  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.
