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
Lottery trees: motivational deployment of networked systems
Proceedings of the 2007 conference on Applications, technologies, architectures, and protocols for computer communications
Truthful and Quality Conscious Query Incentive Networks
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
The anatomy of a large-scale social search engine
Proceedings of the 19th international conference on World wide web
Bluffing and strategic reticence in prediction markets
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
Mechanisms for multi-level marketing
Proceedings of the 12th ACM conference on Electronic commerce
Characterizing and aggregating agent estimates
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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We describe methods for routing a prediction task on a network where each participant can contribute information and route the task onwards. Routing scoring rules bring truthful contribution of information about the task and optimal routing of the task into a Perfect Bayesian Equilibrium under common knowledge about the competencies of agents. Relaxing the common knowledge assumption, we address the challenge of routing in situations where each agent's knowledge about other agents is limited to a local neighborhood. A family of local routing rules isolate in equilibrium routing decisions that depend only on this local knowledge, and are the only routing scoring rules with this property. Simulation results show that local routing rules can promote effective task routing.