Efficiently and equitably assigning students to public schools
Throughout the United States, public school systems seek to incorporate student choices when assigning students to public schools. The design of such school choice systems has been widely studied in the theoretical literature, and academics and practitioners have together developed algorithms for centralized school choice that incentivize families to report truthfully while allowing both choice and district priorities to guide the overall assignment.
In this talk, I will give an overview of key issues that arise when operationalizing school choice algorithms, and provide technical and computational approaches for addressing some of these issues. In joint work with the San Francisco Unified School District we propose a new student assign policy using zones and other school choice policy levers to minimize travel distances while ensuring diversity. In other work, we consider school waitlists and reassignment, and propose, axiomatically justify, and optimize over a class of reassignment mechanisms. In simulations using NYC public school data we show that our optimal mechanism maintains efficiency while halving reassignment in simulations. I will end with a reflection on where there continue to be gaps between academic school choice mechanisms and practical implementation, and how we as academics can co-design technical solutions with our non-academic partners.
Irene Lo is an assistant professor in the department of Management Science & Engineering at Stanford University. Her research builds on tools from algorithms and economics to design matching markets and assignment processes, with a focus on public sector and non-profit applications. She is especially interested in resource allocation in education, the environment, and the developing world. She is currently one of the program chairs for the 1st ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO ’21), and was a co-organizer of the Mechanism Design for Social Good (MD4SG) research initiative.