Examining Interpretation Strategies for Multiple Forecast Visualizations with Two and Four Forecasts

Multiple forecast visualizations (MFVs) present curated sets of forecasts to support decision-making under uncertainty. However, the research community knows little about how people interpret and integrate competing forecasts. In this study, we investigate the strategies individuals use when predicting hypothetical future events with MFVs across five visualization types (median, 95% CIs, standard deviation intervals, density plots, and hypothetical outcome plots) and multiple probability distributions in two preregistered experiments (n = 500 each). Analysis of 18 participant strategies and open responses shows that whereas many participants attempted to visually average across forecasts, others adopted a winner-takes-all approach (e.g., selecting a single forecast as the most likely outcome), which deviates from rational agent expectations. We also observed reliance on visual artifacts, such as intersection points or end caps. These findings underscore the complexity of interpreting a range of forecasts and help explain why individuals may privilege particular predictions in real-world decision contexts.