Robust aggregation integrates predictions from multiple experts without knowledge of the experts' information structures. Prior work assumes experts are Bayesian, providing predictions as perfect posteriors based on their signals. However, real-world experts often deviate systematically from Bayesian reasoning. Our work considers experts who tend to ignore the base rate. We find that a certain degree of base rate neglect helps with robust forecast aggregation. Specifically, we consider a forecast aggregation problem with two experts who each predict a binary world state after observing private signals. Unlike previous work, we model experts exhibiting base rate neglect, where they incorporate the base rate information to degree $\lambda\in[0,1]$, with $\lambda=0$ indicating complete ignorance and $\lambda=1$ perfect Bayesian updating. To evaluate aggregators' performance, we adopt Arieli et al. (2018)'s worst-case regret model, which measures the maximum regret across the set of considered information structures compared to an omniscient benchmark. Our results reveal the surprising V-shape of regret as a function of $\lambda$. That is, predictions with an intermediate incorporating degree of base rate $\lambda<1$ can counter-intuitively lead to lower regret than perfect Bayesian posteriors with $\lambda=1$. We additionally propose a new aggregator with low regret robust to unknown $\lambda$. Finally, we conduct an empirical study to test the base rate neglect model and evaluate the performance of various aggregators.

翻译：鲁棒聚合旨在整合多位专家的预测，而无需了解专家的信息结构。先前研究假设专家遵循贝叶斯法则，能够基于其观测信号提供完美的后验概率。然而，现实中的专家常常系统性地偏离贝叶斯推理。本研究探讨倾向于忽视基础率的专家行为。我们发现，一定程度的基础率忽视反而有助于提升预测聚合的鲁棒性。具体而言，我们考虑一个由两位专家参与的预测聚合问题：每位专家在观测私有信号后，对二元世界状态进行预测。与以往研究不同，我们构建了具有基础率忽视特征的专家模型，其中专家对基础率信息的整合程度用参数 $\lambda\in[0,1]$ 表示：$\lambda=0$ 代表完全忽视基础率，$\lambda=1$ 代表完美的贝叶斯更新。为评估聚合器的性能，我们采用 Arieli 等人（2018）提出的最坏情况遗憾模型，该模型通过对比全知基准，衡量聚合器在特定信息结构集合中的最大遗憾值。研究结果揭示了遗憾值随 $\lambda$ 变化呈现令人惊讶的 V 型曲线：即当基础率整合程度处于中间水平（$\lambda<1$）时，其预测结果反而可能产生比完美贝叶斯后验（$\lambda=1$）更低的遗憾值。我们进一步提出一种对未知 $\lambda$ 具有鲁棒性的低遗憾聚合器。最后，通过实证研究验证基础率忽视模型，并评估不同聚合器的实际性能。