Forecast aggregation combines the predictions of multiple forecasters to improve accuracy. However, the lack of knowledge about forecasters' information structure hinders optimal aggregation. Given a family of information structures, robust forecast aggregation aims to find the aggregator with minimal worst-case regret compared to the omniscient aggregator. Previous approaches for robust forecast aggregation rely on heuristic observations and parameter tuning. We propose an algorithmic framework for robust forecast aggregation. Our framework provides efficient approximation schemes for general information aggregation with a finite family of possible information structures. In the setting considered by Arieli et al. (2018) where two agents receive independent signals conditioned on a binary state, our framework also provides efficient approximation schemes by imposing Lipschitz conditions on the aggregator or discrete conditions on agents' reports. Numerical experiments demonstrate the effectiveness of our method by providing a nearly optimal aggregator in the setting considered by Arieli et al. (2018).

翻译：预测聚合通过整合多个预测者的预测结果以提高准确性。然而，对预测者信息结构的认知缺失阻碍了最优聚合的实现。给定一组可能的信息结构，鲁棒预测聚合旨在寻找与全知聚合器相比具有最小最差遗憾的聚合器。现有鲁棒预测聚合方法依赖启发式观察与参数调优。我们提出了一种面向鲁棒预测聚合的算法框架。该框架为有限可能信息结构族下的通用信息聚合提供了高效近似方案。在Arieli等人（2018）研究的双智能体条件下——即智能体基于二元状态接收独立信号——我们的框架通过引入聚合器的Lipschitz条件或智能体报告的离散条件，同样提供了高效近似方案。数值实验表明，在Arieli等人（2018）的设定中，该方法提供的近似最优聚合器效果显著。