Lecture 
Assignment02/14
WednesdayHomework #2  Matching and Networks released! 
Due02/15 23:59 ET
ThursdayHomework #1 due 
Lecture02/19
MondayLecture 8  Intro to networksReading EK Chapter 2
Optional Reading

Due02/19 02:55 ET
MondayIn class quiz: matching markets 
Lecture02/21
WednesdayLecture 9  Algorithms on graphs 
Lecture02/26
MondayNO CLASS FEBRUARY BREAK 
Lecture02/28
WednesdayLecture 10  Incentives on networks 
Lecture03/04
MondayStudent paper presentations 
Due03/04 02:55 ET
MondayIn class quiz: networks 
Lecture03/06
WednesdayGuest 2  AnaAndreea Stoica Guest LectureTitle: Algorithm design for social good: fair design and strategic interactions
Abstract: In this talk, I will present my recent work on social aspects of algorithm design, encompassing two lines of work. In one line of work, we study methods for diagnosing when and why an algorithm is biased against societal minority groups, with applications to ranking algorithms and social influence. In another line of work, we study the impact of strategic interactions in social contexts, with a focus on precise error estimation of treatment effects in the presence of competition. In this talk, I will present theoretical challenges in designing algorithms that (1) do not amplify bias against minority groups and (2) are aware of strategic feedback from a population that aims to maximize its utility.
Papers:
 Stoica, AnaAndreea, Nelly Litvak, and Augustin Chaintreau. “Fairness Rising from the Ranks: HITS and PageRank on Homophilic Networks.” To appear at The Web Conf’24.
 Stoica, AnaAndreea, Jessy Xinyi Han, and Augustin Chaintreau. “Seeding network influence in biased networks and the benefits of diversity.” In Proceedings of The Web Conference 2020, pp. 20892098. 2020.

Due03/07 23:59 ET
ThursdayHomework #2 due 
Assignment03/08
FridayHomework #3  Networks released! 
Lecture03/11
MondayStudent paper presentations 
Due03/11 02:55 ET
MondayIn class quiz 
Lecture03/13
WednesdayStudent paper presentations 
Lecture03/18
MondayStudent paper presentations 
Lecture03/20
WednesdayStudent paper presentations 
Due03/20 02:55 ET
WednesdayIn class quiz 
Lecture03/27
WednesdayGuest 3  Manish Raghavan Guest Lecture 
Assignment03/27
WednesdayHomework #4  Miscellaneous released! 
Due03/28 23:59 ET
ThursdayHomework #3 due 
Due04/08 02:55 ET
MondayIn class quiz 
Lecture04/10
WednesdayGuest 4  Meena Jagadeesan Guest Lecture 
Lecture04/15
MondayStudent paper presentations 
Lecture04/17
WednesdayGuest 5  Akshaya Suresh Guest LectureTitle  Redesigning Recommendation on VolunteerMatch: Theory and Practice
Abstract – This work is the result of a multicollaboration with VolunteerMatch (VM), the largest platform in the United States connecting volunteers to organizations, to help them redesign their recommendation system. VM has helped to facilitate over 10 million connections and would ideally like to help all of its organizations find enough connections to fulfill their needs. However, in practice many opportunities for volunteering get no connections and there is a significant number of “wasted” or excess connections. Reallocating interest from opportunities with too many volunteers to accommodate towards those not getting enough can significantly improve welfare for all agents. We studied how VM can improve its recommendation system to accomplish this reallocation and maximize useful signups. Our contributions are both theoretical and applied. On the theory side, we model a key feature of many online platforms – multi channel traffic – and design a new algorithm for this context with provable guarantees that are nearoptimal in some regimes. On the applied side, we designed and implemented a new recommendation algorithm, SmartSort, which led to significant improvements in equity on VM’s platform and provided insights into the equityefficiency tradeoff in the context of VM.
Bio Akshaya Suresh is PhD candidate in Operations at Yale University at the School of Management (SOM). Her research uses tools from statistical modeling and datadriven optimization for nonprofit and public sector applications, including collaborations with partners like VolunteerMatch, the Florida Center for Reading Research, and the New Haven Lexinome Project. Prior to attending Yale SOM, Akshaya received a BS in Astrophysics from Yale University, and an MA in Social Science from the University of Chicago. She has professional experience in data science consulting and public policy, and served as a Presidential Management Fellow at the Internal Revenue Service and the Department of Housing and Urban Development. In her free time, she enjoys needle crafts, learning new languages, and playing violin.

Assignment04/18
ThursdayHomework #5 released! 
Due04/18 23:59 ET
ThursdayHomework #4 due 
Lecture04/22
MondayStudent paper presentations 
Due04/22 02:55 ET
MondayIn class quiz 
Lecture04/24
WednesdayStudent paper presentations 
Lecture04/29
MondayStudent paper presentations 
Lecture05/01
WednesdayGerrymandering and multiwinner elections[slides] 
Due05/01 02:55 ET
WednesdayIn class quiz 
Due05/07 23:59 ET
TuesdayHomework #5 due