职位(财务)
社会学习
社交网络(社会语言学)
社会网络分析
计算机科学
学习网络
业务
知识管理
人工智能
万维网
社会化媒体
财务
作者
Miriam E. Schwyck,Meng Du,Carolyn Parkinson
标识
DOI:10.31219/osf.io/xyqzb
摘要
Navigating our complex social lives requires understanding where others sit in our social networks. Some individuals may be more attuned to the structure of their social world, and thus, better able to learn new social networks due to having accumulated accurate priors about social network structure in their own lives. Correspondingly, such individuals may acquire more advantageous positions in their own social networks. In four studies (N = 1,768), brokers (people who connect otherwise disparate people in their own networks) were especially good at learning and remembering new networks that were structured like typical real-world social networks, but not unnaturally-structured ones, suggesting that brokers are attuned to the structure of real-world networks. Additionally, we found that brokers were able to learn networks better by focusing on ties that exist in those networks (as opposed to focusing on ties that were missing) and other brokers. We found no differences when the network was framed as a social network of friends or a non-social network of flights between airports. This work illuminates the mechanisms of network learning based on one’s own experiences, and establishes links between one’s own social network position and one’s ability to learn new networks.
科研通智能强力驱动
Strongly Powered by AbleSci AI