On the useful-ness of models

One thing I’d really like to do in 2021 is get back into writing just for fun. Although I have written a lot academically in the last few years, my space and time to just write my thoughts had become really squeezed. I hope to use some spare time on Friday mornings to quickly put a few words together about what’s on my mind at the time and re-engage with the craft. These are my own personal views and opinions.

On the useful-ness of models

Most numerical modellers will be familiar with mathematician George Box’s quote “All models are wrong, but some are useful”. I love this quote, as even though I don’t think it was intended for numerical simulations, it strikes right at the heart of many of the issues our research community are trying to address.

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Too often though, we don’t consider how ‘useful’ our models are. How wrong they are? Yes, we look at that all the time. We develop new ways to calculate, express, and communicate how wrong they are. We work hard on new methods and at collecting new, more, and better data so we can make the models less wrong. When we’ve done this, we have models that are either less wrong, which is good as they will be right more often, or are able to show us how wrong they might be, which is also good as it allows people to make better informed choices about risks.

When we do consider how useful a model is, it’s often in the ways discussed above. Providing decision makers with the information about how wrong a model is lets them make a better informed decision. It is more useful to them. Great, box ticked. But, in my opinion, the model does not stop there.

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In a recent post for CIWEM, Phiala Mehring, a floodie, research director, and PhD researcher, discussed how we communicate with communities affected, or at risk of being affected, by flooding. It’s a really important post so please go read it here. There was one paragraph that really stood out for me:

“Imagine having lived in your home for three decades, to have a complete stranger knock on your door to say you are at risk of flooding “because the flood model says so”. What do you believe; a model that simulates the area – or your lived experience of more than 30 years?”

In this situation, to this audience, it does not matter how precise and accurate that model had been made. All the effort and hours put in developing methods to communicate how wrong the model might be do not matter either. It also does not matter how useful decision makers found it. Here, in this situation, the model is useless.

How we utilise model results when working out in the real-world communicating flood risk is a crucial facet of the model’s development and its use. It’s just as important as finding reliable and accurate rainfall information to input into it right at the start of the chain. And it’s the reason we should always measure our models by that one criteria George Box proposed to us – how useful they are.

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