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In a crowd forecasting system, aggregation is an algorithm that returnsaggregated probabilities for each question based on the probabilities providedper question by each individual in the crowd. Various aggregation methods havebeen proposed, but simple strategies like linear averaging or selecting thebest-performing individual remain competitive. With the recent advance inneural networks, we model forecasts aggregation as a machine translation task,that translates from a sequence of individual forecasts into aggregatedforecasts, based on proposed Anchor Attention between questions andforecasters. We evaluate our approach using data collected on our forecastingplatform and publicly available Good Judgement Project dataset, and show thatour method outperforms current state-of-the-art aggregation approaches bylearning a good representation of forecaster and question.

Anchor Attention for Hybrid Crowd Forecasts Aggregation