Log-odds aggregation handles unknown state spaces in forecast combining
Chen, Peng, and Tang propose a closed-form aggregator for combining expert forecasts when the underlying outcome range is unknown, achieving tighter regret bounds than prior methods.
A log-odds pooling rule aggregates expert forecasts robustly even when the true outcome space and joint information structure remain hidden.
- — Standard forecast aggregation assumes known binary outcomes {0, 1}; this work allows unknown arbitrary values in [0, 1].
- — Log-odds aggregator linearly pools forecasts in logit space, yielding closed-form solution with explicit regret guarantees.
- — Under conditionally independent signals, unknown state space increases worst-case regret from <0.0226 to 0.0255.
- — Regret bounds derived for three regimes: conditionally independent, Blackwell-ordered, and general information structures.
- — When expert marginal distributions are known, generalized log-odds rule achieves regret of 0.0228 with matching lower bound.
- — First explicit aggregator achieving regret strictly below 0.0226 in classical binary setting.
Astrobobo tool mapping
- Knowledge Capture Record the paper's log-odds formula and regret bounds as a reference card for forecast aggregation decisions in your domain.
- Focus Brief Summarize the three regret regimes (CI, Blackwell-ordered, general) and note which applies to your expert structure.
- Reading Queue Queue related work on Blackwell ordering and information structures to understand when tighter bounds apply.
Frequently asked
- Log-odds aggregation pools expert forecasts in logit (log-odds) space rather than probability space. This approach is robust when the true outcome range is unknown or varies across environments. The paper shows it achieves tighter worst-case regret bounds than simpler averaging methods, especially when experts' signals are conditionally independent.
cite ▸
APA
Zhi Chen, Cheng Peng, Wei Tang. (2026, April 28). Log-odds aggregation handles unknown state spaces in forecast combining. Astrobobo Content Engine (rewrite of arxiv/cs.LG). https://astrobobo-content-engine.vercel.app/article/log-odds-aggregation-handles-unknown-state-spaces-in-forecast-combining-2c1ae4
MLA
Zhi Chen, Cheng Peng, Wei Tang. "Log-odds aggregation handles unknown state spaces in forecast combining." Astrobobo Content Engine, 28 Apr 2026, https://astrobobo-content-engine.vercel.app/article/log-odds-aggregation-handles-unknown-state-spaces-in-forecast-combining-2c1ae4. Based on "arxiv/cs.LG", https://arxiv.org/abs/2604.24517.
BibTeX
@misc{astrobobo_log-odds-aggregation-handles-unknown-state-spaces-in-forecast-combining-2c1ae4_2026,
author = {Zhi Chen, Cheng Peng, Wei Tang},
title = {Log-odds aggregation handles unknown state spaces in forecast combining},
year = {2026},
url = {https://astrobobo-content-engine.vercel.app/article/log-odds-aggregation-handles-unknown-state-spaces-in-forecast-combining-2c1ae4},
note = {Astrobobo rewrite of arxiv/cs.LG, https://arxiv.org/abs/2604.24517},
}