Collaborative Research: High-Performance Computational Standards For Redistricting

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General AbstractThis project develops computational tools that objectively evaluate redistricting plans, and automate the creation of redistricting plans to satisfy particular criteria selected by users. The tool will provide a mechanism for decision-makers to use when negotiating redistricting plans, eliminating the inherent bias that arises when the data and the ability to propose plans are available to only a few political interests. The project will entail multiple elements, namely: formulate the redistricting problem as a discrete optimization problem, introduce quantitative measurements to score maps on a wide set of criteria, create novel optimization algorithms customized for the redistricting problem to identify maps that score well on given criteria, and create a computational tool that allows states, individuals, and political parties to negotiate redistricting plans. In addition to the development of the computational tool, this project will engage in a detailed study of how to use computational models to shed new substantive insight and aid in the creation of fairness standards in the American redistricting process. Such standards have been elusive despite decades of effort. The broader impact of the work seeks to transform the upcoming future redistricting rounds by opening it up to participation to a broader and more diverse group of stakeholders. Likewise the tool will provide greater flexibility and enhanced capabilities for developing redistricting plans than ever before. In the research realm, the algorithm development will also be applicable to large-scale optimization problems that utilize massively parallel computing architecture. The project also contributes to graduate education, providing instruction about the application of computational approaches to an array of social scientific questions.Technical AbstractThe contributions of this work span a variety of disciplines including political science, law, computer science, math, operations research, and supercomputing. In the computer science and supercomputing realm, the research will tune and enable a parallel genetic algorithm library to scale to hundreds of thousands of processors. The algorithm advances operations research heuristics for large combinatorial optimization problems. The implementation is a hybrid metaheuristic that combines the search capabilities of evolutionary algorithms with refinements for diversification and intensification to empower a more efficient and effective search process. The mathematical approach yields new quantitative measures of political phenomenon. In political science and law, the project will create a new ability to synthesize and analyze massive amounts of data that will yield new substantive insights about fairness standards for redistricting as well as the effect and impact of redistricting on the democratic process.