Engineering Societal Systems / Spring 2022


  • 01/20 -- New Lecture is up: Lecture 2 - Great ideas in market design [slides]
  • 01/20 -- New Lecture is up: Lecture 1 - Course Introduction [slides]

Course Description

This class will explore topics in the intersection of computer science, economics, and operations -- on the application of algorithms, data science/machine learning, and mechanism design to the study of democracy, markets, and societal systems at large. Methodological tools discussed include both theoretical (mechanism/market design, social choice, pricing, matching, etc) and empirical (large-scale optimization, demand estimation, using governmental data, etc). Application domains could include markets (labor, accommodation, services, rides), governmental (transportation, democracy) and other non-profit settings (education, housing, humanitarian, volunteer coordination, food allocation).

Important links

See the syllabus for details. The class will be fully remote, with students split across Ithaca and NYC.

Course topics

  • Introduction and overview (~1 weeks)
    • What is market design? What are the tools used?
    • Overview of classic papers/ideas
    • How do we analyze existing systems? What is the role of an engineer/computing in understanding societal systems? How do we design new systems? Where has engineering of societal systems most succeeded? How do we choose an appropriate objective function?
  • Transportation systems as a representative case study (~2-3 weeks)
    • Transportation marketplaces: pricing, matching
    • Public transportation: school bus routing, stop placement, shared vehicles
    • Congestion pricing
  • Online marketplaces more generally (~2 weeks)
    • Pricing, matching, reputation systems, recommendations
  • Crowdsourcing, social choice, information design (~2 weeks)
    • Wisdom of crowds, herding, information design
    • Voting in complicated spaces (rankings, participatory budgeting)
    • Optimization + voting (gerrymandering, sortition)
    • Social choice + machine learning
  • Education systems (~2 weeks)
    • School choice (matching + recommendations), school zone design (optimization)
    • Designing admissions systems
  • Miscellaneous methodologies and applications
  • Limits of technical approaches (~1 week)
    • What are the limits to engineering methodologies?
    • How do we incorporate qualitative methods?