text: 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.
  @cite_20: Abstract : This paper investigates the question of how to reconcile incoherent probability assessments, i.e., assessments that are inconsistent with the laws of probability. A general model for the analysis of probability assessments is introduced, and two approaches to the reconciliation problem are developed. In the internal approach, one estimates the subject's true probabilities on the basis of his assessments. In the external approach, an external observer updates his own coherent probabilities in the light of the assessments made by the subject. The two approaches are illustrated and discussed. Least-squares procedures for reconciliation are developed within the internal approach. (Author)
summary: @cite_20 consider a Bayesian approach to reconciling probability forecasts, whereby noisy" observations @math are assumed to arise from a coherent set @math . CAP can be viewed as a special-case of their model, since as discussed above, the solution to ) admits a Bayesian interpretation as the maximum-likelihood coherent forecasts given additive white noise corrupted observations @math . However, note that @cite_20 sought to eliminate incoherence from a single judge, whereas CAP was introduced to address the panel aggregation problem. Moreover, Osherson and Vardi were motivated by non-statistical interpretations of CAP and as here, addressed the computational issue of implementing CAP.