If there’s one silver lining of the Wuhan Plague, it’s that the fallacy of computer modelling has been stripped bare for all who care to see.
Over-reliance on modelling has been one of the biggest factors in the snowballing crisis in modern science (alongside a broken peer-review system and stultifying groupthink). In place of empirical evidence and real-world experiments, computer models are increasingly touted as “evidence”.
Anyone who has ever worked with computer modelling should be familiar with the acronym GIGO: Garbage In, Garbage Out.
There is an old joke that a geologist, a physicist and an economist are marooned on a desert island with just a can of soup but nothing to open it with. The geologist says ‘let’s use a rock to smash it open’ and the physicist suggests ‘let’s use the sun and heat it up till it blows open’. Then the economist steps forward and says ‘your suggestions will ruin the soup, let’s just assume we have a can opener.’
In his political parable, Dune, Frank Herbert’s “human computers”, the mentats, are absolutely dependent on reliable data. The villainous Baron Harkonnen tricks a mentat into making disastrously erroneous conclusions by deliberately feeding him false data. Modellers rarely deliberately feed false data into their models, but they do make assumptions which are often wildly prejudiced (for instance, climate models assume that solar output is a constant, which we know for a fact is not true).
Assumptions are the mother of all stuff-ups. And perhaps the mother of all those has been the Victorian government’s coronavirus response. Dan Andrews has claimed that his disastrous lockdown policies have been decided by a ‘supercomputer’. Alarm bells would be ringing for anyone who has experience with actual models.
Unfortunately, too much of modern science and public policy fetishises computer modelling. Because it seems high-tech, it must be true.
For those unfamiliar with the complexity of any topic, modelling gives a reassuring level of precision. The outputs are clean and precise. Graphs can demonstrate an unerring prediction of the future and all can be reassured about the correctness of their policies.
These outputs provide a false precision that hides the making of the sausage and the, often, hundreds of assumptions that must be made to generate a precise estimate. What you get out of the model is only as good as what you put in and there is often little scrutiny of what gets put in.
The modelling that Dan Andrews relies upon makes 26 different assumptions about a variety of inputs to the model. One of the crucial assumptions is that an ‘unmitigated epidemic’ would result ‘in approximately 60 per cent infection across the population.
In effect, the modellers assume that if we don’t take any action to control the coronavirus’s spread then 60 per cent of us will get it.
Both assumptions are wrong.
In reality, even if the government did not impose severe lockdown measures, individuals themselves will take preventative action in the middle of a pandemic. That is not to say that government’s shouldn’t do anything to protect public health; just that it is naive to assume in the absence of government action, all people would go about their lives as normal.
The more severe criticism is that this 60 per cent ‘herd immunity’ rate is out of date. The most recent evidence indicates that herd immunity could be achieved at much lower rates of spread than that figure thanks to an innate immunity that would appear to be present in some people.
The observed reality is that the COVID-19 herd immunity threshold could be as low as 10 to 20 per cent according to Oxford University’s Centre for Tropical Medicine and Global Health.
But, hey, who are you going to believe? The real world or the computer model?
Too often, scientists and policy makers make the wrong choice of which to believe.
Remember we were told at the start of the pandemic that 50,000 to 150,000 Australians could die. These predictions have proven to be widely inaccurate.
As the great Richard Feynman said, if it disagrees with the evidence, then it’s wrong.
What’s been proven true of pandemic modelling is every bit as true for climate models. The only difference is that the obvious wrongness of pandemic modelling was shown up in just weeks and months.
Climate change modelling regularly forecasts armageddon-like outcomes if there is no action taken to try to change the temperature of the globe. These models of global climate have to make gigantic assumptions over weather patterns, the role of water vapour and the formation of clouds. It is no surprise then that the models often generate incorrect forecasts[…]
When I was last on Q&A, the first question I was asked was: given we have listened to the experts on coronavirus, why shouldn’t we listen to the experts on climate change?
Well, we have the answer to that: we listened to the experts on the Wuhan Plague, and they were disastrously, expensively, ruinously, deadly wrong.
Now let’s apply that lesson to the garbage coming out of the “experts on climate change”.
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