Mechanisms, Optimization, and Education Policy in Student Assignment Systems

Explore a plenary talk that examines the intersection of algorithms, mechanisms, and optimization in education policy through a detailed case study of San Francisco's student assignment system. Learn how mathematical approaches were applied to design a new school assignment policy for the San Francisco Unified School District (SFUSD) between 2018-2020, focusing on achieving diversity, predictability, and proximity goals. Discover how optimization methods for redistricting were utilized to suggest potential zones, and understand the development of an end-to-end simulation tool for policy evaluation. Examine findings that demonstrate how zone-based systems with minority reserves can successfully meet district objectives, while analyzing the impact of choice on zone diversity. Gain insights into how this research influenced SFUSD's adoption of a zone-based policy scheduled for implementation in the 2026-27 school year.