The Paradox of Models: Deciphering Data in Model Land
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We live in an information age – really an age of information overload. But measured quantities do not speak for themselves. Data are given meaning only through the context and framing provided by models. When we want to know how rapidly a new infectious virus is likely to spread, we turn to mathematical models. Models are used by climate scientists to project global warming; by options traders to price contracts; by the Congressional Budget Office to forecast the economic effects of legislation. Without models, data would be only a meaningless stream of numbers.
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Ubiquitous and persuasive, models also drive decisions, one reason why they require our urgent attention. Different models, it was observed, predicted different overall effects, and produced contradictory results. Models seek to represent the real world, but they live outside it. Indeed, they exist in their own ‘wonderful place’, dubbed ‘Model Land’. In Model Land, the assumptions of a model are considered ‘literally true,’ enabling expansive exploration and ambitious predictions. The problem is that Model Land is easy to enter but difficult to escape from. Having built ‘a beautifully internally consistent model, it can be ‘emotionally difficult to acknowledge that the initial assumptions on which the whole thing is built are literally not true.
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There are all sorts of ways that models can lead us astray. A small measurement error on an input can lead to wildly inaccurate forecasts – a phenomenon known as the Butterfly Effect. Fortunately, this type of uncertainty is often manageable. Far more problematic are ‘unquantifiable unknowns’ – things that are left out of a model’s calculation because they can’t be anticipated, such as the unexpected arrival of a transformative technology or the abrupt collapse of a robust market. It is not always true that the data we have now will be relevant to the future.
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Beyond the inherent inability of models to account for the unaccountable, models also reflect the biases of their creators. We may be inclined to regard models as objective expressions of truth, yet they are deliberately constructed interpretations, imbued with the values and viewpoints of the modelers – primarily well-educated, middle-class individuals. Bearing the pandemic, models ‘took more account of harms to some groups of people than others, resulting in a ‘moral case’ for lockdowns that was ‘partial and biased’. Modelers who worked from home – while others maintained the supply chain – often overlooked ‘all of the possible harms’ of the actions their models were suggesting. And 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 and the idea of following the science is meaningless.
