The Great Certainty Purge & a Python Script
Summary: We’ve been gently pulling on threads around our newly subcritical society. Here, we pull a bit more...
Societally, we have binged on certainty for decades. And the pandemic now looks like the great certainty purge. A recent Scientific American article drives that point home: they examined the “Language of Science,” the terms the magazine had used over the last 175 years. It is increasingly clear that the only thing we are certain about is that we are less certain.
This increasing uncertainty about causes, effects, and consequences has consequences. For one, as we have been writing here, it is causing people to question the products of science, like medicine. When it is increasingly clear to non-medical people that a field’s recent findings are equivocal, at best, and reversing, all too often, they feel justified in developing their own field epistemology, one that combines small sample sizes, anecdotes, just-so stories, Facebook posts, and YouTube videos. After all, it could hardly be worse, or so they think.
Of course, however, it can be worse. The system of knowledge acquisition that we call modern science has always been equivocal and reversing, but most people didn’t know. We call it the gas station rule: People don’t notice that commodity prices change constantly, until it affects them directly, like posted prices at local gas stations -- and then they see sinister forces behind the changes and clustering. Similarly, with science, that its progress is often halting, uncertain, and even reversing, which it has always been, is of little consequence, until it has to do with something people care about, like masks in a pandemic, vaccines, or, say, red wine. And then, people want to burn the whole place down, or they see sinister, often politicized, forces at work.
But science, for all its flaws, thrives on messiness. As philosopher of science Alan Chalmers has written, “Science progresses by trial and error, by conjectures and refutations. Only the fittest theories survive.” These conjectures and refutations are essential, creating a miasma in which science proceeds, often one dead scientist at a time, to adapt Max Planck’s line. This miasma, in turn, slowly coalesces into a distribution of the possible, a distribution in which we must learn to think if we are to avoid the temptation to wrongly burn the whole edifice down for its required equivocality. As we like to remind people, the wrong Ptolemaic model of the solar system hung on for a thousand years, because it mostly worked, and the Copernican model supplanted it, despite it mostly not working. Science is often, at least briefly, a giant ¯\_(ツ)_/¯ -- if the science works, meaning it provides directions for future work, makes testable predictions, and solves puzzles in prior theories.
So, we are living in the great certainty purge. An overdue one, if we are honest and adult about it. But this great certainty purge forces us into a world where it’s increasingly important to think in distributions. As we said in our last subscriber send:
“...people are struggling to hold in their heads the distribution of possible outcomes. As Stephen Gould wrote in his classic book on evolutionary theory, ‘Full House: The Spread of Excellence from Plato to Darwin,’ in any complex system we need to consider the variation in the distribution of outcomes, and not just get stuck on the median of a particular parameter....Getting stuck on the median of a single parameter in a complex distribution with a wide range of outcomes is, to put it mildly, hugely misleading.”
But humans are ill-equipped to think in distributions of outcomes. We ignore distributions in our daily lives: When we go to the grocery store we don’t explicitly consider the set of all possible outcomes and their probabilities, from meteor strikes, to car accidents, to, voila, returning with milk. We just go. Thinking in distributions is alien to most people, most of the time. We are terrible at thinking in even joint probability terms.
To drive that point home, famously, Tversky and Kahneman (1983) asked participants to solve the following problem:
Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations. Which is more probable?
Linda is a bank teller.
Linda is a bank teller and is active in the feminist movement.
More than 80% of respondents went with the second, that Linda is a bank teller and active in the feminist movement. This is, of course, dead wrong, largely because people can’t think in distributional and joint probabilistic terms. Being a bank teller and being active in the feminist movement is the intersection of two sets, always a less likely outcome than simply being one or the other, no matter how compelling the just-so story might be based on the context.
Sadly, the universe doesn’t care about our unwillingness to think in distributional terms: the layered complexity of varied risks associated with distribution outcomes now acts as an accelerant. And when people don’t realize that something that seems impossible was merely improbable, the intersection of sets that came crashing together, it inflames them into grasping for certainty beyond the standard, eternal Frankl-ian “search for meaning,” giving rise to all-encompassing explanations, from climate change as the sole cause for the current round of California forest fires, to QAnon explaining the equivocations of mask wearing and vaccines.
In the context of this, we’ve started writing lists of things that have gone subcritical -- binging and purging; subject to dart off in unexpected directions at the slightest provocation. These things now include:
Pepperoni and Mozzarella Cheese
Policing
Coins (physical change)
Power (as in utilities)
Lumber supplies
The stock market
Cities
Medical procedures
Schools
Movie theaters, restaurants, bars, happy hours, weddings, conferences, concerts
The economy
Housing demand
Road repairs
The FDA, IRS and United States Postal Service
Cans (soda, beer)
Air Quality
Lest this start sounding like the usual rant about how the world is becoming less certain, THEREFORE BUY THIS BOOK, this isn’t that. Not at all. The world isn't becoming less certain, despite that claim funding so many books, conferences, and futurists’ careers. It's just that we're indirectly noticing that it's always been uncertain, which is making us less certain about certainty itself. We are, in short, losing faith in certainty, even if going from here to there is a dislocating change, like small birds falling out of a comfortable nest from twenty feet up.
We are like Arthur Dent in “The Hitchhiker’s Guide to the Galaxy,”: “Did I do anything wrong today, or has the world always been like this and I've been too wrapped up in myself to notice?” No, the world has always been this way, to a greater or lesser degree, and we are only now noticing. The switch to subcritical forced us to notice. It’s about time.
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Paul’s Python Script:
Speed tracking for a home broadband connection. Useful if you have many people relying on the connection for Zoom calls and the like. The speedtest.py script collects and stores the data locally; uploadtosheets.py sends it to Google Sheets for graphing and data storage. You alternatively use st.py to do both in one routine, but makes it more complicated to view, share, and alert.
You can find the GitHub project here.
Disclaimer: This is meant as a fun project for those that are so inclined. Any requests for tech support, debugging, or assistance will be promptly ignored and trashed.
Sincerely,
The Management
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If you’re not yet a subscriber, and would like to read us writing about “Anesthesia Death Spikes” in the next send, go here.
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Footnotes:
A just-so story is an untestable explanation, usually via a compelling story, for a cultural practice, a biological trait, or behavior of humans or animals. It is a reference to Rudyard Kipling's 1902 Just So Stories, which was a delightful collection of fanciful stories for children pretending to explain animal characteristics, such as the origin of a leopard’s spots.
“A new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die, and a new generation grows up that is familiar with it.” — Max Planck