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Combining survey questions with a Bayesian bootstrap method yields accurate election forecasts

We present a new Bayesian 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 Bayesian forecast given private and background evidence available to that individual. It then infers the independent evidence and aggregates it across participants. The bootstrap forecast outperforms forecasts based on own intentions questions posed on large national samples before the 2018 and 2020 U.S. elections. The bootstrap forecast puts most weight on people’s expectations about how their social contacts will vote, which might incorporate information about voters who are difficult to reach or who hide their true intentions. Beyond election polling, the new method is expected to improve the validity of other social science surveys.