We study the problem of robust forecast aggregation: combining expert forecasts with provable accuracy guarantees compared to the best possible aggregation of the underlying information. Prior work shows strong impossibility results, e.g. that even under natural assumptions, no aggregation of the experts' individual forecasts can outperform simply following a random expert (Neyman and Roughgarden, 2022). In this paper, we introduce a more general framework that allows the principal to elicit richer information from experts through structured queries. Our framework ensures that experts will truthfully report their underlying beliefs, and also enables us to define notions of complexity over the difficulty of asking these queries. Under a general model of independent but overlapping expert signals, we show that optimal aggregation is achievable in the worst case with each complexity measure bounded above by the number of agents $n$. We further establish tight tradeoffs between accuracy and query complexity: aggregation error decreases linearly with the number of queries, and vanishes when the "order of reasoning" and number of agents relevant to a query is $ω(\sqrt{n})$. These results demonstrate that modest extensions to the space of expert queries dramatically strengthen the power of robust forecast aggregation. We therefore expect that our new query framework will open up a fruitful line of research in this area.

翻译：我们研究鲁棒预测聚合问题：将专家预测进行组合，并确保与基于底层信息的最佳可能聚合相比具有可证明的精度保证。先前的研究显示了强烈的不可能性结果，例如，即使在自然假设下，对专家个体预测的任何聚合都无法超越简单地跟随随机专家的表现（Neyman 和 Roughgarden，2022）。在本文中，我们引入了一个更通用的框架，允许主体通过结构化查询从专家处获取更丰富的信息。我们的框架确保专家将如实报告其底层信念，并使我们能够定义关于提出这些查询难度的复杂性度量。在独立但重叠的专家信号的一般模型下，我们证明了在每种复杂性度量上界为代理数量 $n$ 的最坏情况下，可以实现最优聚合。我们进一步建立了精度与查询复杂度之间的严格权衡：聚合误差随查询数量线性下降，当查询的“推理阶数”和涉及代理数量为 $ω(\sqrt{n})$ 时，误差消失。这些结果表明，对专家查询空间的适度扩展显著增强了鲁棒预测聚合的能力。因此，我们期望这一新的查询框架将开启该领域富有成果的研究方向。