How can policymakers avoid being wrong-footed by ‘black swan’ events such as the global financial crisis, when their modelling proves limited and rigid? One project employs sophisticated algorithms that uses localised data for better forecasting.
How can policymakers avoid being wrong-footed by ‘black swan’ events such as the global financial crisis, when their modelling proves limited and rigid? One project employs sophisticated algorithms that uses localised data for better forecasting.