Lectures

You can download the lectures here.

This class is partially based on the following excellent courses and textbook, with many of the readings coming from their resources:

• Tim Roughgarden’s course, Incentives in Computer Science. Labeled as “R” in the suggested readings below.
• David Easley and Jon Kleinberg’s course/textbook Networks, Crowds, and Markets. Labeled as “EK” in the suggested readings below.

• 

  Lecture 1 - Course Introduction
  tl;dr: Course introduction.
  [slides]
  

  Reading R Lecture 1

  Optional Reading

  • Roth, Alvin E. “The Economist as Engineer: Game Theory, Experimentation, and Computation as Tools for Design Economics”
• 

  Lecture 2 - Intro to game theory
  tl;dr: Intro to game theory.
  [notes]
  

  Reading EK Chapter 6

• 

  Lecture 3 - Intro to mechanism design and auctions
  tl;dr: Intro to mechanism design and auctions.
  [slides] [notes]
  

  Reading R Lecture 3

  Optional Reading

  • EK Chapter 9
  • R Lecture 4
  • Credible Auctions: A Trilemma
• 

  Lecture 4 - Games and auctions continued
  tl;dr: Games and auctions continued.
  
  
• 

  Lecture 5 - Intro to centralized markets
  tl;dr: Intro to centralized markets.
  [slides] [notes]
  

  Reading R Lecture 2

  Optional Reading

  • Citation for the 2012 Nobel Prize (Roth and Shapley)
  • Market Design and Maintenance
• 

  Lecture 6 - Matching markets continued
  tl;dr: Matching markets.
  
  
• 

  Lecture 7 - Matching markets in practice
  tl;dr: Matching markets.
  
  

  Optional 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), 537-569
  • Modeling Assumptions Clash with the Real World: Transparency, Equity, and Community Challenges for Student Assignment Algorithms
• 

  Guest - Paul Gölz Guest Lecture
  tl;dr: Paul Gölz (Cornell ORIE).
  
  

  Title: TBD

  Abstract: TBD

  Bio: Paul Gölz is an assistant professor at Cornell University’s School of Operations Research and Information Engineering. His research spans computational social choice, fair division, and pluralistic AI alignment.

  Website: https://paulgoelz.de/

• 

  Monoculture in Matching Markets
  tl;dr: Monoculture in Matching Markets
  [slides]
  

  Optional Reading

  • Monoculture in Matching Markets
  • Correlated Errors in Large Language Models
  • Wisdom and Foolishness of Noisy Matching Markets
• 

  Lecture 8 - Intro to networks
  tl;dr: Intro to networks.
  [notes]
  

  Reading EK Chapter 2

  Optional Reading

  • Why Your Friends Have More Friends Than You Do
  • EK Chapter 3
  • EK Chapter 4
  • EK Chapter 5
• 

  Lecture 9 - Algorithms on graphs
  tl;dr: Algorithms on graphs.
  
  

  Optional Reading

  • The strength of weak ties. American Journal of Sociology, 78:1360–1380, 1973.
  • A causal test of the strength of weak ties
  • The Anatomy of the Facebook Social Graph
• 

  Lecture 10 - Incentives on networks
  tl;dr: Incentives on networks.
  [notes]
  

  Reading

  • R Lecture 7
  • Physical video of Braess’s paradox
• 

  Guest - Dave Rand Guest Lecture
  tl;dr: Dave Rand (Cornell IS).
  
  

  Website: https://davidrand-cooperation.com/home

• 

  Lecture 11 - Social choice -- voting
  tl;dr: Voting.
  [notes]
  
• 

  Lecture 12 - Social choice -- wisdom of crowds
  tl;dr: Wisdom of crowds.
  [notes]
  
• 

  Special Lecture (by Evan, Erica)
  tl;dr: Evan and Erica will discuss ML/recommendations in capacity constrained settings
  
  
• 

  Special Lecture (by Kenny, Sophie) -- various aspects of recommenders
  tl;dr: Kenny and Sophie will discuss various aspects of recommenders