REGRET: reputation in gregarious societies
Proceedings of the fifth international conference on Autonomous agents
Analyzing the economic efficiency of eBay-like online reputation reporting mechanisms
Proceedings of the 3rd ACM conference on Electronic Commerce
A Learning Algorithm for Buying and Selling Agents in Electronic Marketplaces
AI '02 Proceedings of the 15th Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
AAMAS '02 Revised Papers from the Workshop on Agent Mediated Electronic Commerce on Agent-Mediated Electronic Commerce IV, Designing Mechanisms and Systems
A reputation system for peer-to-peer networks
NOSSDAV '03 Proceedings of the 13th international workshop on Network and operating systems support for digital audio and video
Collaborative Reputation Mechanisms in Electronic Marketplaces
HICSS '99 Proceedings of the Thirty-second Annual Hawaii International Conference on System Sciences-Volume 8 - Volume 8
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Review on Computational Trust and Reputation Models
Artificial Intelligence Review
Avoiding ballot stuffing in eBay-like reputation systems
Proceedings of the 2005 ACM SIGCOMM workshop on Economics of peer-to-peer systems
Task delegation using experience-based multi-dimensional trust
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Towards provably secure trust and reputation systems in e-marketplaces
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Smart cheaters do prosper: defeating trust and reputation systems
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Protecting buying agents in e-marketplaces by direct experience trust modelling
Knowledge and Information Systems
TREET: the Trust and Reputation Experimentation and Evaluation Testbed
Electronic Commerce Research
Trust evaluation initialization using contextual information
Proceedings of the International Conference on Management of Emergent Digital EcoSystems
Defining trust evidence: research directions
Proceedings of the Seventh Annual Workshop on Cyber Security and Information Intelligence Research
Addressing common vulnerabilities of reputation systems for electronic commerce
Journal of Theoretical and Applied Electronic Commerce Research
A dynamic reputation system with built-in attack resilience to safeguard buyers in e-market
ACM SIGSOFT Software Engineering Notes
Macau: a basis for evaluating reputation systems
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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In large electronic marketplaces populated by buying and selling agents, repeated transactions between traders may be rare. This makes it difficult for buying agents to judge the reliability of selling agents, discouraging participation in the market. A variety of systems have been proposed to help traders to find trustworthy partners; however, most proposed systems suffer from multiple vulnerabilities that might be exploited by unscrupulous parties. In this paper, we propose a new model, wherein abstract units are used to represent trust in much the same way that units of money represent value. In a manner similar to money, 'trunits' flow during transactions. A trader's trunit balance determines if they are trustworthy for a given transaction. Faithful execution of a transaction results in a larger trunit balance, permitting the trader to engage in more transactions in the future---a built-in economic incentive for honesty. We demonstrate that for a wide range of realistic market parameters, the Trunits mechanism ensures that honest sellers profit more than dishonest sellers. We also discuss how intrinsic properties of our model make it secure from many of the attacks to which other systems are vulnerable. In summary, we present our Trunits model as the basis for modeling trust in electronic marketplaces, useful as buying agents develop algorithms to intelligently choose trustworthy sellers for their business partners.