{"title":"社交网络分析:游戏实验","authors":"T. Ben-Zvi","doi":"10.1145/1807406.1807490","DOIUrl":null,"url":null,"abstract":"This study examines how early business relationships in company networks may predict later performance and centrality. We define a way of classifying centrality trajectories in social networks, providing a method that can be used more generally to predict network change over time. Employing a game simulation, we show that there are strategies that correlate with eventual centrality and profit, and other strategies that correlate with poor performance.","PeriodicalId":142982,"journal":{"name":"Behavioral and Quantitative Game Theory","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Social networks analysis: a game experiment\",\"authors\":\"T. Ben-Zvi\",\"doi\":\"10.1145/1807406.1807490\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study examines how early business relationships in company networks may predict later performance and centrality. We define a way of classifying centrality trajectories in social networks, providing a method that can be used more generally to predict network change over time. Employing a game simulation, we show that there are strategies that correlate with eventual centrality and profit, and other strategies that correlate with poor performance.\",\"PeriodicalId\":142982,\"journal\":{\"name\":\"Behavioral and Quantitative Game Theory\",\"volume\":\"39 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-05-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Behavioral and Quantitative Game Theory\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/1807406.1807490\",\"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 and Quantitative Game Theory","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1807406.1807490","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This study examines how early business relationships in company networks may predict later performance and centrality. We define a way of classifying centrality trajectories in social networks, providing a method that can be used more generally to predict network change over time. Employing a game simulation, we show that there are strategies that correlate with eventual centrality and profit, and other strategies that correlate with poor performance.