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Thursday, April 25, 2024
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HomeNewsMathematical models, and why we can’t trust them

Mathematical models, and why we can’t trust them

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ERICA Thompson’s book, Escape From Model Land, (Basic Books, £20) is a fascinating and timely examination of the use of mathematical models in society. Anyone interested in the use of models in finance, pandemics and climate change will benefit from its informed yet sceptical wisdom.


She makes a clear distinction that there is ‘Model Land’ and there is the real world and they are not the same, even though many make the mistake in thinking that they are. Hence the ‘Escape’ in the title. How does one relate to the other? If we confuse models with the real world then we fool ourselves. Remember the Chief Financial Officer of Goldman Sachs bemoaning the financial crisis in 2007 expressing amazement at the 25-standard deviation events which occurred several days in a row? Such extremely rare occurrences never happened in the models, so it was thought it couldn’t happen in real life! In a sense models fail to account for the unaccountable, and that has implications for anything that is modelled. J K Galbraith also had the right idea that the only real function of economic forecasting was to make astrology look respectable.

Models are full of built-in assumptions. For simple models this can be OK. Modelling the swing of a double pendulum relies on the law of gravity. Complex models have biases and uncertainties. Hence any model of anything that comes from one person, or a team or institute must always be viewed sceptically. Bias, groupthink and the viewpoints of the modellers creep in. Models are not objective expressions of truth.

The inquiry into the Covid pandemic will fail if politicians don’t have this point drummed into them, if it hasn’t been already. Some pandemic models might have been all we had at the time it started, but can it be said they added to the decisions that were made beyond expert judgment and common sense?

This is a point Thompson makes in the book. Expert assessment of models is needed because it is all we have in the real world even if the true competence of that judgment is unknown. I am reminded of the fashion for climate event attribution, using models of extreme weather events to link them to climate change. The models are complex and full of subjective factors and curated by a small group who mark each other’s homework, while the media accept their output as reality.

Models rely on data and as a society we are generating more and more of it, analysed with powerful computers and artificial intelligence. Models will play an increasingly important role in our lives and we should not let them do so with their limitations unappreciated.

Models played an important role in the pandemic and their accuracy has been questioned. But even if they had some validity, Thompson notes that during the pandemic they ‘took more account of harms to some groups of people than others’, resulting in a ‘moral case’ for lockdowns that was ‘partial and biased’. Modellers often overlooked ‘all of the possible harms’ of the actions their models were suggesting.

Even when models try to describe the effects of different courses of action, it’s human beings who must ultimately weigh the benefits and harms. ‘Science cannot tell us how to value things,’ she writes. ‘The idea of “following the science” is meaningless.’

Models are useful if we know them for what they are. I fear too many politicians and journalists, who may be intimidated by equations, code, algorithms and technical speak, let them pass too uncritically. Nobel Prize winner Peter Diamond summed it up when he said: ‘Taking a model literally is not taking a model seriously.’

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