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.

我们研究了预测聚合中考虑基本概率忽视的问题，发现中等程度的基本概率忽视可以降低后悔程度，在最坏情况后悔模型中表现出 V 形曲线。我们还提出了一种在未知基本概率的情况下具有低后悔的新的聚合方法，并进行了实证研究来测试基本概率忽视模型和不同聚合方法的性能。