Beware the “lure of models”

Scientists cannot help but use models says Mark Buchanan. Models help to clarify the consequences of theoretical assumptions, or to draw out complex lines of cause and effect … Without simplified conceptual models, scientific communication itself would be largely impossible.
Of course, mathematical models also underlie some of the sciences’ most impressive achievements, like e.g. today’s numerical weather predictions. Without models, we would have no insight into ways to engineer … .

Thompson shows how model-building can still be a constructive tool for decision-making, even if the models are flawed

Even so, models should come with safety warnings.
Most people realize that model predictions can be wrong, and often are wrong.
Yet misplaced confidence can draw individuals, groups or entire governments into making painfully poor decisions.

“Models have an almost magical capacity to lure their users into mistaking the sharp, tidy and analytically accessible world of a model with actual reality.”

Mark BuchananBeware the lure of models

Erica Thompson examines in her incisive new book Escape from Model Land, models have an almost magical capacity to lure their users into mistaking the sharp, tidy and analytically accessible world of a model with actual reality, with unfortunate consequences.

Thompson highlights several modelling issues that arise repeatedly, especially in this era of big data. As the world is increasingly “awash in data” coming from satellites, sensors, mobile phones and computing devices, scientists might think they can just “stick to the data”, and so avoid modelling, but this is clearly impossible.
Data, without an interpreting framework, is “a meaningless stream of numbers”. You can choose which kind of model to use, complex or simple, but you will still be using a model — even if merely analysing your data on the back of an envelope.

A second and perhaps more troubling confusion, Thompson argues, is thinking that models can somehow be developed in a way that is free from values, ethics or politics. Scientists seeking intellectual cleanliness may hope to avoid such influences, but it’s not possible. Whether in models developed for climate, epidemiology or traffic management, modellers make decisions of what to include and what to exclude, and these decisions ultimately rest on goals reflecting extra-scientific values. Once in use, models become more than tools for scientific analysis — they are also tools of social persuasion and objects of political contestation, often used in ways the scientists developing them could never have imagined.

But perhaps the greatest problem with models is something no modeller is likely to see as a problem — using them is good fun! “Model Land is a wonderful place,” says Thompson, because in Model Land “all of our assumptions are true, we can really make progress on understanding the model and how it works. We can make predictions. We can investigate many different configurations of the model, and run it with different inputs to see what would happen in different circumstances.”

In all, Escape from Model Land is a refreshing and fearless examination of the strengths and weaknesses of scientific modelling, and its interaction with the habits of the human mind.
In an era of increasing public distrust of science, it might be tempting to defend modelling as something always done in a legitimate scientific spirit.
But the truth is messier — modelling is hugely valuable, and also the source of much confusion.

If the map doesn’t agree with the ground the map is wrong

Gordon Livingston
Too Soon Old, Too Late Smart

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