Sound decision-making relies on accurate prediction for tangible outcomes ranging from military conflict to disease outbreaks. To improve crowdsourced forecasting accuracy, we developed SAGE, a hybrid forecasting system that combines human and machine generated forecasts. The system provides a platform where users can interact with machine models and thus anchor their judgments on an objective benchmark. The system also aggregates human and machine forecasts weighting both for propinquity and based on assessed skill while adjusting for overconfidence. We present results from the Hybrid Forecasting Competition (HFC) - larger than comparable forecasting tournaments - including 1085 users forecasting 398 real-world forecasting problems over eight months. Our main result is that the hybrid system generated more accurate forecasts compared to a human-only baseline which had no machine generated predictions. We found that skilled forecasters who had access to machine-generated forecasts outperformed those who only viewed historical data. We also demonstrated the inclusion of machine-generated forecasts in our aggregation algorithms improved performance, both in terms of accuracy and scalability. This suggests that hybrid forecasting systems, which potentially require fewer human resources, can be a viable approach for maintaining a competitive level of accuracy over a larger number of forecasting questions.

翻译：可靠的决策依赖于对从军事冲突到疾病暴发等具体结果的准确预测。为提高众包预测的准确性，我们开发了SAGE混合预测系统，该系统融合了人类与机器生成的预测。该平台使用户能够与机器学习模型交互，从而将其判断锚定在客观基准上。系统在聚合人类与机器预测时，会根据邻近性和评估的技能水平进行加权，同时校正过度自信偏差。我们展示了混合预测竞赛（HFC）的结果——该竞赛规模超过同类预测锦标赛——包含1085名用户在八个月内对398个现实世界预测问题的预测。主要结果表明：相较于完全依赖人类预测（无机器生成预测）的基线，混合系统产生了更准确的预测。我们发现，能够获取机器生成预测的熟练预测者，其表现优于仅能查看历史数据的预测者。我们还证明，在聚合算法中纳入机器生成预测能提升性能，包括准确性和可扩展性两方面。这表明混合预测系统可能以较少的人力资源需求，成为在大量预测问题上维持竞争优势精度的可行途径。