Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior
Proceedings of the 2nd ACM conference on Electronic commerce
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Coping with inaccurate reputation sources: experimental analysis of a probabilistic trust model
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Monopolizing markets by exploiting trust
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Eliciting Informative Feedback: The Peer-Prediction Method
Management Science
Design of a mechanism for promoting honesty in E-marketplaces
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
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In this paper, we develop a detailed bidding strategy for selling agents in electronic marketplaces, in a setting where buyers and sellers have incentives to be honest, due to a particular framework for trust modeling. In our mechanism, buyers model other buyers and select the most trustworthy ones as their neighbours to form a social network which can be used to ask advice about sellers. In addition, however, sellers model the reputation of buyers based on the social network. Reputable buyers provide fair ratings for sellers, and are likely to be neighbours of many other buyers. Sellers will provide more attractive products to reputable buyers, in order to build their own reputation. We include simulations of a dynamic marketplace operating using our mechanism, where buyers and sellers may come and go, and show that greater profit can be realized both for buyers that are honest and sellers that are honest.