The Intellectual Asymmetry that should be rescuing us from bad policy.

“Oh, I don’t know enough science to comment on that- I just have to trust the experts!” How often do you hear that when you try to persuade someone to treat with greater scepticism the claims of the climate catastrophists, or of the lock-down-and-vaccinate Covidista? It’s hard to tell whether they are simply trying to shut down a conversation that’s heading in a direction with which they are not comfortable, or whether they sincerely believe themselves to be incapable of casting a critical eye over the conclusions of the priesthood that seems to be exercising an ever-greater control over our lives. Either way, the appeal to the authority of ‘experts’ is a deeply frustrating device. Apart from the logical fallacy inherent in the appeal to authority, it’s a statement that is usually simply untrue.

I used to think that the reason that climate catastrophism had so successfully – and destructively – captured the public policy sphere was that not enough people had the grounding in rudimentary physics that I took to be a standard part of the curriculum of anyone who completed a normal high school education. The cataract of repressive, socially destructive, and scientifically perverse measures taken in response to Covid made it clear to me that the problem is not simply that people have no grasp of a particular field of science. The problem is much deeper – they don’t understand the nature of science itself. And by that, I suppose I mean they misunderstand the Scientific Method.

Observation trumps theory – every time

The Scientific Method creates a virtuous circle by means of which knowledge about the natural world may, but is not guaranteed, to progress. That circle begins and ends with observation of the real world. That no guarantee of progress exists is important, since the starting condition of scientific enquiry is a one of principled ignorance, and the scientist must be willing to return to it if the rules of the method do not produce an unequivocal result.

So much for the starting condition of enquiry; from this position, enquiry proceeds in steps:

1. The first step in scientific enquiry is observation. The enquirer observes two or more phenomena that interest him or her, and that appear to be related.
2. Next, the scientist forms one or more falsifiable hypotheses which might explain the observed correlation between the observed phenomena. Falsifiability is a cardinal attribute of a scientific hypothesis. Climate ‘science’ has long since proceeded in violation of this principle, first predicting terminal aridity, then catastrophic flooding, until today, when any form of weather which inconveniences people is attributed to ‘anthropogenic climate change’. Note, also, the almost complete abandonment of the term ‘global warming’, in favour of ‘climate change’. Since climate is, by definition, in a perpetual state of change, the insistence that observed changes are attributable to human activity is almost a textbook example of an unfalsifiable hypothesis.
3. The next step is to design an experiment to test the hypothesis – that is, to determine whether the hypothesised relationship between the observed phenomena exists, and if so, to quantify it. Not all experiments are conducted in a laboratory. In the case of climatology, much experimentation takes place retrospectively, using data about the past climate obtained from such sources as ice cores and tree growth rings.
4. The final step is to observe the results. This is where the cruelty comes in. Because if the observed results differ from those predicted by the hypothesis, the hypothesis is disconfirmed, and must be rejected. In the words of the great Richard Feynman, ‘It doesn’t matter how beautiful your theory is, it doesn’t matter how smart you are. If it doesn’t agree with experiment, it’s wrong.’

That the work of months or years, and the conjectures of a brilliant mind, may be vitiated by a single observation is a bitter pill for the possessor of that mind to swallow, but it gets worse. Because while the mind of the scientist who conceived the hypothesis and designed and ran the experiment to test it may have been brilliant, the success or failure of the hypothesis will be readily apparent to anyone who can read and understand the results predicted by the hypothesis, and the observed results of the experiment. Broadly speaking, that’s you and me. We don’t have to understand the mechanism by which the observed phenomena interact – indeed many useful hypotheses have been successfully tested without any such mechanism being declared. We, however, merely need to be able to tell whether the outcome of the experiment was what the hypothesis predicted.

Cruel though it may be, this scientific method has served mankind extraordinarily well, enabling us to live longer, roundly disconfirming the gloomy hypothesis of Malthus by feeding us in abundance, taking us to the moon, and giving us the comforts and conveniences we take for granted. It’s a habit of mind that we abandon at our peril. It’s a way of thinking that was central to the way I was taught science in the 60s. Every new principle was introduced with a preamble in which the experimental basis upon which it rested was expounded. For boys who were, with few exceptions, going to pursue careers in which scientific wrangling would play little part, understanding the Scientific Method was seen as equally important as grasping the scientific knowledge it had yielded.

In the heyday of science, academics would gleefully share their work with their peers, urging them to scrutinise it for error.

But that was then. These days, we have Prof Phil Jones, of the UEA, and part of the cabal of arithromancers at the centre of the 2009 ‘Climategate’ scandal, retorting to someone asking him to share his  work” “Why should I let you have the data when all you want to do is find something wrong with it?” These days, almost nobody seems to be able to tell the difference between experimentally validated science, fit to inform public policy, and a merely plausible conjecture about the natural world that, however much respect it deserves, has yet to survive the rigours of scientific method.

