It is a commonplace, certainly on this blog, that the whole crisis (qua “crisis”) of COVID-19 in the UK, and in the US, has been triggered by the Imperial College computer models of Ian Ferguson. One might quibble about their direct influence on the rest of the world, but given the me-tooism evident in every measure taken by governments, from Cultural Maskism to the cult of eschatological vaccines, it is likely that when America blinked, others would jump.
The 300,000 of us of us who have signed the Great Barrington Declaration (apart from the inevitable trolls) would all say that the models were wrong by an order of magnitude. But our government still appears to believe that their lockdowns averted hundreds of thousands of deaths still waiting to happen if, as the BMA is advocating on no evidence whatsoever, masks are not made mandatory outside. I’m glad I never joined.
But Sweden, as always, is the test case: its deaths have been far, far, lower than the Ferguson model estimated either with or without lockdown. The models have not stood up anywhere in the world, and the data, to 18,000 medical practitioners apart from me, favours the more routine kind of viral behaviour familiar from past epidemics. But – and here’s the point – the reality we’re living in is the world of fear, loss of liberty, excess non-COVID deaths and, probably, poor protection for the vulnerable because of the panic measures on everybody. We are living in the computer model.
I first became interested in models because of a series of blogs on them which I referenced here. The writer of the series showed, amongst other things, how the nature of reality demonstrates how randomized models really can’t be justifiably applied to biological systems, because of their organized complexity. Because at that time my main interest was in biological origins, I immediately saw how population genetics is, par excellence, an idealized mathematical model. This is fine when its simplifications can be tested against the real world if, for example, population genetics is used for understanding the mutation behaviour of micro-organisms or tumours.
But when it is used to describe the history of life in our world over billions of years – a history that, for the most part, cannot be validated by other methods – then the past so constructed is just a computer simulation. When I talk of validation, I should include sciences like palaeontology. But that proves my point because of the numerous levels at which this data-driven science contradicts what population genetics models predict.
For example, it is notorious how morphological phylogenies based on the fossil record frequently differ radically from the phylogenies based on genetic studies (which are computer models, no more and no less). Because the fossil record is, of course, incomplete, and palaeontology is essentially an old science of spades, microscopes and deductive nous, there has been an increasing tendency to favour the high-tech computer results over the crusty old fossils themselves.
The simulation therefore outweighs reality, and it matters. I have been asked to review a Christian book which rehearses the old message that evolution casts doubt on God because it is so wasteful. “We now know that 95% of all species have gone extinct.” Well, actually all we know from the fossil record is that a little more than 150,000 extinct species have been found, well under 10% of those alive today. The 95% is an estimate based on modelling that assumes the truth and sufficiency of gradualist Neo-darwinian theory. If it’s true, then although species are thought to average 5-10 million years duration, 99.5% have left no fossil trace whatsoever. Whereas most fossil species have been found in multiple specimens. Something does not compute.
Only in the last year or two have I got interested in climate change science, finding that the predictions on which our energy and economic policies (and more) are based depend on computer models which have consistently over-predicted actual temperatures despite the constant adjustment of their many parameters. It is criticism of those models as tools for prediction that forms the scientific critique of climate alarmism. Yet the models win out, in the debate, over both the critiques of their methodology and the facts on the ground.
It’s frankly astonishing how much of the baggage associated with climate change is also based on modelling, rather than data. The models say sea levels are drowning the Maldives, though the old beachfront hotels and new airports do not. Yet Prince William has an audience with Attenborough, and predicts that Sandringham will soon be underwater (though if so, it is because Southern England is sinking after the ice age, not because climate change is making the sea rise).
I got into the whole area via Susan Crockford’s work on polar bears, whose official endangered status arose from – you guessed it – simulations based on the climate models. The supporting “data” consists of untruthful documentaries by David Attenborough, et al, fraudulent photos in the National Geographic, and their consequent use as illustrative icons in climate change articles without the need for evidence. The census data, and the appeals to government by Inuit tribes about increasing danger from bears, show that numbers are actually increasing. But in our world teenage tears are shed at the United Nations about their imminent disappearance.
But we’re also increasingly regaled with alarm over the current mass-extinction of every kind of species (not least in Attenborough’s most recent series). Species are going extinct each and every day, we are told. I covered the strange gap between the scare-mongering and the identification of actual species that have gone extinct last year . The rabbits, deer, badgers, foxes and so on still proliferate before my eyes (though numbers of each always vary from year to year, and the trend to warmer weather affects their distributions).
