  An important question when eliciting opinions from experts is how to
aggregate the reported opinions. In this paper, we propose a pooling method to
aggregate expert opinions. Intuitively, it works as if the experts were
continuously updating their opinions in order to accommodate the expertise of
others. Each updated opinion takes the form of a linear opinion pool, where the
weight that an expert assigns to a peer's opinion is inversely related to the
distance between their opinions. In other words, experts are assumed to prefer
opinions that are close to their own opinions. We prove that such an updating
process leads to consensus, \textit{i.e.}, the experts all converge towards the
same opinion. Further, we show that if rational experts are rewarded using the
quadratic scoring rule, then the assumption that they prefer opinions that are
close to their own opinions follows naturally. We empirically demonstrate the
efficacy of the proposed method using real-world data.
