There are, of course, many items that could be placed in a risk register for our ongoing management of COVID-19. I find myself drawn to those categorizable as, or perhaps triggered by, human perception and behavior. By way of limiting the scope of this post to reasonable attention spans, here are my current top 3:
Statistics
“Smartphones Are Killing Americans, But Nobody’s Counting”
Over the past two years, after decades of declining deaths on the road, U.S. traffic fatalities surged by 14.4 percent. In 2016 alone, more than 100 people died every day in or near vehicles in America, the first time the country has passed that grim toll in a decade. Regulators, meanwhile, still have no good idea why crash-related deaths are spiking: People are driving longer distances but not tremendously so; total miles were up just 2.2 percent last year. Collectively, we seemed to be speeding and drinking a little more, but not much more than usual. Together, experts say these upticks don’t explain the surge in road deaths.
There are however three big clues, and they don’t rest along the highway. One, as you may have guessed, is the substantial increase in smartphone use by U.S. drivers as they drive. From 2014 to 2016, the share of Americans who owned an iPhone, Android phone, or something comparable rose from 75 percent to 81 percent.
The second is the changing way in which Americans use their phones while they drive. These days, we’re pretty much done talking. Texting, Twitter, Facebook, and Instagram are the order of the day—all activities that require far more attention than simply holding a gadget to your ear or responding to a disembodied voice. By 2015, almost 70 percent of Americans were using their phones to share photos and follow news events via social media. In just two additional years, that figure has jumped to 80 percent.
Time will tell what’s really going on but the smartphone distraction hypothesis seems likely. Walk around an urban area and you see many drivers who are obviously distracted. It’s not just texting. People are glued to navigation apps, watching videos, doing all kinds of mentally absorbing activities with their phones while they are behind the wheel. Some people are clearly incapable of having a phone conversation without losing focus on whatever else they are doing, such as driving. They look like pilots flying on instruments down busy streets. Quite a few pedestrians are looking at their phones too, which raises the question how many smartphone-related accidents they are responsible for.
I’m guessing there will eventually be a cultural backlash against distracted driving as there was with drinking and driving, and that rules, customs and technology will be changed to reduce the danger. In the meantime it seems like a good idea to be extra careful.
Socio-Economic Modeling and Behavioral Simulations
In his Foundation series of books, Isaac Asimov imagined a science, which he termed psycho-history, that combined elements of psychology, history, economics, and statistics to predict the behaviors of large population over time under a given set of socio-economic conditions. It’s an intriguing idea. And I have no doubt much, much more difficult to do than it sounds, and it doesn’t sound particularly easy to begin with.
Behavioral modeling is currently being used in many of the science and engineering disciplines. Finite element analysis (FEA), for example, is used to model electromagnetic effects, thermal effects and structural behaviors under varying conditions. The ‘elements’ in FEA are simply building blocks, maybe a tiny cube of aluminum, that are given properties like stiffness, coefficient of thermal expansion, thermal resistivity, electrical resistivity, flexural modulus, tensile strength, mass, etc. Then objects are constructed from these blocks and, under stimulus, they take on macro-scale behaviors as a function of their micro-scale properties. There are a couple of key ideas to keep in mind here, however. The first is that inanimate objects do not exercise free will. The second is that the equations used to derive effects are based on first principles, which is to say basic laws of physics, which are tested and well understood. A similar approach is used for computational fluid dynamics (CFD), which is used to model the atmosphere for weather prediction, the flow of water over a surface for dam design, or the flow of air over an aircraft model. The power of these models lies in the ability of the user to vary both the model and the input stimulus parameters and then observe the effects. That’s assuming you’ve built your model correctly. That’s the crux of it, isn’t it?
I was listening to a lecture on the work of a Swiss team of astrophysicists the other day called the Quantum Origins of Space and Time. They made an interesting prediction based on the modeling they’ve done of the structure of spacetime. In a result sure to disappoint science fiction fans everywhere, they predict that wormholes do not exist. The reason for the prediction is simply that when they allow them to exist at the quantum level, they cannot get a large scale universe to form over time. When they are disallowed, the same models create De Sitter universes like the one we have.
