Algorithmic Robust Forecast Aggregation
This work addresses the challenge of improving forecast accuracy for decision-makers by offering a more systematic approach to aggregation, though it is incremental as it builds on prior heuristic methods.
The paper tackles the problem of robust forecast aggregation under unknown information structures by proposing an algorithmic framework that provides efficient approximation schemes, demonstrating nearly optimal performance in a specific binary-state setting.
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).