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The Primacy of Doubt - Tim Palmer *****

This is quite possibly the best popular science book I've ever read (and I've read many hundreds). To describe what Tim Palmer, a physicist turned meteorologist, does in simple terms does not do it justice. But essentially he explores the nature of (mathematically) chaotic systems and shows how we can deal better with uncertainty, even using his expertise to propose a different way to look at the lack of local reality in quantum physics.

This is interesting stuff anyway, but what is astounding is the way that Palmer rattles through a series of topics that are quite difficult to get your head around and, in several diverse cases, gives the most approachable explanation of the topic I've ever seen.

I'm not saying this book is an easy read, by the way. You do have to think about what you are reading, and I had to go back over a couple of sections to make sure it sunk in. But it is so rewarding of the effort.

In terms of this broad enlightening nature, the first of the three sections in the book stands out head and shoulders above the rest. Palmer starts by exploring chaos and gives the best explanation of the behaviour of chaotic systems, state space and attractors I've come across. Then he throws in Cantor sets, then shows the relationship of weather forecasts to all this, and introduces p-adic numbers (arguably the only bit that could have been better explained). He then shows graphically (literally, not metaphorically) how the introduction of noise can make models of chaotic systems work better. Finally in this section, he takes on quantum uncertainty, with one of the only explanations of the use of Bell's inequality I've ever seen that is at least vaguely comprehensible.

I don't usually go into that much detail in a review, but just wanted to show how much is crammed into the first 80 or so pages.

In the second section, Palmer addresses the use of Monte Carlo methods and ensembles in making at least partly successful predictions of chaotic systems, such as the weather, the climate and pandemics. Usually, the applications of the theory are the most interesting bits of a book, but somehow this isn't quite as engaging as the theory in the first section, though things really liven up when we get onto economics, and how economists are stuck in the fairly useless state meteorologists were before the great storm of 1987, when they used single-run forecasts, rather than ensembles. He also shows fairly bluntly that economists have failed in the development of the kind of models that can handle a chaotic system like the economy.

Finally, in the third section, Palmer addresses the big picture. He starts with an alternative interpretation of quantum theory that effectively enables hidden variables, using an approach that he describes as involving 'counterfactual indefiniteness', a concept he calls the 'cosmological invariant set' and invariant set theory. How much this will appeal probably depends how you feel about quantum interpretations, or get worried about the idea that until a quantum system interacts with the outside world it doesn't have real values for things like the location of particles. This part felt a bit hand-wavy, partly, I think because it needed too much of the mathematics behind it (which we sensibly don't see) to get a handle on it.

To end this more speculative section, Palmer takes on things like consciousness, free will and God - not bad going for a relatively short book. Finishing The Primacy of Doubt is like getting off one of those exciting roller coaster rides, when your immediate inclination is to think 'I want to do that again, but I'll have a bit of a break first.' I will be reading this book again, without doubt. Remarkable.

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

Comments

  1. The pandemic chapter has a few errors. I can't remember ever seeing a SIR model in which the R included the dead. If exponential growth was rapid I would be very happy with the balance of my savings account. Exponential growth of epidemics is debunked by Farr's Law of 1840. In 1927 Kermack and McKendrick showed logistic growth.

    Now to read the rest of the book.

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  2. I've finally finished the book and think it is a very worthwhile read that has some significant omissions.

    He discusses CovidSim which had its source code released after John Carmack (of Doom fame) had made its output for multi-threaded runs deterministic thus allowing regression tests. A regression tests fails for one of two reasons - either you introduce a bug or fix a bug. What it didn't have, and what the book doesn't mention about this or any other code, is validation tests. Such tests are used by commercial engineering codes to assure their customers that the implementation of the physical models do not have bugs. The author expresses doubt in some of the results of the models but never the model implementation.

    Continuing with epidemiology the standard SIR model has been extended to be stochastic in a number of ways. Several of which are by making the reproduction rate normally distributed. This is the usual lazy assumption by modellers and is debunked by a derivation from first principles that shows it is logit-normally distributed. The author references various models in which he has added noise but never mentions which distribution was used and how that was determined which raises questions over the subsequent conclusions that he draws.

    He seems to rate the Stern Review which I believe to have been a waste of tax payers money on the assumption that its software was seemingly never made available to be updated. The discussion of cost-loss models for tying economics to climate reminded me of Pascal's wager.

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