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In “Conservatives are Too Quick to Dismiss the Rise of the Robots,” James Pethokoukis worries that whereas in the past, technology has given rise to new jobs to replace those lost to innovation, this time it may be different.
James provides us with an excellent specimen of the kind of thinking that constantly causes macroeconomists, politicians, and other self-styled high-level thinkers to make serious errors when analyzing changes to economies and human societies. I’m not picking on James, who’s an otherwise excellent analyst, but on this error, which is so common that it really needs to be discussed.
This phrase, in particular, jumped out at me:
Just think about the progress made in autonomous vehicles and the fact that the most common job in most states is that of truck driver.
His statement displays a top-down approach to analyzing the problem. Truck drivers drive vehicles. Soon, vehicles will be autonomous. So, no more truck drivers. But this kind of of analysis doen’t reflect the true complexity of truck driving.
And this isn’t just about truck drivers. To explain the problem and why it matters so much, I need to digress. The economy is a complex system. Society itself is a complex system. Workers in a society have to be productive within this complexity. These systems are very different from, say, a complicated machine. A complicated machine can be understood in a reductionist way: Take apart a motor; understand the various parts and what they do; and you can understand what the motor does. If you have full understanding of the motor, you can treat it like a black box, with inputs and outputs, and ignore the complicated workings inside. Engineers and scientists use this type of analysis to break down complicated problems and organize them into simpler ones.
Complex systems turn this upside down. A complex system looks simple on the surface, but becomes increasingly complex as you drill down. Complex systems are more than just a collection of parts – their behavior is governed not just by what each part does, but by the interactions among the parts. For example, you can’t understand a brain just by learning what neurons do. You must also — at least — understand the web of billions of neurons and the interactions among them.
The other problem with analyzing complex systems from the top down is that these systems function by means of feedback from the bottom, which causes constant iteration. If the price of steel goes up, that information changes the behavior of steel producers and consumers. That, in turn, causes its price to change again. The new price may create more consumers or more producers, or cause manufacturers to substitute other materials, which in turn causes the prices of those materials to change, and so on, ad infinitum.
You can think of such systems as a kind of self-programming computer: They constantly take in data, process it, and in response change the output. This process of feedback and constant change makes these systems very sensitive to initial conditions; as a result, seen from on high, they are opaque, and behave unpredictably Hence the most common word in a macroeconomist’s vocabulary is apt to be “unexpectedly.”
Conservatives tend to understand this, because the thinkers we tend to read and follow understood it. Adam Smith’s phrase, “the invisible hand,” suggests how well he understood the way emergent properties drive complex systems. Hayek’s opposition to “scientism” and the pretense of knowledge were an evocation of complex systems theory. In fact, Hayek is considered one of the early contributors to that field.
Statists believe that the economy and society can be treated the same way. If they can find the levers that control the economy, the smart people at the top can push and pull on them and drive the ship of state. Social scientists want to be mathematical and scientific, just like the engineers and physicists, so they go through contortions to create models decorated by a few numbers and formulas into which they can be plugged. They use these to justify applying “scientific” techniques to managing people and the interactions between them. This is what Hayek called “scientism,” not science.
When macroeconomists reduce the economy to aggregate variables like GDP, employment, capital, inflation, or the consumer price index, they’re abstracting away everything that really matters in an economy in favor of a few numbers that are amenable to mathematical modeling. These numbers may indeed be useful when trying to understand the state of an economy, but the variables can’t be tweaked by central planners in sure confidence that the outcomes will be predictable. Attempts to do so lead to unintended consequences and to the destruction of the feedback forces the system needs to remain healthy.
If you’ve never read it, I highly recommend reading the classic essay I, Pencil by Leonard Read. It’s a perfect description of the way complex systems deceive people who look at them only from a very high level. If you ask someone how hard it is to build a pencil, they might think about it and say, “Oh, not hard. You need a wooden dowel, a hole drilled in it, and some lead or graphite to fill the hole. Glue it in, and you’re done.” But as Read’s pencil replied in the first person, “Simple? Yet, not a single person on the face of this earth knows how to make me.“
The essay drills down into the construction of the pencil. You need some wood. Fine. Where do you get it? Will any wood do? Or are there special characteristics? And how do you get this wood? Chopping down trees? How do you do that? With an axe or a saw? How do you make an axe or a saw? Oh, you need a steel axe head. How do you make steel? And so on, and so on. Spoiler alert: By the time you walk down just a couple of steps of production, you find efforts that require thousands of people, each with specialized knowledge the others do not share. It’s an incredibly complex endeavor, and the amount of economic and physical coordination required to make pencils is astounding.
The reality of complex systems is the reason conservatives oppose central planning. Hayek knew this, and it formed the core of his arguments against an overweening state, the supposed superiority of macroeconomic modeling, and decision-making by central authorities.
This failure to see hidden complexity is not limited to politicians and economists. Most engineering projects that run over budget, or that fail completely, do so because of a failure to take into account hidden complexity lurking in the details. Software engineering has moved away from top-down design and toward bottom-up, iterative development cycles precisely because it better matches the real world. The largest, most carefully thought-out architecture developed by people in the head office generally doesn’t survive contact with the real world, which is why that type of development isn’t done much any more.
Now back to the truck driver. Can a robot drive a truck? Maybe, on a well-documented road, and under unexceptional circumstances. From the high-level view, that answers the question. But if you ask a truck driver what he does, you might find that he also loads and unloads cargo. And if you dig into that activity, you might find that he needs to rely on years of experience to know how to do that safely and efficiently with the load properly balanced and secured. He may be required to act as an agent for the company, collecting payment and verifying that the shipment matches the manifest. The truck driver is also the early warning system for vehicle problems. He has the knowledge and judgment to be able to tell if something is wrong. A rattling sound on a road full of debris might not be a problem. The same rattle heard on a smooth road? Might be a problem.
The truck driver is the coordinator of on-road repairs. His presence protects the cargo from theft or tampering. He deals with many different end-customers, many of whom are still using old-fashioned paper manifests and invoices a computer can’t deal with. He may use his judgment to determine if a check can be accepted for delivery. Each customer’s loading dock may have hazards and unique maneuvering difficulties. Then there are the ancillary benefits of human truck drivers – they cement relationships with customers. They spot opportunities. They report traffic accidents or crime to the police. They notice damaged goods in a shipment. Sleeping in the truck protects it from theft.
These are the things off the top of my head, and I’m not a truck driver. I’ll bet if you asked Dave Carter what he does, he could go into much greater detail. And if you asked other people in the chain, they’d have their own set of complexities that are part of the entire work process called “truck driving.”
Robots don’t do complexity well. They are excellent at repetitive tasks, or tasks that can be extremely well defined, and which have a fixed set of parameters and boundary conditions. A robot on an assembly line knows exactly what it has to do, and the list of potential failures (parts out of alignment, defects in materials, etc.) are well known. Even a self-driving robot car needs to know what the road looks like — Google’s cars use pre-mapped road data — and it can’t deal with situations that are very far outside the norm.
We are making strides here, and Google’s robot cars have a surprising amount of autonomous decision-making capability when it comes to things like cars stopping in front of them suddenly and obstacles on the road. But that’s a far cry from the kind of generalized human judgement required in most occupations — which is why the robots won’t be taking over any time soon.
I can’t say the same for the central planners. We seem to be stuck with them.