{"title":"Objective Weights for Scoring: The Automatic Democratic Method","authors":"Chris Tofallis","doi":"arxiv-2409.02087","DOIUrl":null,"url":null,"abstract":"When comparing performance (of products, services, entities, etc.), multiple\nattributes are involved. This paper deals with a way of weighting these\nattributes when one is seeking an overall score. It presents an objective\napproach to generating the weights in a scoring formula which avoids personal\njudgement. The first step is to find the maximum possible score for each\nassessed entity. These upper bound scores are found using Data Envelopment\nAnalysis. In the second step the weights in the scoring formula are found by\nregressing the unique DEA scores on the attribute data. Reasons for using least\nsquares and avoiding other distance measures are given. The method is tested on\ndata where the true scores and weights are known. The method enables the\nconstruction of an objective scoring formula which has been generated from the\ndata arising from all assessed entities and is, in that sense, democratic.","PeriodicalId":501188,"journal":{"name":"arXiv - ECON - Theoretical Economics","volume":"43 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - ECON - Theoretical Economics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.02087","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
When comparing performance (of products, services, entities, etc.), multiple
attributes are involved. This paper deals with a way of weighting these
attributes when one is seeking an overall score. It presents an objective
approach to generating the weights in a scoring formula which avoids personal
judgement. The first step is to find the maximum possible score for each
assessed entity. These upper bound scores are found using Data Envelopment
Analysis. In the second step the weights in the scoring formula are found by
regressing the unique DEA scores on the attribute data. Reasons for using least
squares and avoiding other distance measures are given. The method is tested on
data where the true scores and weights are known. The method enables the
construction of an objective scoring formula which has been generated from the
data arising from all assessed entities and is, in that sense, democratic.
在比较(产品、服务、实体等)性能时,会涉及多个属性。本文探讨了在寻求总体得分时如何对这些属性进行加权。它提出了一种在评分公式中生成权重的客观方法,避免了个人判断。第一步是为每个被评估实体找到可能的最高得分。这些上限分数是通过数据包络分析法找到的。第二步,通过对属性数据的独特 DEA 分数进行回归,找到评分公式中的权重。给出了使用最小二乘法而避免使用其他距离测量方法的原因。该方法在已知真实得分和权重的数据上进行了测试。该方法能够构建一个客观的评分公式,该公式由所有被评估实体的数据生成,从这个意义上说是民主的。