COVID-19 May Disturb Predictions of ‘White House Keys’

Alan Lichtman, distinguished political scientist from American University, has shown an uncanny talent for predicting who will win the US presidential election.

Using an earthquake prediction model adapted to the election prediction, use 13 simple true / false questions, titled “The White House Keys,” with six or more spurious keys indicating that the competitor will defeat the incumbent. Originally formulated to predict the winner of the popular vote, Lichtman’s forecast adjustments after the 2000 Bush-Gore elections led to a correct prediction for Trump about Clinton in 2016.

For the 2020 elections, Lichtman labeled seven of the keys as false, indicating that Joe Biden, Joe Biden, Trump and advisers are considering firing the FBI director after the election: Obama’s father to call Biden in Florida, the Supreme Court re-imposes a ban on voting on the sidewalk. In Alabama. The next president, not just winning the popular vote, which is sure to happen without the help of any role models (think Biden’s dominance in California and New York).

Prediction models, like most analytical models, use sets of input data that combine and transform into an output. These outputs may provide a prediction of something that has yet to happen, or something imminent, but no one knows when. Earthquake prediction models are useful, but like all models, the quality of the input determines the quality of the predictions. As such, they may predict an earthquake that is not occurring (false alarm) or not (obviously false) an earthquake.

Any input-driven model relies on historical information hidden secretly in the data. Artificial intelligence and machine learning usually require massive amounts of data to be most effective. However, if the data is devoid of some very rare event, or events that were never observed, the models may not be able to provide accurate predictions for such phenomena.

How does this relate to this year’s presidential election? Upon examining the keys to conclude the 13, it is clear that four of them are wrong (midterm gains for the current party, no social unrest, no scandals, or foreign or military successes), and six are clearly correct (being the current president, there is no major competitor for the incumbent, no There is an important third-party candidate, a major change in policy, an external or military failure, and the opposition party candidate is not charismatic).

The remaining three keys (positive short-term economic outlook, positive long-term economic outlook and current company are attractive) became the deciding factors. To get to six faulty keys, two of those last three keys must be wrong. Lichtman lists it all as bogus, with COVID-19 turning into the two economic keys. However, there has never been a public health event of the size and scope of the current COVID-19 pandemic. As such, the data used to generate the Thirteen Keys and the Rules of Right / Wrong cannot explain the economic downturn caused by the public health crisis. Looking back at early 2020, before the country closed, the short and long-term economic outlook was positive, which would have given Trump five bogus keys, not seven, an indication of his re-election.

If the model splits the two economic keys, given that the stock market is still strong, the deciding factor becomes whether the incumbent has an attractive personality. Lichtman declares that it is not. Others, especially those who belong to his base on the right, might argue otherwise. Given that the election is about getting out, if Trump’s base is eager to come and vote, the key to Trump’s charisma could become reality.

This exercise shows that despite the huge advances in opinion polls, these elections are closer than many would like to admit. It is possible that Lichtman’s predictions are correct. However, like forecasting earthquakes, sometimes false alarms occur and earthquakes do not occur.

Sheldon H. Jacobson, Ph.D., professor of computer science at the University of Illinois at Urbana-Champaign. It applies its expertise in risk-based, data-based assessment to public policy assessment and communication. He is the founder of Election Analytics at the University of Illinois, a STEM learning lab for election prediction.

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