{"id":57527,"date":"2021-06-01T00:00:20","date_gmt":"2021-06-01T04:00:20","guid":{"rendered":"https:\/\/world.edu\/?p=57527"},"modified":"2021-05-31T15:40:12","modified_gmt":"2021-05-31T19:40:12","slug":"superforecasters-what-pandemic-planners-can-learn-from-the-worlds-best-predictors","status":"publish","type":"post","link":"https:\/\/world.edu\/superforecasters-what-pandemic-planners-can-learn-from-the-worlds-best-predictors\/","title":{"rendered":"Superforecasters: what pandemic planners can learn from the world\u2019s best predictors"},"content":{"rendered":"

\"\"<\/p>\n

Experts got it catastrophically wrong, according to Dominic Cummings, UK prime minister Boris Johnson\u2019s former chief adviser.\u00a0Cummings has argued<\/a>\u00a0that the UK government\u2019s 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.<\/p>\n

According to Cummings, it was certain specialists with less knowledge of pandemics or medicine \u2013 such as data scientist Ben Warner, artificial intelligence researcher Demis Hassabis of DeepMind, and mathematician Tim Gowers \u2013 who gave more accurate forecasts at this point.<\/p>\n

Cummings is also known to be a\u00a0fan<\/a>\u00a0of\u00a0Superforecasting<\/em>\u00a0by Philip Tetlock, a book about people who predict future events more reliably than most. Some superforecasters\u00a0have been praised<\/a>\u00a0for their predictions about the pandemic, while others have also been\u00a0critical of the experts\u2019<\/a>\u00a0record.<\/p>\n

So should governments make greater use of superforecasters instead of relying on scientific experts? The evidence isn\u2019t quite that clear cut. But there certainly seem to be things governments could learn from superforecasting.<\/p>\n

In a\u00a0famous American study<\/a>\u00a0on 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.<\/p>\n

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?<\/p>\n

Of course, just because someone does well one year doesn\u2019t prove that they\u2019re more skilled than anyone else. Maybe they just got lucky. We have to look at how well they did in the following years to evaluate how \u201csuper\u201d they really are.<\/p>\n

Impressively, these superforecasters maintained their edge as the tournament wore on for three more years. In fact, after being combined into \u201csuperforecasting teams\u201d containing only other top performers, their performance increased by a substantial margin. The researchers also found that working in teams and taking relevant training improved performance for other forecasters, compared to forecasters in a control condition.<\/p>\n

Teams and training<\/strong><\/h4>\n

Whether or not we take Cummings at his word that the UK\u2019s pandemic planning suffered from a \u201cclassic group-think bubble\u201d, we know that teams don\u2019t always make wise decisions. What was it that made the teams more successful in the US study?<\/p>\n

It\u2019s hard to say for sure, but the researchers specifically encouraged the teams to ask precise questions to encourage clearer thinking about the evidence supporting a particular forecast, to \u201clook for evidence that contradicts your current prediction\u201d, and to constructively introduce alternative points of view.<\/p>\n

Such debate may well\u00a0improve collective judgment<\/a>\u00a0and\u00a0guard against groupthink<\/a>. Nor were team members required to come to a consensus. Although they shared information and opinions, they still made individual predictions which were combined by algorithm. Superforecaster teams in particular were\u00a0highly engaged<\/a>, frequently sharing information with and asking questions of other team members.<\/p>\n

Another study<\/a>\u00a0took a closer look at which specific training techniques seemed to help most. Three techniques were particularly associated with higher accuracy. The first was the use of so-called comparison classes.<\/p>\n

For example, if I am trying to predict the probability that Benedict Cumberbatch and Sophie Hunter will still be together in five years, it can be helpful to think about other \u201cclasses\u201d that are relevant \u2014 say, the class of celebrity marriages, or even marriages in general. This allows me to look to history to inform my predictions: What percentage of celebrity marriages end in any given five-year period?<\/p>\n

