Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Communications of the ACM
Reputation systems: an axiomatic approach
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Link analysis ranking: algorithms, theory, and experiments
ACM Transactions on Internet Technology (TOIT)
Ranking systems: the PageRank axioms
Proceedings of the 6th ACM conference on Electronic commerce
Sybilproof reputation mechanisms
Proceedings of the 2005 ACM SIGCOMM workshop on Economics of peer-to-peer systems
On the axiomatic foundations of ranking systems
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Trust-based recommendation systems: an axiomatic approach
Proceedings of the 17th international conference on World Wide Web
Strategyproof deterministic lotteries under broadcast communication
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
WINE '08 Proceedings of the 4th International Workshop on Internet and Network Economics
Axiomatic foundations for ranking systems
Journal of Artificial Intelligence Research
An axiomatic approach to personalized ranking systems
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Consistent Continuous Trust-Based Recommendation Systems
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
An axiomatic approach to personalized ranking systems
Journal of the ACM (JACM)
Hybrid transitive trust mechanisms
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
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Reasoning about agent preferences on a set of alternatives, and the aggregation of such preferences into some social ranking is a fundamental issue in reasoning about multi-agent systems. When the set of agents and the set of alternatives coincide, we get the ranking systems setting. A famous type of ranking systems are page ranking systems in the context of search engines. Such ranking systems do not exist in empty space, and therefore agents' incentives should be carefully considered. In this paper we define three measures for quantifying the incentive compatibility of ranking systems. We apply these measures to several known ranking systems, such as PageRank, and prove tight bounds on the level of incentive compatibility under two basic properties: strong monotonicity and non-imposition. We also introduce two novel nonimposing ranking systems, one general, and the other for the case of systems with three participants. A full axiomatization is provided for the latter.