  There are many examples of 'wisdom of the crowd' effects in which the large
number of participants imparts confidence in the collective judgment of the
crowd. But how do we form an aggregated judgment when the size of the crowd is
limited? Whose judgments do we include, and whose do we accord the most weight?
This paper considers this problem in the context of geopolitical event
forecasting, where volunteer analysts are queried to give their expertise,
confidence, and predictions about the outcome of an event. We develop a
forecast aggregation model that integrates topical information about a
question, meta-data about a pair of forecasters, and their predictions in a
deep siamese neural network that decides which forecasters' predictions are
more likely to be close to the correct response. A ranking of the forecasters
is induced from a tournament of pair-wise forecaster comparisons, with the
ranking used to create an aggregate forecast. Preliminary results find the
aggregate prediction of the best forecasters ranked by our deep siamese network
model consistently beats typical aggregation techniques by Brier score.
