As the once-confident vision of a global world broke down under the coronavirus pandemic, the press sought to provide authoritative guidance to the public, clothed in the mantle of what it presumed was science. But they failed, by first grossly underestimating then drastically overestimating the pandemic.
As it unfolded in the media, the story of the pandemic was initially that of a nonevent. On January 31, Vox ran a supposedly comprehensive “explainer” about the coronavirus. There was no need for Americans to wear protective face masks, Vox said, and “really no reason to worry.” On Twitter, Vox was still more blunt: “Is this going to be a deadly pandemic? No.” The hectoring tone and sham certitude are Vox specialties. But Vox wasn’t alone in dismissing the virus. USA Today, the Washington Post, Canada’s National Post, and many other outlets treated the Wuhan virus (as it was then known) less as a matter of objective concern than an instance of mass hysteria.
The City-Journal argues that the media’s main error was depending on politicized science. “Their cardinal error, in almost every case, was to rely on the WHO, an organization at best egregiously mistaken and at worst politically compromised, carrying water for the Chinese Communist Party and President Xi Jinping.” But reliance on a poisoned source was only part of the reason the Explainers failed. “Scientific papers” authored by institutions besides WHO were also wildly wrong.
Perhaps the error lay in what the media thought science did versus what it actually does. Science is less about maintaining a stock of authoritative pronouncements than looking for answers through a process of inquiry that often takes a long time. Until then, the honest answer to a new problem is often “I don’t know.”
Dr. Anthony Fauci–and this is not meant as criticism–has epitomized the public-health diagnostic process with multiple, incorrect, early pronouncements … Dr. Fauci now espouses the opposite of each of his earlier statements, but there is nothing wrong with that. As economist John Maynard Keynes purportedly said, “When the facts change, I change my mind–what do you do, sir?”
The lesson: in evolving public-health emergencies–despite the demand from some politicians to “Listen to the science!”–science alone can’t always determine the best course of action. Policymakers have to balance multiple, competing factors while working with imperfect information and uncertain science.
The phrase “uncertain science” is more threatening to the authority of politicians and institutions than to the scientific enterprise itself, which has long grappled with limits on what humans could ultimately know. What the public calls “science” “began with Francis Bacon moving the study of Nature from haphazard experience to designed experiments, and Galileo placing scientific knowledge within the frame of mathematics, not requiring explanation in terms of human physical categories.” For so long as a phenomenon could be mathematically modeled and predicted, it was valid even if could not be expressed in common-sense terms. Hence the expression “trust the science.” This epistemology was so successful it seemed the scientific method had reached its final form even if purchased at the price of incomprehensibility.
The advent of quantum mechanics in the first part of the Twentieth Century brought it to light: a theory may be preposterous from the perspective of human intelligibility but lead to predictions that agree with empirical observation–and therefore be scientifically valid. Man can possess knowledge beyond the limits of his physical understanding.
You just had to “trust the science,” even if you could not explain it. The Galilean-Newtonian paradigm shift made the modern bureaucratic state and secular millennial movements possible by seemingly guaranteeing that scientifically informed authority could control events and thereby human destiny. It was the foundation of authority and legitimacy.
But toward the end of the 20th century, scientists began to bump into complex systems that could not be modeled even in principle. In the words of Edward Dougherty, “system complexity has resulted in data requirements that cannot be met. Model parameters cannot be accurately estimated, thereby resulting in model uncertainty. On the other hand, model simplification means that there can be many models aiming to describe the same complex phenomena, all being inherently partial and hence yielding different predictions.”
Society was up against phenomena it could neither understand in human terms nor adequately describe mathematically. Some of the difficulties in predicting complex systems are that they are affected by factors outside the model’s boundaries. The whole can generate behaviors absent from the parts. They can often respond in different ways to the same input, depending on an unknowable context.
These problems represented a potential dagger through the heart of reductionism since, as Dougherty puts it, “The inability to validate theory via observations constitutes an existential crisis for science.” But while science itself may regard complex systems as merely another challenge to be solved, perhaps by some future Galileo-Newton or perhaps never, for activists and Explainers it represents an immediate threat to their political power because complex phenomena make up the bulk of what they promised to control. That they can’t model them means they can no longer say “trust the science” nor “trust the institutions” in the matters they wish to govern most.
Examples of complex systems are Earth’s global climate, organisms, the human brain, infrastructure such as power grid, transportation or communication systems, social and economic organizations (like cities), an ecosystem, a living cell, and ultimately the entire universe.
It is perhaps no accident that the most radical critics of the Galilean-Newtonian paradigm are from the field of biology because that is where some of the most complex subjects are. Stuart Kauffman, “American medical doctor, theoretical biologist, and complex systems researcher who studies the origin of life on Earth” provocatively wrote that the old paradigms are inadequate to predict the evolution of the biosphere.
The aim of this article is to demonstrate that the mode of understanding in physics since Newton, namely differential equations, initial and boundary conditions, then integration which constitutes deduction, which in turn constitutes “entailment”, fails fundamentally for the evolution of life. No law in the physical sense, we will argue, entails the evolution of life. If we are correct, this spells the end of “strong reductionism”, the long held belief that a set of laws “down there” entails all that happens in the universe. More, if no law entails the evolution of life, yet the biosphere is the most complex system we know of in the universe, it has managed to come into existence without an entailing law. Then such law is not necessary for extraordinary complexity to arise and thrive. We need new ways to think about how current life organization can have come into being and persists. Those ways include coming to understand how what we will call organisms as “Kantian wholes” co-create their worlds with one another.
Whether or not the prophets of the challenge of complexity have correctly argued that the end of “strong reductionism” is at hand, the practical difficulties of predicting biological events were openly underlined by the travails of the Covid-19 pandemic model-builders. The Explainers failed to provide authoritative guidance to the public not simply because they relied on politicized science but because science itself could not tell them precisely what to say.
Matt Ridley at the Telegraph writes that science may soon beat back the coronavirus epidemic. “From vaccine triumphs to leadership learning curves, we can finally dare to hope for a breakthrough.” But that doesn’t mean we don’t live in a dangerous era of increasingly unforeseeable consequences–that Beijing or someone else is not thinking up the next nightmare that we will struggle to understand let alone overcome. In the complex 21st century of our own making, we are losing the arms race against ourselves. At this watershed moment Dougherty says there appear to be four basic options for science:
- Dispense with modeling complex systems that cannot be validated.
- Model complex systems and pretend they are validated.
- Model complex systems, admit that the models are not validated, utilize them pragmatically where possible, and be extremely prudent when interpreting them.
- Strive to develop a new and perhaps weaker scientific epistemology.
At all events, authority should be more humble when attempting climate engineering, social engineering, and biological experimentation. Humanity can still advance, perhaps more swiftly than before, but on an engineering basis, mindful of the danger and testing its footing at each step. But until science can develop a paradigm that can cope with complex systems, the elites can never again beat forward with the elan and confidence of gods. They — and their Explainers — will have to venture onward as mere men, bereft of certainty, sustained by faith and courage, mindful they are in a very big universe.
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