# Algorithmic Robust Forecast Aggregation

arXiv: https://arxiv.org/abs/2401.17743 · HTML: https://mlsanity.com/paper/2401.17743/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 48 references that are in the map.
- First posted on arXiv 31 Jan 2024.

## Builds on (most cited first, 8 of 8)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Last-Iterate Convergence: Zero-Sum Games and Constrained Min-Max Optimization](https://mlsanity.com/paper/1807.04252/) (2018)
- [Are You Smarter Than a Random Expert? The Robust Aggregation of Substitutable Signals](https://mlsanity.com/paper/2111.03153/) (2021)
- [Online Learning and Solving Infinite Games with an ERM Oracle](https://mlsanity.com/paper/2307.01689/) (2023)
- [Randomization beats Second Price as a Prior-Independent Auction](https://mlsanity.com/paper/1507.08042/) (2015)
- [Combining and Extremizing Real-Valued Forecasts](https://mlsanity.com/paper/1506.06405/) (2015)
- [Prior-Independent Mechanisms for Scheduling](https://mlsanity.com/paper/1305.0597/) (2013)
- [Benchmark Design and Prior-independent Optimization](https://mlsanity.com/paper/2001.10157/) (2020)

## Built on by (most cited first, 5 of 5)

- [Beyond Majority Voting: LLM Aggregation by Leveraging Higher-Order Information](https://mlsanity.com/paper/2510.01499/) (2025)
- [Eliciting Informative Text Evaluations with Large Language Models](https://mlsanity.com/paper/2405.15077/) (2024)
- [Robust forecast aggregation via additional queries](https://mlsanity.com/paper/2512.05271/) (2025)
- [Prior-Agnostic Robust Forecast Aggregation](https://mlsanity.com/paper/2604.24517/) (2026)
- [Investigating the Effectiveness of a Socratic Chain-of-Thoughts Reasoning Method for Task Planning in Robotics, A Case Study](https://mlsanity.com/paper/2503.08174/) (2025)

## Abstract (arXiv)

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).