FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
Minimum payments that reward honest reputation feedback
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Eliciting Informative Feedback: The Peer-Prediction Method
Management Science
Incentives for expressing opinions in online polls
Proceedings of the 9th ACM conference on Electronic commerce
Truthful opinions from the crowds
ACM SIGecom Exchanges
Mechanisms for making crowds truthful
Journal of Artificial Intelligence Research
Truthful and Quality Conscious Query Incentive Networks
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
On a linear framework for belief dynamics in multi-agent environments
CLIMA VII'06 Proceedings of the 7th international conference on Computational logic in multi-agent systems
Eliciting forecasts from self-interested experts: scoring rules for decision makers
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
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Eliciting accurate information on any object (perhaps a new product or service or person) using the wisdom of a crowd of individuals utilizing web-based platforms such as social networks is an important and interesting problem. Peer-prediction method is one of the known efforts in this direction but is limited to a single level of participating nodes. We non-trivially generalize the peer-prediction mechanism to the setting of a tree network of participating nodes that would get formed when the query about the object originates at a root node and propagates to nodes in a social network through forwarding. The feedback provided by the participating nodes must be aggregated hierarchically to generate a high quality answer at the root level. In the proposed tree-based peer-prediction mechanism, we use proper scoring rules for continuous distributions and prove that honest reporting is a Nash Equilibrium when prior probabilities are common knowledge in the tree and the observations made by the sibling nodes are stochastically relevant. To compute payments, we explore the logarithmic, quadratic, and spherical scoring rules using techniques from complex analysis. Through detailed simulations, we obtain several insights including the relationship between the budget of the mechanism designer and the quality of answer generated at the root node.