Vincent Conitzer, Rupert Freeman, Markus Brill, Yuqian Li
{"title":"Rules for Choosing Societal Tradeoffs","authors":"Vincent Conitzer, Rupert Freeman, Markus Brill, Yuqian Li","doi":"10.1609/aaai.v30i1.10055","DOIUrl":null,"url":null,"abstract":"\n \n We study the societal tradeoffs problem, where a set of voters each submit their ideal tradeoff value between each pair of activities (e.g., \"using a gallon of gasoline is as bad as creating 2 bags of landfill trash\"), and these are then aggregated into the societal tradeoff vector using a rule. We introduce the family of distance-based rules and show that these can be justified as maximum likelihood estimators of the truth. Within this family, we single out the logarithmic distance-based rule as especially appealing based on a social-choice-theoretic axiomatization. We give an efficient algorithm for executing this rule as well as an approximate hill climbing algorithm, and evaluate these experimentally.\n \n","PeriodicalId":354113,"journal":{"name":"International Symposium on Artificial Intelligence and Mathematics","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Symposium on Artificial Intelligence and Mathematics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1609/aaai.v30i1.10055","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11
Abstract
We study the societal tradeoffs problem, where a set of voters each submit their ideal tradeoff value between each pair of activities (e.g., "using a gallon of gasoline is as bad as creating 2 bags of landfill trash"), and these are then aggregated into the societal tradeoff vector using a rule. We introduce the family of distance-based rules and show that these can be justified as maximum likelihood estimators of the truth. Within this family, we single out the logarithmic distance-based rule as especially appealing based on a social-choice-theoretic axiomatization. We give an efficient algorithm for executing this rule as well as an approximate hill climbing algorithm, and evaluate these experimentally.