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Escape from Model Land - Erica Thompson ***

Over the last few years a number of books, notably Sabine Hossenfelder's Lost in Math, David Orrell's Economyths, Cathy O Neil's Weapons of Math Destruction and Tim Palmer's The Primacy of Doubt, have pointed out problems with the mathematical modelling done by businesses, physicists, meteorologists, epidemiologists, economists and more. These are not anti-science polemics, but rather people who know what they're talking about pointing out the dangers of getting too carried away with elegant mathematics and models, often assuming that the models effectively are reality (and certainly presenting them that way in some of the writing and press releases from the scientists building and using the models).

Erica Thompson takes on the problems of mentally inhabiting the mathematical world she describes as 'model land'. As she cogently points out, it's fine to play in model land all that you like - the problem comes with the way that you exit model land and tie back to the real world. This book is loaded with examples from climate forecasts, economics, pandemic forecasting and more where the modellers have been unable to successfully get out of model land and present their information usefully to those who have to make decisions (or the public). This is not an attempt to get rid of models. Thompson's key argument is that while models will pretty well always be wrong they can still be very useful - and an understanding of uncertainty/risk combined with expert interpretation is the best (if sometimes narrow) bridge to link model land to the real world.

Unfortunately, unlike the books mentioned above, Thompson's doesn't read particularly well - the writing is very dry. It's also arguable that having set us up to ask questions about scientific output and models, we don't get the same degree of analysis applied to Thompson's personal ideas. So, for example, she tells us 'Diversity in boardrooms is shown to result in better decision making'. I don't doubt this, or the parallel she is drawing for needing diversity of models - but how was success of decision making measured, and what does diversity mean in this context? In fact diversity is something of a running theme, with Thompson several times referring to model makers as largely WEIRD (apparently standing for Western, Educated, Industrial, Rich, Developed) - the acronym seems an unnecessarily ad hominem jibe - and is it really possible to develop mathematical models without being educated?

There are a few oddities and omissions. One of Palmer's big points in The Primacy of Doubt is the oddity that economics hasn't taken up ensemble forecasting - something that isn't mentioned here. The way (mathematical) chaos is presented is also a little odd - it's mostly referred to as 'the butterfly effect', which is really only a specific example of a potential (though relatively unlikely) impact of a chaotic system. Thompson also calls chaotic systems complex, yet they can be surprisingly simple. It's also unfortunate when describing the limitations of vaccine modelling there is no mention of a point emphasised in the scientific journal Nature: the way surface transmission and cleaning continued to be pushed many months after there was clear evidence that transmission was primarily airborne. 

Thompson's enthusiasm for diversity has one notable exception that throws into doubt her concept that the best way to use models is to have more intuitive human input from academics to interpret and modify the results. She has an impressive list of diversity requirements - age, social class, background, gender and race for those academics. But she omits the diversity elephant in the room, which is political leanings. When the vast majority of academics are politically left wing, surely this too needs to be taken into account.

Overall, some interesting points, but a dry academic writing style combined with some limitations means that it has less impact than the books mentioned above.

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Review by Brian Clegg - See all of Brian's online articles or subscribe to a weekly digest for free here
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Comments

  1. zebedee10 July 2024 at 11:04

    Just managed to get through the first chapter which was painful given the writing style and the mistakes. Apparently she worked on epidemic models during Covid-19 and yet says epidemics grow exponentially which is against the model equations never mind the laws of Maths and epidemiology. Doesn't understand which conditions are initial conditions. Complains about modelling being WEIRD when lots of cultures had astronomers before contact with the West and so, presumably, astronomical models, their grander buildings may have required engineering models and I'm sure that there are plenty more examples.

    There's a YouTube video plugging the book and the author came across as woolly so I put off buying a copy having previously seen lots of marketing hype. Somewhat regretting my purchase decision now as I'm not sure that I'll finish the book.

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    Replies
    1. Brian Clegg11 July 2024 at 01:43

      I don't have a copy to hand, so don't know the context of the exponential growth, but there seems good support that pandemics are well represented by exponential models in the early stages - see, for example, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7575103/

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