{"title":"计算信任模型在理性环境中的有效使用","authors":"Le-Hung Vu, K. Aberer","doi":"10.1145/2019591.2019593","DOIUrl":null,"url":null,"abstract":"Reputation-based trust models using statistical learning have been intensively studied for distributed systems where peers behave maliciously. However practical applications of such models in environments with both malicious and rational behaviors are still very little understood. This paper studies the relation between accuracy of a computational trust model and its ability to effectively enforce cooperation among rational agents. We provide theoretical results showing under which conditions cooperation emerges when using a trust learning algorithms with given accuracy and how cooperation can be still sustained while reducing cost and accuracy of those algorithms. We then verify and extend these theoretical results to a variety of settings involving honest, malicious and strategic players through extensive simulation. These results will enable a much more targeted, cost-effective and realistic design for decentralized trust management systems, such as needed for peer-to-peer systems and electronic commerce.","PeriodicalId":393772,"journal":{"name":"2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2011-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Effective Usage of Computational Trust Models in Rational Environments\",\"authors\":\"Le-Hung Vu, K. Aberer\",\"doi\":\"10.1145/2019591.2019593\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Reputation-based trust models using statistical learning have been intensively studied for distributed systems where peers behave maliciously. However practical applications of such models in environments with both malicious and rational behaviors are still very little understood. This paper studies the relation between accuracy of a computational trust model and its ability to effectively enforce cooperation among rational agents. We provide theoretical results showing under which conditions cooperation emerges when using a trust learning algorithms with given accuracy and how cooperation can be still sustained while reducing cost and accuracy of those algorithms. We then verify and extend these theoretical results to a variety of settings involving honest, malicious and strategic players through extensive simulation. These results will enable a much more targeted, cost-effective and realistic design for decentralized trust management systems, such as needed for peer-to-peer systems and electronic commerce.\",\"PeriodicalId\":393772,\"journal\":{\"name\":\"2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2019591.2019593\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2019591.2019593","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Effective Usage of Computational Trust Models in Rational Environments
Reputation-based trust models using statistical learning have been intensively studied for distributed systems where peers behave maliciously. However practical applications of such models in environments with both malicious and rational behaviors are still very little understood. This paper studies the relation between accuracy of a computational trust model and its ability to effectively enforce cooperation among rational agents. We provide theoretical results showing under which conditions cooperation emerges when using a trust learning algorithms with given accuracy and how cooperation can be still sustained while reducing cost and accuracy of those algorithms. We then verify and extend these theoretical results to a variety of settings involving honest, malicious and strategic players through extensive simulation. These results will enable a much more targeted, cost-effective and realistic design for decentralized trust management systems, such as needed for peer-to-peer systems and electronic commerce.