Impediments to Universal preference-based default theories
Proceedings of the first international conference on Principles of knowledge representation and reasoning
A non-numeric approach to multi-criteria/multi-expert aggregation based on approximate reasoning
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Economic principles of multi-agent systems
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Communications of the ACM
Conditional, hierarchical, multi-agent preferences
TARK '98 Proceedings of the 7th conference on Theoretical aspects of rationality and knowledge
Proceedings of the 4th ACM conference on Electronic commerce
TARK '96 Proceedings of the 6th conference on Theoretical aspects of rationality and knowledge
Making markets and democracy work: a story of incentives and computing
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Complexity of mechanism design
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Ranking systems: the PageRank axioms
Proceedings of the 6th ACM conference on Electronic commerce
Trust-based recommendation systems: an axiomatic approach
Proceedings of the 17th international conference on World Wide Web
Game-theoretic recommendations: some progress in an uphill battle
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
An Analytic Approach to Reputation Ranking of Participants in Online Transactions
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Quantifying incentive compatibility of ranking systems
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
RATEWeb: Reputation Assessment for Trust Establishment among Web services
The VLDB Journal — The International Journal on Very Large Data Bases
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
On the axiomatic foundations of ranking systems
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Web Services Reputation Assessment Using a Hidden Markov Model
ICSOC-ServiceWave '09 Proceedings of the 7th International Joint Conference on Service-Oriented Computing
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)
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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 uncertainty and multi-agent systems. When the set of agents and the set of alternatives coincide, we get the so-called reputation systems setting. Famous types of reputation systems include page ranking in the context of search engines and traders ranking in the context of e-commerce. In this paper we present the first axiomatic study of reputation systems. We present three basic postulates that the desired/aggregated social ranking should satisfy and prove an impossibility theorem showing that no appropriate social ranking, satisfying all requirements, exists. Then we show that by relaxing any of these requirements an appropriate social ranking can be found. We first study reputation systems with (only) positive feedbacks. This setting refers to systems where agents' votes are interpreted as indications for the importance of other agents, as is the case in page ranking. Following this, we discuss the case of negative feedbacks, a most common situation in e-commerce settings, where traders may complain about the behavior of others. Finally, we discuss the case where both positive and negative feedbacks are available.