In the absence of historical data for use as forecasting inputs, decision makers often ask a panel of judges to predict the outcome of interest, leveraging the wisdom of the crowd (Surowiecki 2005). Even if the crowd is large and skilled, shared information can bias the simple mean of judges' estimates. Addressing the issue of bias, Palley and Soll (2019) introduces a novel approach called pivoting. Pivoting can take several forms, most notably the powerful and reliable minimal pivot. We build on the intuition of the minimal pivot and propose a more aggressive bias correction known as the neutral pivot. The neutral pivot achieves the largest bias correction of its class that both avoids the need to directly estimate crowd composition or skill and maintains a smaller expected squared error than the simple mean for all considered settings. Empirical assessments on real datasets confirm the effectiveness of the neutral pivot compared to current methods.

翻译：在缺乏历史数据作为预测输入的情况下，决策者常借助群体智慧，邀请专家组预测目标结果（Surowiecki 2005）。即便群体规模庞大且经验丰富，共享信息仍可能导致简单平均估计值产生偏差。针对该偏差问题，Palley与Soll（2019）提出了一种名为"枢轴法"的创新方法。枢轴法可采取多种形式，其中最具代表性的是稳健可靠的最小枢轴法。本文基于最小枢轴法的核心理念，提出了一种更具激进性的偏差校正方法——中立枢轴法。该类方法既能避免直接估计群体构成或专业能力，又能在所有考察场景中保持比简单平均更小的期望平方误差，而中立枢轴法实现了该类方法中最大的偏差校正能力。基于真实数据集的实证评估证实，相较现有方法，中立枢轴法具有显著有效性。