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November 2, 2020 02:00 pm

Why You Can't Rely on Election Forecasts

Zeynep Tufekci, writing at The New York Times: There's a strong case for ignoring the predictions. Why do we have models? Why can't we just consider polling averages? Well, presidents are not elected by a national vote total but by the electoral votes of each state, so national polls do not give us the information we need. As two of the last five elections showed -- in 2000 and 2016 -- it's possible to win the popular vote and lose the Electoral College. Models give us a way to process polls of various quality in 50 states to arrive at a forecast. There are two broad ways to model an event: using "fundamentals" -- mechanisms that can affect the event -- and probabilities -- measurements like polls. For elections, fundamentals would be historically informed lessons like, "a better economy favors incumbents." With polls, there is no theory about why they are the way they are. We just use the numbers they produce. Electoral forecast modelers run simulations of an election based on various inputs -- including state and national polls, polling on issues and information about the economy and the national situation. If they ran, say, 1,000 different simulations with various permutations of those inputs, and if Joe Biden got 270 electoral votes in 800 of them, the forecast would be that Mr. Biden has an 80 percent chance of winning the election. This is where weather and electoral forecasts start to differ. For weather, we have fundamentals -- advanced science on how atmospheric dynamics work -- and years of detailed, day-by-day, even hour-by-hour data from a vast number of observation stations. For elections, we simply do not have anything near that kind of knowledge or data. While we have some theories on what influences voters, we have no fine-grained understanding of why people vote the way they do, and what polling data we have is relatively sparse. Consequently, most electoral forecasts that are updated daily -- like those from FiveThirtyEight or The Economist -- rely heavily on current polls and those of past elections, but also allow fundamentals to have some influence. Since many models use polls from the beginning of the modern primary era in 1972, there are a mere 12 examples of past presidential elections with dependable polling data. That means there are only 12 chances to test assumptions and outcomes, though it's unclear what in practice that would involve. A thornier problem is that unlike weather events, presidential elections are not genuine "repeat" events. Facebook didn't play a major role in elections until probably 2012. Twitter, without which Mr. Trump thinks he might not have won, wasn't even founded until 2006. How much does an election in 1972, conducted when a few broadcast channels dominated the public sphere, tell us about what might happen in 2020? Interpreting electoral forecasts correctly is yet another challenge. If a candidate wins an election with 53 percent of the vote, that would be a decisive victory. If a probability model gives a candidate a 53 percent chance of winning, that means that if we ran simulations of the election 100 times, that candidate would win 53 times and the opponent 47 times -- almost equal odds.

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