Ugo Bardi experiments with new predictive methods.
This post was inspired mainly by the shock I had with the various failed attempts to predict the outcome of the Covid-19 epidemic. It was truly a sobering experience: bad predictions, clueless politicians, arrogant scientists, idiotic journalists, and more. It made me doubt of the usefulness of models in general. I think we are doing several (too many) things wrong with the way we use models and (sometimes) we trust them. I’ll be discussing more on this subject in future posts, for the time being, here is a list of failed predictions that I think can teach us something.
1. Coronavirus Deaths. In 2020, the model developed in large part by Neil Ferguson at the Imperial College in London was the main element that led the British government to engage in a strict “lockdown” policy to avoid the hundreds of thousands (perhaps millions) of deaths that the model predicted as a result of the COVID-19 disease. Most European States followed suit. It is still early to evaluate how the real world followed the model but, if we look at the result proposed in the “Report n. 9“, we see that the model was clearly overly pessimistic. The authors of the model defended their work saying that their prediction of doom was just one of several scenarios, which is correct, but weak as a defense. In the future, we’ll be able to say if Europeans truly wrecked their economies for nothing but, for the time being, the coronavirus experience can be seen as a sobering experience on the limits of the models as predictive tools.
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