Superforecasters: what pandemic planners can learn from the world’s best predictors

Experts got it catastrophically wrong, according to Dominic Cummings, UK prime minister Boris Johnson’s former chief adviser. Cummings has argued that the UK government’s official scientific advice in March 2020 hugely misunderstood how the pandemic would play out, leading to a delay in locking down that cost thousands of lives.

According to Cummings, it was certain specialists with less knowledge of pandemics or medicine – such as data scientist Ben Warner, artificial intelligence researcher Demis Hassabis of DeepMind, and mathematician Tim Gowers – who gave more accurate forecasts at this point.

Cummings is also known to be a fan of Superforecasting by Philip Tetlock, a book about people who predict future events more reliably than most. Some superforecasters have been praised for their predictions about the pandemic, while others have also been critical of the experts’ record.

So should governments make greater use of superforecasters instead of relying on scientific experts? The evidence isn’t quite that clear cut. But there certainly seem to be things governments could learn from superforecasting.

In a famous American study on superforecasters published in 2014, they were an elite crew. Only the top 2% of contenders performed well enough in a geopolitical forecasting tournament to win the title. Their task was to assign probabilities to possible answers to dozens of questions.

The researchers provide a few illustrative examples. Who would be the president of Russia in 2012? Will North Korea detonate another nuclear weapon in the next three months? How many refugees will flee Syria next year?

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