The second was to make use of mathematical and statistical models, when available, to help inform one\u2019s views. The third was to \u201cselect the right questions\u201d \u2013 a recommendation to spend more time predicting answers to questions where you know more than others about the topic, or on which additional research is likely to pay off. However, the researchers stressed that all components of\u00a0the training<\/a>\u00a0may have contributed holistically to better performance.<\/p>\n

Research has also shown that\u00a0accuracy improves when we keep track of our past performance<\/a>\u00a0– but the kind of feedback matters. Did outcomes you thought would happen 20% of the time actually happen 20% of the time? What about outcomes you thought would happen 90% of the time? Performance improves for those who receive this kind of information.<\/p>\n

Can governments do better?<\/strong><\/h4>\n

Could the UK government have done better on COVID-19 by soliciting input from teams of superforecasters? It\u2019s possible. Superforecasters at\u00a0Good Judgment Open<\/a>\u00a0and practised forecasters at\u00a0Metaculus<\/a>\u00a0(in which I\u2019ve participated) each seem to have done well on COVID-19, with Metaculus claiming to have\u00a0outperformed experts<\/a>\u00a0in June 2020. That said, in a\u00a0recent series<\/a>\u00a0of COVID-19-related predictions, trained forecasters were not always more accurate than experts. The researchers behind the survey are experimenting with ways of combining predictions from domain experts and trained forecasters into a \u201cconsensus forecast\u201d.<\/p>\n

It also seems plausible that even the training that helped the non-supers make better forecasts would have been useful. For example, Cummings claimed that although there was much attention to epidemiological models, evidence that contradicted the models\u2019 assumptions \u2014 such as data being reported by intensive care units \u2014 was ignored. It certainly seems plausible that someone trained to \u201clook for evidence that contradicts your current prediction\u201d might have spotted this earlier.<\/p>\n

Of course, not all recommendations from the literature are practical in government settings. In theory, governments could test such recommendations for themselves, adopting any that seemed beneficial. Unfortunately, you can\u2019t improve what you don\u2019t measure.<\/p>\n

In\u00a0Superforecasting<\/em>, Tetlock emphasises that any organisation serious about improving its forecasts must attach concrete numbers to them, at least internally. A phrase like \u201cserious possibility\u201d may mean a 20% chance to one person and an 80% chance to another.<\/p>\n

This is almost certainly what Cummings was referring to\u00a0when he said<\/a>: \u201cA guy called Phil Tetlock wrote a book and in that book he said that you should not use words like reasonable and probable and likely, because it confuses everybody.\u201d Perhaps it shouldn\u2019t surprise us if organisations that do not make forecasts in a way that they can be evaluated are not equipped to learn how to make them better. To improve, you first have to try.<\/p>\n

Author Bio:<\/strong> Gabriel Recchia is a Research Associate, Winton Centre for Risk and Evidence Communication at the University of Cambridge<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

Experts got it catastrophically wrong, according to Dominic Cummings, UK prime minister Boris Johnson\u2019s former chief adviser.\u00a0Cummings has argued\u00a0that the UK government\u2019s 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 […]\n… learn more→<\/a><\/p>","protected":false},"author":33,"featured_media":57528,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[3],"tags":[366380],"coauthors":[11733],"class_list":["post-57527","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-business-politics","tag-superforecasters"],"acf":[],"yoast_head":"\nSuperforecasters: what pandemic planners can learn from the world\u2019s best predictors - World leading higher education information and services<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/world.edu\/superforecasters-what-pandemic-planners-can-learn-from-the-worlds-best-predictors\/\" class=\"yoast-seo-meta-tag\" \/>\n<meta property=\"og:locale\" content=\"en_US\" class=\"yoast-seo-meta-tag\" \/>\n<meta property=\"og:type\" content=\"article\" class=\"yoast-seo-meta-tag\" \/>\n<meta property=\"og:title\" content=\"Superforecasters: what pandemic planners can learn from the world\u2019s best predictors - World leading higher education information and services\" class=\"yoast-seo-meta-tag\" \/>\n<meta property=\"og:description\" content=\"Experts got it catastrophically wrong, according to Dominic Cummings, UK prime minister Boris Johnson\u2019s former chief adviser.\u00a0Cummings has argued\u00a0that the UK government\u2019s 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. 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