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原文連結
論文資訊
- 類型：已發表論文
- 日期：2021-02-17
摘要
We present a new 貝氏 bootstrap method for election forecasts that combines traditional polling questions about people’s own intentions with their expectations about how others will vote. It treats each participant’s election winner expectation as an optimal 貝氏 forecast given private and public evidence available to that individual. It then infers the independent evidence and aggregates it across participants. The bootstrap forecast outperforms aggregate national polls in the 2020 U.S. election, as well as the forecasts based on traditional polling questions posed on large national probabilistic samples before the 2018 and 2020 U.S. elections. The bootstrap forecast puts most weight on people’s expectations about how their 社會 contacts will vote, which might incorporate 資訊 about voters who ar
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