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I have been a professional mathematical modeller for the past 40 years. Over that period my confidence in models’ forecasts, my own included, has fallen and fallen.
I now think that models of complex systems are of little or no use to decision-makers – they are subject to too much bias, too many spurious correlations. They endow forecasts with an aura of ‘expertise’ that is, in my view, misplaced. Beware forecasts.
Some of the best predictive models turn out to be simple (with perhaps only one or two variables included), even though the historical “fit” is not that good.
Good predictive power tends to come where the modellers are genuinely on top of the theory as well as the data. So, for example, modellers of airflow over an aircraft wing will build a model to predict what angle and speed combination will induce a stall in an aircraft, and these models are amazingly reliable (thank goodness).
But that’s because the modellers really understand the limited influences on airflow over a wing, and include only those variables which are genuinely relevant. This is not a complex system.
But if the system that the forecaster is modelling is complex – by which I mean has unknowable numbers of influences and information, and/or randomness inherent in it – then forecasting models become little better than guesses.
And typically the forecast that models produce in these circumstances often reflect the interests and biases of the forecasters themselves.
Examples of complex systems include: modern economies; stock market and currency prices; as well as the climate. In modelling systems like these, the modeller will never be able to accurately reflect what is going on.
I worked for the Bank of England – economic forecasts cannot be trusted
Models can be amazingly reliable, or little better than a guess
www.telegraph.co.uk