Decision-makers consult multiple forecasts to account for uncertainties when forming judgments about future events. While prior works have compared unaggregated and highly-aggregated designs for displaying multiple forecasts (e.g., Multiple Forecast Visualizations versus confidence interval plots), it remains unclear how partial aggregation impacts judgment. To investigate the effect of partial aggregation, we curated three designs that partially aggregate multiple forecasts. Through two large-scale studies (Experiment 1 n = 695 and Experiment 2 n = 389) across 14 judgment-related metrics, we observed that one design (Horizon Sampled MFV) significantly enhanced participants' ability to predict future trends, thereby reducing their surprise when confronted with the actual outcomes. Grounded in empirical evidence, we provide insights into how to design visualizations for multiple forecasts to communicate uncertainty more effectively. Specifically, since no approach excels in all metrics, we advise choosing different designs based on communication goals and prior knowledge of forecasts.

翻译：决策者在形成对未来事件的判断时，通常会参考多重预测以应对不确定性。尽管先前的研究已比较了展示多重预测的非聚合与高度聚合设计（例如多重预测可视化与置信区间图），但部分聚合如何影响判断仍不明确。为探究部分聚合的效果，我们策划了三种对多重预测进行部分聚合的设计。通过两项大规模研究（实验一 n = 695，实验二 n = 389）并基于14项与判断相关的指标，我们观察到其中一种设计（Horizon Sampled MFV）显著提升了参与者预测未来趋势的能力，从而降低了他们在面对实际结果时的意外感。基于实证证据，我们深入探讨了如何设计多重预测的可视化以更有效地传达不确定性。具体而言，由于没有一种方法在所有指标上都表现优异，我们建议根据沟通目标和预测的先验知识选择不同的设计。