Robust forecast aggregation

Arieli Itai; Babichenko Yakov; Smorodinsky Rann

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Robust forecast aggregation

机译：Robust forecast aggregation

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摘要

Bayesian experts who are exposed to different evidence often make contradictory probabilistic forecasts. An aggregator, ignorant of the underlying model, uses this to calculate his or her own forecast. We use the notions of scoring rules and regret to propose a natural way to evaluate an aggregation scheme. We focus on a binary state space and construct low regret aggregation schemes whenever there are only two experts that either are Blackwell-ordered or receive conditionally independent and identically distributed (i.i.d.) signals. In contrast, if there are many experts with conditionally i.i.d. signals, then no scheme performs (asymptotically) better than a (0.5, 0.5) forecast.

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来源
《Proceedings of the National Academy of Sciences of the United States of America.》 |2018年第52期|E12135-E12143|共9页
作者
Arieli Itai; Babichenko Yakov; Smorodinsky Rann;
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作者单位

Technion Israel Inst Technol, Fac Ind Engn & Management, IL-3200003 Haifa, Israel;

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收录信息美国《科学引文索引》(SCI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
原文格式 PDF
正文语种英语
中图分类自然科学总论;
关键词
information aggregation; one-shot regret minimization; conditionally independent information structure; Blackwell-ordered information structure;

Robust forecast aggregation

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