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dc.contributor.advisorItai Ashlagi.en_US
dc.contributor.authorShi, Peng, Ph. D. Massachusetts Institute of Technologyen_US
dc.contributor.otherMassachusetts Institute of Technology. Operations Research Center.en_US
dc.date.accessioned2016-10-25T19:18:03Z
dc.date.available2016-10-25T19:18:03Z
dc.date.copyright2016en_US
dc.date.issued2016en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/105002
dc.descriptionThesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2016.en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 243-250).en_US
dc.description.abstractIn this thesis, I study how data-driven optimization can be used to improve school choice. In a typical school choice system, each student receives a set of school options, called the student's menu. Based on his/her menu, each student submits a preference ranking of schools in the menu. Based on the submitted preferences, a centralized algorithm determines the assignment. In Boston, New York City, Chicago, Denver, New Orleans,Washington DC, among other cities, the assignment algorithm is the student-proposing deferred acceptance (DA) algorithm, which can also incorporate a priority for each student at each school. These priorities may contain a deterministic as well as a random component. An advantage of this algorithm is incentive-compatibility, meaning that no student has incentives to misreport his/her preferences. The first research question of this thesis is how to optimize the menus and priorities so that students have equitable chances to go to the schools they want, while the city's school busing costs are controlled. The second question is how the assignment algorithm can be modified to keep the same assignment probability of every student to every school, while improving neighbors' chances of going to the same school. To answer these questions, I build a multinomial logit (MNL) model to predict how students will rank schools under new menus, and validate the predictive accuracy of this model out of sample. I also propose a simple plan for menus and priorities, called the Home-Based plan, and compare with other proposals using the MNL model. (As a result of this analysis, the Home-Based plan was adopted by Boston in 2013.) I then show how one can further optimize the menus and priorities under the MNL model, by developing a new theoretical connection between stable matching and assortment planning, as well as methodologies on solving a new type of assortment planning problem, in which the objective is social welfare rather than revenue. Finally, I show how to further optimize the correlations between students' assignments to improve neighbors' chances of going to the same school.en_US
dc.description.statementofresponsibilityby Peng Shi.en_US
dc.format.extent250 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectOperations Research Center.en_US
dc.titlePrediction and optimization in school choiceen_US
dc.typeThesisen_US
dc.description.degreePh. D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Operations Research Center.en_US
dc.identifier.oclc960816448en_US


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