Propagation of trust and distrust
Proceedings of the 13th international conference on World Wide Web
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
On threshold behavior in query incentive networks
Proceedings of the 8th ACM conference on Electronic commerce
Generalized scoring rules and the frequency of coalitional manipulability
Proceedings of the 9th ACM conference on Electronic commerce
Eliciting properties of probability distributions
Proceedings of the 9th ACM conference on Electronic commerce
“CONFESS”. eliciting honest feedback without independent verification authorities
AAMAS'04 Proceedings of the 6th AAMAS international conference on Agent-Mediated Electronic Commerce: theories for and Engineering of Distributed Mechanisms and Systems
Game theoretic models for social network analysis
Proceedings of the 20th international conference companion on World wide web
Incentive compatible influence maximization in social networks and application to viral marketing
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Finding red balloons with split contracts: robustness to individuals' selfishness
STOC '12 Proceedings of the forty-fourth annual ACM symposium on Theory of computing
Task routing for prediction tasks
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Sybil-proof mechanisms in query incentive networks
Proceedings of the fourteenth ACM conference on Electronic commerce
Eliciting high quality feedback from crowdsourced tree networks using continuous scoring rules
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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Query incentive networks capture the role of incentives in extracting information from decentralized information networks such as a social network. Several game theoretic models of query incentive networks have been proposed in the literature to study and characterize the dependence, of the monetary reward required to extract the answer for a query, on various factors such as the structure of the network, the level of difficulty of the query, and the required success probability. None of the existing models, however, captures the practical and important factor of quality of answers. In this paper, we develop a complete mechanism design based framework to incorporate the quality of answers, in the monetization of query incentive networks. First, we extend the model of Kleinberg and Raghavan [2] to allow the nodes to modulate the incentive on the basis of the quality of the answer they receive. For this quality conscious model, we show the existence of a unique Nash equilibrium and study the impact of quality of answers on the growth rate of the initial reward, with respect to the branching factor of the network. Next, we present two mechanisms, the direct comparison mechanism and the peer prediction mechanism, for truthful elicitation of quality from the agents. These mechanisms are based on scoring rules and cover different scenarios which may arise in query incentive networks. We show that the proposed quality elicitation mechanisms are incentive compatible and ex-ante budget balanced. We also derive conditions under which ex-post budget balance can be achieved by these mechanisms.