The inapparent disappearances are all generated by computer models, not field observations, backed up by vague impressions that fewer flies get squashed on our car windscreens than of yore. In fact, within the last year I read reports that two of the few species “known” to have become extinct in recent times have been found alive. In other words, we even experience mass extinction whilst nothing in our actual experience demonstrates it. We are living in a simulated reality.
Even people can be made to die invisibly, too. I took for granted the statistics about people dying from respiratory diseases in cities from diesel particulates, until I discovered that these deaths are entirely estimates, based on models which assume the false premise of no-threshold linearity. Nobody has counted real people. The same faulty assumptions underlie many other intimations of mortality based on models, from the numbers who have died from nuclear accidents to the numbers who will die if we don’t ban all kinds of useful substances.
And my latest example is the vast worldwide toll of that former apocalyptic scare, AIDS. My interest has been picqued by discovering for the first time, only yesterday, the HIV conspiracy theory that happens to have been endorsed by Nobel Prize Winner and PCR test inventor, Kary Mullis, as well as some leading virologists.
Mullis’s abandonment of HIV is another, related story: he discovered that the science proving a link between HIV and AIDS didn’t exist, and it appears that, in essence, the re-formulation of Koch’s Postulates to allow a link to be made depends on computer modelling, given the surprising inability to isolate a titre of HIV above which AIDS symptoms can be correlated.
Incidentally, I quickly found the now familiar Groupthink tactics of the scientific “community” at play in that controversy. An article published in Frontiers of Public Health on the history of dissent from the HIV hypothesis was met with a hail of objections from AIDS scientists (none with the calibre of its main proponents), pointing out that only loonies and conspiracy theories have bought into it. The article was first changed to an “opinion” piece, and subsequently, after more pressure from Mainstream AIDS people, retracted:
These commentaries situated the original paper within the context of unsupported, fringe theories on HIV-AIDS. They were intended to ensure that all readers understand that the causal link between HIV and AIDS cannot be called into question.
Now, we all thought science was all about calling old theories into question. But, to quote the possessed Whitaker from the old radio serial The Red Planet again, “Orders must be obeyed without question at all times.” And we didn’t think they gave Nobel Prizes to fringe theorists. But the main point is this: all the AIDS community had to do, in order to silence Mullis, in particular, was to point him to the evidence he requested for the link between HIV and AIDS – or, if there was no such paper in the literature, to write up the definitive science in reply.
Still, I’ve digressed from my subject. It turns out that the stories we have all heard about the collapse of Africa under the burden of AIDS deaths have never been based on a body count, but on computer modelling derived from rates of HIV positive tests in routine pregnancy screening, calculated up to enormously high numbers of deaths, virtual populations of orphans, and simulated deserted villages. That is not to say there is no AIDS, nor a complete absence of deaths, but those who have actually looked for the evidence of “bodies” have found contradictory evidence and become doubters of the modellers’ narrative.
Now, as in the case of Ferguson’s COVID models, there is always a way to maintain the truth of the models over the evidence of our lying eyes. In this case, many countries in sub-Saharan Africa have no reliable death registration system. The exception is South Africa. But since Jacob Zuma did not believe in the link between HIV and AIDs, and deaths are registered by the disease people actually die from (AIDS, of course, being simply a syndrome comprising various conditions), his critics say that “underlying HIV” has been omitted, or deaths otherwise recorded inaccurately. The models are what show the true death rate, and Zuma’s standing apart from the herd proves he was another irresponsible loony.
This may, of course, be true. But the fact is that, as the Spectator article says, the model estimates have repeatedly been revised significantly downwards, meaning that the people who said the piles of bodies did not exist were right, and the AIDS community was wrong. It is worthy of note, though, that the AIDS community made many millions of dollars from the error, as have those in the climate mainstream, green energy industries, vaccine manufacturers and even those in the mainstream of evolutionary theory. Those models can make money.
Meanwhile it does matter on the ground, because it seems that, with the same pathological condition, a positive HIV test is the passport to a share of the billions of dollars of grant money for treatment, whereas a negative test may mean you get no treatment at all. All we see, though, are the model projections.
Computer simulations have their place. Many people seem to enjoy playing out their fantasies in virtual game worlds that may fool them into treating them as reality. But I’m getting rather tired of discovering that much of what I hear about the present, the distant past and the probable future also arises from someone’s over-simplified algorithm, and not from the real world. Systemic simulations and pervasive propaganda – not a healthy mix for a decent life.