{"title":"网络游戏中的信念","authors":"W. Kets","doi":"10.2139/ssrn.1004279","DOIUrl":null,"url":null,"abstract":"Networks can have an important effect on economic outcomes. Given the complexity of many of these networks, agents will generally not know their structure. We study the sensitivity of game-theoretic predictions to the specification of players' (common) prior on the network in a setting where players play a fixed game with their neighbors and only have local information on the network structure. We show that two priors are close in a strategic sense if and only if (i) the priors assign similar probabilities to all events that involve a player and his neighbors, and (ii) with high probability, a player believes, given his type, that his neighbors' conditional beliefs are close under the two priors, and that his neighbors believe, given their type, that...the conditional beliefs of their neighbors are close, for any number of iterations.","PeriodicalId":343564,"journal":{"name":"Economics of Networks","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Beliefs in Network Games\",\"authors\":\"W. Kets\",\"doi\":\"10.2139/ssrn.1004279\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Networks can have an important effect on economic outcomes. Given the complexity of many of these networks, agents will generally not know their structure. We study the sensitivity of game-theoretic predictions to the specification of players' (common) prior on the network in a setting where players play a fixed game with their neighbors and only have local information on the network structure. We show that two priors are close in a strategic sense if and only if (i) the priors assign similar probabilities to all events that involve a player and his neighbors, and (ii) with high probability, a player believes, given his type, that his neighbors' conditional beliefs are close under the two priors, and that his neighbors believe, given their type, that...the conditional beliefs of their neighbors are close, for any number of iterations.\",\"PeriodicalId\":343564,\"journal\":{\"name\":\"Economics of Networks\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Economics of Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.1004279\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Economics of Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.1004279","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Networks can have an important effect on economic outcomes. Given the complexity of many of these networks, agents will generally not know their structure. We study the sensitivity of game-theoretic predictions to the specification of players' (common) prior on the network in a setting where players play a fixed game with their neighbors and only have local information on the network structure. We show that two priors are close in a strategic sense if and only if (i) the priors assign similar probabilities to all events that involve a player and his neighbors, and (ii) with high probability, a player believes, given his type, that his neighbors' conditional beliefs are close under the two priors, and that his neighbors believe, given their type, that...the conditional beliefs of their neighbors are close, for any number of iterations.