A thoughtful look at modelling by an experienced climate modeller.

What are models for? The most common answer would be “to predict the future behaviour of some system,” but Thompson argues a far more subtle line: that the most important models often fail to be predictive in any real sense. Much of this is down to problems of validation, especially in climate models for which we have no experience of the world the models are trying to predict.

An even more subtle mistake is regarding all models as “cameras” that simply observe the world. That’s true for the more abstract kinds of modelling, where one is trying to understand possible behaviours of systems in general without tying them to specific circumstances. But the models with which most people are familiar act ore like “engines” that can perturb the system they’re purporting simply to observe by baing used as drivers for policy. Climate and epidemic models seek to warn as well as predict and understand, but this exacerbates the problems of validation: if the model’s predictions don’t come to pass, perhaps this is because policy-makers took corrective actions in response, or maybe bacause they didn’t intervent effectively enough. This isn’t a reason to give up on modelling altogether: how else are we to understand complex systems, and how else are we to respond rationally to them? But it does mean that the notion of “following the science” problematic.

Thompson also wrestles with the problem of groupthink amongst modellers, who often share a common overallping background. I agree this is a problem, but the idea that we can increase diversity in the community easily seems flawed to me. Modellers share a scientific viewpoint and a belief in modelling, and no-one who doesn’t will ever be able to effectively engage with the models or their arguments. Perhaps it’s enough that scientists are always advisors and never decision-makers, and allow politicians to deal with the integration of different choices and values – although that split isn’t always appreciated by the public, and is often (as in the covid-19 pandemic) deliberately blurred to allow less-trusted politicians to draw credibility from more-trusted scientists and doctors.

Overall I think this is a lucid and valiant attempt to summarise and explore the benefits and limitations of models, and science in general, when it impacts directly on the wider world. It deserves to be widely read in the scientific community so that we can better understand our place in policies that we often unavoidably have to influence.

4/5. Finished Friday 22 March, 2024.

(Originally published on Goodreads.)