A log-odds linear pooling rule achieves near-tight minimax regret bounds (0.0255 for unknown state space under CI signals; below 0.0226 for known {0,1} state space) for prior-agnostic robust forecast aggregation.
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Prior-Agnostic Robust Forecast Aggregation
A log-odds linear pooling rule achieves near-tight minimax regret bounds (0.0255 for unknown state space under CI signals; below 0.0226 for known {0,1} state space) for prior-agnostic robust forecast aggregation.