On the Nesta blog, Network member Geoff Mulgan previews the upcoming FutureFest event with a post on the value of prediction and learning.
In one of his arguments, Mulgan proposes “some algorithms are (sometimes) better predictors than the average professional.”
“The spread of predictive algorithms has made prediction more precise. Healthcare has been using algorithms for decades; so have the police and criminal justice systems. Often they have found that the algorithms are better predictors than the average professional. In a very different field, Philip Tetlock’s work on expert political judgement was even more damning of the experts’ ability to predict.
My proposal would be not to eliminate prediction but to make it much more explicit, and to make it part of how professions and experts learn, and are held to account. This is beginning to happen as teachers predict children’s exam grades. Doctors can predict risks of patients’ return to hospital. Governments can offer predictions of the impacts of their policies at the same time as they get laws passed and budgets agreed. Business leaders can predict how well their new strategy will go. Journalists can predict what they think will happen in forthcoming elections.”
