{"title":"观察学习下的最优影响","authors":"Nikolas Tsakas","doi":"10.2139/ssrn.2449420","DOIUrl":null,"url":null,"abstract":"We study the optimal targeting problem of a firm that seeks to maximize the diffusion of a product in a society where agents learn from their neighbors. The firm can seed the product to a subset of the population and our goal is to find which is the optimal subset to target. We provide a condition that characterizes the optimal targeting strategy for any network structure. The key parameter in this condition is the agents' decay centrality, which takes into account how close an agent is to others, in a way that distant agents are weighted less than closer ones.","PeriodicalId":443127,"journal":{"name":"Behavioral Marketing eJournal","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Optimal Influence Under Observational Learning\",\"authors\":\"Nikolas Tsakas\",\"doi\":\"10.2139/ssrn.2449420\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We study the optimal targeting problem of a firm that seeks to maximize the diffusion of a product in a society where agents learn from their neighbors. The firm can seed the product to a subset of the population and our goal is to find which is the optimal subset to target. We provide a condition that characterizes the optimal targeting strategy for any network structure. The key parameter in this condition is the agents' decay centrality, which takes into account how close an agent is to others, in a way that distant agents are weighted less than closer ones.\",\"PeriodicalId\":443127,\"journal\":{\"name\":\"Behavioral Marketing eJournal\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-11-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Behavioral Marketing eJournal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.2449420\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Behavioral Marketing eJournal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.2449420","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We study the optimal targeting problem of a firm that seeks to maximize the diffusion of a product in a society where agents learn from their neighbors. The firm can seed the product to a subset of the population and our goal is to find which is the optimal subset to target. We provide a condition that characterizes the optimal targeting strategy for any network structure. The key parameter in this condition is the agents' decay centrality, which takes into account how close an agent is to others, in a way that distant agents are weighted less than closer ones.