{"title":"网络结构与社会学习","authors":"Krishna Dasaratha, Kevin He","doi":"10.1145/3505156.3505163","DOIUrl":null,"url":null,"abstract":"We describe results from Dasaratha and He [DH21a] and Dasaratha and He [DH20] about how network structure influences social learning outcomes. These papers share a tractable sequential model that lets us compare learning dynamics across networks. With Bayesian agents, incomplete networks can generate informational confounding that makes learning arbitrarily inefficient. With naive agents, related forces can lead to mislearning.","PeriodicalId":56237,"journal":{"name":"ACM SIGecom Exchanges","volume":"19 1","pages":"62 - 67"},"PeriodicalIF":0.6000,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Network structure and social learning\",\"authors\":\"Krishna Dasaratha, Kevin He\",\"doi\":\"10.1145/3505156.3505163\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We describe results from Dasaratha and He [DH21a] and Dasaratha and He [DH20] about how network structure influences social learning outcomes. These papers share a tractable sequential model that lets us compare learning dynamics across networks. With Bayesian agents, incomplete networks can generate informational confounding that makes learning arbitrarily inefficient. With naive agents, related forces can lead to mislearning.\",\"PeriodicalId\":56237,\"journal\":{\"name\":\"ACM SIGecom Exchanges\",\"volume\":\"19 1\",\"pages\":\"62 - 67\"},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2021-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM SIGecom Exchanges\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3505156.3505163\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM SIGecom Exchanges","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3505156.3505163","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
We describe results from Dasaratha and He [DH21a] and Dasaratha and He [DH20] about how network structure influences social learning outcomes. These papers share a tractable sequential model that lets us compare learning dynamics across networks. With Bayesian agents, incomplete networks can generate informational confounding that makes learning arbitrarily inefficient. With naive agents, related forces can lead to mislearning.
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