Task routing for prediction tasks

Abstract

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.