There are probably several reasons why this has come about. Clearly, high school teaching has abandoned the ‘nullius in verba’ approach which I was taught. That has left pretty much the entirety of the Mainstream Media incapable of properly interrogating the claims of zealots bent upon influencing public policy. In the field of academic science itself, career advancement came increasingly to depend upon publication. Testing a brilliant hypothesis by running an experiment which ultimately compels you to reject the hypothesis leaves little to publish, despite the fact that, to quote Darwin; “To kill an error is as good a service as, and sometimes better than, the establishing of a new truth or fact”.

The availability of computers has exacerbated the pernicious effects of this ‘publish or perish’ mindset, by enabling many mediocre scientists to make a career out of numerical modelling, or from using computer-aided statistical analysis to tease out of a mass of inherently noisy data a signal with a claim to informing public policy. The worst example of this ‘arithromancy’ is the climate scare, variously known as ‘global freezing’, ‘global warming’ and now ‘climate change’, as its hucksters attempt, with enviable success, to stay one step ahead of the failure of each of the hypotheses implied by these terms.

The predictions of doom upon which these second-rate scientists have rested their successful capture of the commanding heights of policy-making, and by which the ruination of the economy of the West is justified, exist solely in these models. Observationally, the earth’s temperature trajectory is indistinguishable from a planet emerging, with what ought to be gratitude, from the little Ice Age. But observation be damned, it seems; if the observed data differ from the model outputs, it must be the former that are wrong, and not the latter!

The ignorance and gullibility of both the media and the political class have meant that these modelled predictions have successfully been sold to them by the paladins of the climate ‘science’ industry as if they were in fact experiments, and their absurd results scientifically validated ‘fact’.

It’s worth quoting Cambridge Professor Michael Kelly, commenting on the ‘Climategate’ revelations in 2009: “I take real exception to having simulation runs described as experiments (without at least the qualification of ‘computer’ experiments). It does a disservice to centuries of real experimentation and allows simulations’ output to be considered as real data. This last is a very serious matter, as it can lead to the idea that real ‘real data’ might be wrong simply because it disagrees with the models! That is turning centuries of science on its head.”

He continued:

“My overall sympathy is with Ernest Rutherford: “If your experiment needs statistics, you ought to have done a better experiment.”

In truth, all of the models, when their outputs are compared with observed temperatures, ‘run hot’. In time-honoured scientific tradition, these models would be treated as failed hypotheses, and evidence that those who designed them had an imperfect understanding of the climate system they were intended to mimic – or perhaps no understanding at all. But so inadequate is the scientific grasp of our politicians and media that they meekly swallow the dire predictions of the ‘grantafarians’, and enact the swathe of ruinous policy that they demand.

Climate modelling is subjecting the Western world (with few exceptions, the developing world pays mere lip-service to it) to a slow-motion civilisational train smash. When Covid, a rather nasty variant of the common cold, escaped a gain-of-function lab in China, the modellers were again consulted by policy-makers, and this time managed to wreak in months destruction on a scale it has taken the climate modellers decades to achieve. The notorious and arguably sociopathic Imperial College arithromancer Prof Neil Ferguson led the way, claiming that half a million people would die if no public measures were introduced to curb the contagion.

Here, again, a simple question, requiring no understanding of the theoretical entrails of the models themselves, would have sufficed to disqualify Ferguson’s advice for the purposes of policy-making. All that was necessary was to enquire of Ferguson if he could demonstrate the predictive skill of his modelling, by reference to earlier epidemics he had modelled. Had this been done, he would have been forced to admit:

• In 2002, he produced modelled predictions of death from Bovine Spongiform Encephalitis (BSE) of ‘up to 50,000, rising to 150,000 if the disease jumped species to sheep’. In the event, 177 British people died of BSE.
• In 2005, he predicted a death toll from ‘bird flu’ of 200,000. The eventual toll was 282.
• In 2009, he asserted that the swine flu then at large had a case fatality rate of 0.4%, leading him to predict a death toll in the UK alone of 65,000.  In the real world to which the professor appears to be stranger, 457 people died – a CFR of 0.026%.

This litany of modelling failure is striking for its consistency, and for the absence of any evidence that the prof was learning from his errors. But the point is, we can. You don’t have to be an epidemiologist to spot that the guy is utterly – and dangerously – hopeless at his profession, and should not be allowed within cooee of policy-making. Yet successive policy makers in Britain and here in Australia sat at his clay-muddied feet, and we are, three social-distanced, economy-destroying childhood-ruining, mask-wearing years later, and Ferguson is still walking the streets a free man, instead of behind bars, where he belongs.