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

Deep Neural Ranking for Crowdsourced Geopolitical Event Forecasting