Ram Gopalan , Lee Hachadoorian , Steven O. Kimbrough , Frederic H. Murphy
{"title":"利用有效边界从大量可用规划中选择好的重新划分规划","authors":"Ram Gopalan , Lee Hachadoorian , Steven O. Kimbrough , Frederic H. Murphy","doi":"10.1016/j.omega.2023.103000","DOIUrl":null,"url":null,"abstract":"<div><p>As part of a widespread frustration with partisan gerrymandering, many states have considered or implemented redistricting reforms – and others will eventually have to – that include a higher degree of citizen participation in proposing and evaluating redistricting plans. In some states without redistricting reform, public interest groups have created shadow commissions that encourage citizens to submit their own maps. For example, the new map for Pennsylvania Congressional districts, chosen by the state Supreme Court, was proposed by a citizens group.</p><p>As citizen participation grows, analytical methods for rating plans that recognize the different mapping criteria are needed to sort through multiple maps, both for highlighting good maps and for providing measures that allow courts to rule that a map is gerrymandered. Using a modified version of a model called <em>data envelopment analysis</em> (DEA), we present a nonpartisan approach that can score maps while not imposing any prior weights on the criteria. Our modification measures how close a plan is to the convex hull of the Pareto frontier when bigger is better for some criteria and smaller is better for others. Thus, we provide a novel and scalable way to filter out poor plans from large corpora of redistricting plans.</p></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":null,"pages":null},"PeriodicalIF":6.7000,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Selecting good redistricting plans from a large pool of available plans using the efficient frontier\",\"authors\":\"Ram Gopalan , Lee Hachadoorian , Steven O. Kimbrough , Frederic H. Murphy\",\"doi\":\"10.1016/j.omega.2023.103000\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>As part of a widespread frustration with partisan gerrymandering, many states have considered or implemented redistricting reforms – and others will eventually have to – that include a higher degree of citizen participation in proposing and evaluating redistricting plans. In some states without redistricting reform, public interest groups have created shadow commissions that encourage citizens to submit their own maps. For example, the new map for Pennsylvania Congressional districts, chosen by the state Supreme Court, was proposed by a citizens group.</p><p>As citizen participation grows, analytical methods for rating plans that recognize the different mapping criteria are needed to sort through multiple maps, both for highlighting good maps and for providing measures that allow courts to rule that a map is gerrymandered. Using a modified version of a model called <em>data envelopment analysis</em> (DEA), we present a nonpartisan approach that can score maps while not imposing any prior weights on the criteria. Our modification measures how close a plan is to the convex hull of the Pareto frontier when bigger is better for some criteria and smaller is better for others. Thus, we provide a novel and scalable way to filter out poor plans from large corpora of redistricting plans.</p></div>\",\"PeriodicalId\":19529,\"journal\":{\"name\":\"Omega-international Journal of Management Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.7000,\"publicationDate\":\"2023-11-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Omega-international Journal of Management Science\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0305048323001640\",\"RegionNum\":2,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MANAGEMENT\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Omega-international Journal of Management Science","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0305048323001640","RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MANAGEMENT","Score":null,"Total":0}
Selecting good redistricting plans from a large pool of available plans using the efficient frontier
As part of a widespread frustration with partisan gerrymandering, many states have considered or implemented redistricting reforms – and others will eventually have to – that include a higher degree of citizen participation in proposing and evaluating redistricting plans. In some states without redistricting reform, public interest groups have created shadow commissions that encourage citizens to submit their own maps. For example, the new map for Pennsylvania Congressional districts, chosen by the state Supreme Court, was proposed by a citizens group.
As citizen participation grows, analytical methods for rating plans that recognize the different mapping criteria are needed to sort through multiple maps, both for highlighting good maps and for providing measures that allow courts to rule that a map is gerrymandered. Using a modified version of a model called data envelopment analysis (DEA), we present a nonpartisan approach that can score maps while not imposing any prior weights on the criteria. Our modification measures how close a plan is to the convex hull of the Pareto frontier when bigger is better for some criteria and smaller is better for others. Thus, we provide a novel and scalable way to filter out poor plans from large corpora of redistricting plans.
期刊介绍:
Omega reports on developments in management, including the latest research results and applications. Original contributions and review articles describe the state of the art in specific fields or functions of management, while there are shorter critical assessments of particular management techniques. Other features of the journal are the "Memoranda" section for short communications and "Feedback", a correspondence column. Omega is both stimulating reading and an important source for practising managers, specialists in management services, operational research workers and management scientists, management consultants, academics, students and research personnel throughout the world. The material published is of high quality and relevance, written in a manner which makes it accessible to all of this wide-ranging readership. Preference will be given to papers with implications to the practice of management. Submissions of purely theoretical papers are discouraged. The review of material for publication in the journal reflects this aim.