Scalable Algorithms for Aggregating Disparate Forecasts of Probability
  In this paper, computational aspects of the panel aggregation problem are addressed. Motivated primarily by applications of risk assessment, an algorithm is developed for aggregating large corpora of internally incoherent probability assessments. The algorithm is characterized by a provable performance guarantee, and is demonstrated to be orders of magnitude faster than existing tools when tested on several real-world data-sets. In addition, unexpected connections between research in risk assessment and wireless sensor networks are exposed, as several key ideas are illustrated to be useful in both fields. 
Joel B. Predd, Sanjeev R. Kulkarni, Daniel N. Osherson, and H. Vincent   Poor