Decomposing the Effects of Crowd-Wisdom Aggregators: The Bias-Information-Noise (BIN) Model

Aggregating predictions from multiple judges often yields more accurate predictions than relying on a single judge: the “wisdom-of-the-crowd” effect. This aggregation can be conducted by different methods, from simple averaging to complex techniques, like Bayesian estimators and prediction markets. This article applies a broad set of aggregation methods to subjective probability estimates from a series of geopolitical forecasting tournaments. It then uses the Bias-Information-Noise (BIN) model to disentangle three mechanisms by which each aggregation method improves accuracy: the tamping down of bias and noise and the extraction of valid information across forecasters. Averaging works almost entirely via noise reduction whereas more complex techniques, like prediction markets and Bayesian aggregators, work via all three BIN pathways: better signal extraction and noise and bias reduction.