IPTPS '01 Revised Papers from the First International Workshop on Peer-to-Peer Systems
Sybilproof reputation mechanisms
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On threshold behavior in query incentive networks
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Lottery trees: motivational deployment of networked systems
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Characterizing false-name-proof allocation rules in combinatorial auctions
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Optimal false-name-proof voting rules with costly voting
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Pseudonyms in Cost-Sharing Games
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
Truthful and Quality Conscious Query Incentive Networks
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
Threshold behavior of incentives in social networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Mechanisms for multi-level marketing
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Finding red balloons with split contracts: robustness to individuals' selfishness
STOC '12 Proceedings of the forty-fourth annual ACM symposium on Theory of computing
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Simpler sybil-proof mechanisms for multi-level marketing
Proceedings of the 13th ACM Conference on Electronic Commerce
Sybil-proof mechanisms in query incentive networks
Proceedings of the fourteenth ACM conference on Electronic commerce
Sybil-proof mechanisms in query incentive networks
Proceedings of the fourteenth ACM conference on Electronic commerce
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In this paper, we study incentive mechanisms for retrieving information from networked agents. Following the model in [Kleinberg and Raghavan 2005], the agents are represented as nodes in an infinite tree, which is generated by a random branching process. A query is issued by the root, and each node possesses an answer with an independent probability p=1/n. Further, each node in the tree acts strategically to maximize its own payoff. In order to encourage the agents to participate in the information acquisition process, an incentive mechanism is needed to reward agents who provide the information as well as agents who help to facilitate such acquisition. We focus on designing efficient sybil-proof incentive mechanisms, i.e., which are robust to fake identity attacks. We propose a family of mechanisms, called the direct referral (DR) mechanisms, which allocate most reward to the information holder as well as its direct parent (or direct referral). We show that, when designed properly, the direct referral mechanism is sybil-proof and efficient. In particular, we show that we may achieve an expected cost of O(h2) for propagating the query down h levels for any branching factor b1. This result exponentially improves on previous work when requiring to find an answer with high probability. When the underlying network is a deterministic chain, our mechanism is optimal under some mild assumptions. In addition, due to its simple reward structure, the DR mechanism might have good chance to be adopted in practice.