# Prior-Agnostic Robust Forecast Aggregation

arXiv: https://arxiv.org/abs/2604.24517 · HTML: https://mlsanity.com/paper/2604.24517/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 27 Apr 2026.

## Builds on (most cited first, 10 of 10)

- [Beyond Majority Voting: LLM Aggregation by Leveraging Higher-Order Information](https://mlsanity.com/paper/2510.01499/) (2025)
- [Are You Smarter Than a Random Expert? The Robust Aggregation of Substitutable Signals](https://mlsanity.com/paper/2111.03153/) (2021)
- [Elicitation for Aggregation](https://mlsanity.com/paper/1410.0375/) (2014)
- [Forecast Aggregation via Peer Prediction](https://mlsanity.com/paper/1910.03779/) (2019)
- [Robust Decision Aggregation with Second-order Information](https://mlsanity.com/paper/2311.14094/) (2023)
- [Algorithmic Robust Forecast Aggregation](https://mlsanity.com/paper/2401.17743/) (2024)
- [The Surprising Benefits of Base Rate Neglect in Robust Aggregation](https://mlsanity.com/paper/2406.13490/) (2024)
- [Robust forecast aggregation via additional queries](https://mlsanity.com/paper/2512.05271/) (2025)
- [Bayesian aggregation of two forecasts in the partial information framework](https://mlsanity.com/paper/1608.04717/) (2016)
- [Robust Decision Aggregation with Adversarial Experts](https://mlsanity.com/paper/2403.08222/) (2024)

## Built on by (most cited first, 1 of 1)

- [Refinement as a Service: Algorithmic Predictor Refinement](https://mlsanity.com/paper/2610.11415/) (2026)

## Abstract (arXiv)

Robust forecast aggregation combines the predictions of multiple information sources to perform well in the worst case across all possible information structures. Previous work largely focuses on settings with a known binary state space, where the state is either 0 or 1. We study prior-agnostic robust forecast aggregation in which the aggregator observes only experts' reports, yet is ignorant of both the underlying joint information structure and the full prior, including the underlying state space. Unlike the standard model that fixes the binary state space {0, 1}, we allow the (binary) unknown state values to be arbitrary numbers in [0, 1], so the same reported probability may correspond to very different realized outcome frequencies across environments. Our main contribution is a simple, explicit, closed-form log-odds aggregator that linearly pools forecasts in logit space, together with (nearly-)tight minimax-regret guarantees across three knowledge regimes. We first show that under conditionally independent (CI) signals, robust aggregation with an unknown state space is strictly harder than in the known-state setting by establishing a larger lower bound, and our aggregation rule can achieve a worst-case regret of 0.0255. Along the way, we also characterize tight regret bounds for Blackwell-ordered structures and for general information structures. In the classical setting with known state space {0,1}, our aggregator achieves regret strictly below 0.0226 for CI structures. To the best of our knowledge, this is the first explicit closed-form aggregator that achieves a regret upper bound strictly less than 0.0226. Finally, we extend the model where the aggregator additionally knows each expert's marginal forecast distribution; in this setting, with the CI structures, we show that a generalized log-odds rule achieves regret of 0.0228, complementing with a lower bound of 0.0225.