It occurred to me that it would be interesting to have the tools to run models with societies. Given the state of a society X, what is the economic effect of tax policy Y. More to the point, what is cumulative effect of birth rate A, distribution of education levels B, distribution of personal debt C, distribution of state tax rates D, federal debt D, total cost to small business types 1-100 in tax and regulations, etc. This would allow us to test the effects of our current structure of tax, regulation, education and other policies. Setting up the model would be a gargantuan task. You would need to dedicate the resources of an institute level organization with expertise across a wide range of disciplines. Were we to succeed in building even a basic functioning model, its usefulness would be beyond estimation to the larger society.
It’s axiomatic that anything powerful can and will be weaponized. It is also completely predictable that the politically powerful would see this as a tool for achieving their agenda. Simply imagine the software and data sets under the control of a partisan governing body. How might they bias the data to skew the output to a desired state? How might they bias the underlying code? Might an enemy state hack the system with the goal to have you adopt damaging policies, doing the work of social destruction at no expense or risk to them?
Is this achievable? I think yes. All or most of the building blocks exist: computational tools, data, statistical mathematics and economic models. We are in the state we were in with regard to computers in the 1960s, before microprocessors. All the building blocks existed as separate entities, but they had not been integrated in a single working unit at the chip level. What’s needed is the vision, funding and expertise to put it all together. This might be a good project for DARPA.
Quote of the Day 2
Nassim Nicholas Taleb on Facebook:
What we are seeing worldwide, from India to the UK to the US, is the rebellion against the inner circle of no-skin-in-the-game policymaking “clerks” and journalists-insiders, that class of paternalistic semi-intellectual experts with some Ivy league, Oxford-Cambridge, or similar label-driven education who are telling the rest of us 1) what to do, 2) what to eat, 3) how to speak, 4) how to think… and 5) who to vote for.
With psychology papers replicating less than 40%, dietary advice reversing after 30y of fatphobia, macroeconomic analysis working worse than astrology, microeconomic papers wrong 40% of the time, the appointment of Bernanke who was less than clueless of the risks, and pharmaceutical trials replicating only 1/5th of the time, people are perfectly entitled to rely on their own ancestral instinct and listen to their grandmothers with a better track record than these policymaking goons.
Indeed one can see that these academico-bureaucrats wanting to run our lives aren’t even rigorous, whether in medical statistics or policymaking. I have shown that most of what Cass-Sunstein-Richard Thaler types call “rational” or “irrational” comes from misunderstanding of probability theory.
(Via Richard Fernandez.)
Quote of the Day
Charles Murray, quoting himself and Richard Herrnstein from The Bell Curve:
In sum: If tomorrow you knew beyond a shadow of a doubt that all the cognitive differences between races were 100 percent genetic in origin, nothing of any significance should change. The knowledge would give you no reason to treat individuals differently than if ethnic differences were 100 percent environmental. By the same token, knowing that the differences are 100 percent environmental in origin would not suggest a single program or policy that is not already being tried. It would justify no optimism about the time it will take to narrow the existing gaps. It would not even justify confidence that genetically based differences will not be upon us within a few generations. The impulse to think that environmental sources of difference are less threatening than genetic ones is natural but illusory.
In any case, you are not going to learn tomorrow that all the cognitive differences between races are 100 percent genetic in origin, because the scientific state of knowledge, unfinished as it is, already gives ample evidence that environment is part of the story. But the evidence eventually may become unequivocal that genes are also part of the story. We are worried that the elite wisdom on this issue, for years almost hysterically in denial about that possibility, will snap too far in the other direction. It is possible to face all the facts on ethnic and race differences on intelligence and not run screaming from the room. That is the essential message [pp. 314-315].