Meeting number: 120 058 1201
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Optimization Modeling and Algorithms For Politically Fair Redistricting
Abstract: Political districting in the U.S. is a decennial process of drawing legislative districts for political representation. Each district is a contiguous set of geographical units (such as census tracts) that elects a representative. As an optimization problem, districting is computationally intractable for large practical instances. This talk presents a class of approximation algorithms, called multilevel algorithms, for large-scale districting. Here, political fairness criteria such as the efficiency gap, partisan asymmetry, competitiveness and compactness are optimized using a multi-objective optimization method. The practical implications of adopting the algorithm for real-world districting is presented with a case study in Wisconsin. The results demonstrate the positive role of approximation algorithms in districting, and paves the way for algorithmic transparency in the future of electoral representation.
Bio: Rahul Swamy is a Ph.D. Candidate in Industrial Engineering (Operations Research) at the University of Illinois Urbana-Champaign. His research focus is on combinatorial optimization and game theory which spans applications such as political redistricting, election modeling, evacuation planning, and team formation in online health forums. His work on political redistricting has been recognized by First Place in the INFORMS Service Science Best Paper Award 2019, First place in the INFORMS Poster Competition 2019, and a Finalist at the INFORMS Public Sector Operations Research (PSOR) Best Paper Award 2018.
Seminars from 2020.