Algorithmic Robust Forecast Aggregation
Yongkang Guo Jason D. Hartline Zhihuan Huang Yuqing Kong Anant Shah Fang-Yi Yu
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).