Contextual Stochastic Optimization for School Desegregation Policymaking
Hongzhao Guan, Nabeel Gillani, Tyler Simko, Jasmine Mangat, Pascal Van Hentenryck
Abstract
Most US school districts draw geographic "attendance zones" to assign children to schools based on their home address, a process that can replicate existing neighborhood racial/ethnic and socioeconomic status (SES) segregation in schools. Redrawing boundaries can reduce segregation, but estimating expected rezoning impacts is often challenging because families can opt-out of their assigned schools. This paper seeks to alleviate this societal problem by developing a joint redistricting and choice modeling framework, called redistricting with choices (RWC). The RWC framework is applied to a large US public school district to estimate how redrawing elementary school boundaries might realistically impact levels of socioeconomic segregation. The main methodological contribution of RWC is a contextual stochastic optimization model that aims to minimize district-wide segregation by integrating rezoning constraints with a machine learning-based school choice model. The study finds that RWC yields boundary changes that might reduce segregation by a substantial amount (23%) -- but doing so might require the re-assignment of a large number of students, likely to mitigate re-segregation that choice patterns could exacerbate. The results also reveal that predicting school choice is a challenging machine learning problem. Overall, this study offers a novel practical framework that both academics and policymakers might use to foster more diverse and integrated schools.
BibTeX
@article{Guan_Gillani_Simko_Mangat_Van Hentenryck_2025, title={Contextual Stochastic Optimization for School Desegregation Policymaking}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/35020}, DOI={10.1609/aaai.v39i27.35020}, abstractNote={Most US school districts draw geographic "attendance zones" to
assign children to schools based on their home address, a process that
can replicate existing neighborhood racial/ethnic and socioeconomic
status (SES) segregation in schools. Redrawing boundaries can reduce
segregation, but estimating expected rezoning impacts is often challenging because
families can opt-out of their assigned schools. This paper seeks to alleviate this societal problem by developing a joint
redistricting and choice modeling framework, called redistricting
with choices (RWC). The RWC framework is applied to a large US
public school district to estimate how redrawing elementary school
boundaries might realistically impact levels of
socioeconomic segregation. The main methodological contribution of
RWC is a contextual stochastic optimization model that aims to minimize
district-wide segregation by integrating rezoning constraints
with a machine learning-based school choice model. The study finds that RWC
yields boundary changes that might reduce segregation by a substantial
amount (23%) -- but doing so might require the re-assignment of a
large number of students, likely to mitigate re-segregation that
choice patterns could exacerbate. The results also reveal that
predicting school choice is a challenging machine learning problem.
Overall, this study offers a novel practical framework that both
academics and policymakers might use to foster more diverse and
integrated schools.}, number={27}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Guan, Hongzhao and Gillani, Nabeel and Simko, Tyler and Mangat, Jasmine and Van Hentenryck, Pascal}, year={2025}, month={Apr.}, pages={28024-28032} }