Book Club: The 10,000 Year Explosion pt 1

 

5172bf1dp2bnl-_sx323_bo1204203200_For most of he last century, the received wisdom in the social sciences has been that human evolution stopped a long time ago–in the most up-to-date version, before modern humans expanded out of Africa some 50,000 years ago. This implies that human minds must be the same everywhere–the “psychic unity of mankind.” It would certainly make life simpler if it were true.

Thus Cochran and Harpending fire the opening salvo of  The 10,000 Year Explosion: how Civilization Accelerated Human Evolution. (If you haven’t finished the book yet, don’t worry–we’ll discuss one chapter a week, so you have plenty of time.)

The book’s main thesis–as you can guess by reading the title–is that human evolution did not halt back in the stone age, but has accelerated since then.

I’ve been reading Greg and Henry’s blog for years (now Greg’s blog, since Henry sadly passed away.) If you’re a fan of the blog, you’ll like the book, but if you follow all of the latest human genetics religiously, you might find the book a bit redundant. Still, it is nice to have many threads tied together in one place–and in Greg & Henry’s entertaining style. (I am about halfway through the book as of this post, and so far, it has held up extremely well over the years since it was published.)

Chapter One: Conventional Wisdom explains some of the background science and history necessary to understand the book. Don’t worry, it’s not complicated (though it probably helps if you’ve seen this before.)

A lot of of our work could be called “genetic history.” … This means that when a state hires foreign mercenaries, we are interested in their numbers, their geographic origin, and the extent to which they settled down and mixed with the local population. We don’t much care whether they won their battles, as long as they survived and bred. …

For an anthropologist it might be important to look at how farmers in a certain region and time period lived; for us, as genetic historians, the interesting thing is how natural selection allowed agriculture to come about to begin with, and how the pressures of an agricultural lifestyle allowed changes in the population’s genetic makeup to take root and spread.

One of the things I find fascinating about humans is that the agricultural revolution happened more or less independently in 11 different places, all around 10,000 years ago. There’s a little variation due to local conditions and we can’t be positive that the Indus Valley didn’t have some influence on Mesopotamia and vice versa, but this is a remarkable convergence. Homo sapiens are estimated to have been around for about 200-300,000 years, (and we were predated by a couple million years of other human ancestor-species like Homo erectus)  but for the first 280,000 years or so of our existence no one bothered to invent agriculture. Then in the span of a few thousand years, suddenly it popped up all over the darn place, even in peoples like the Native Americans who were completely isolated from developments over in Asia and Africa.

This suggests to me that some process was going on simultaneously in all of these human populations–a process that probably began back when these groups were united and then progressed at about the same speed, culminating in the adoption of agriculture.

One possibility is simply that humans were hunting the local large game, and about 10,000 years ago, they started running out. An unfortunate climactic event could have pushed people over the edge, reducing them from eating large, meaty animals to scrounging for grass and tubers.

Another possibility is that human migrations–particularly the Out of Africa Event, but even internal African migrations could be sufficient–caused people to become smarter as they encountered new environments, which allowed them to make the cognitive leap from merely gathering food to tending food.

A third possibility, which we will discuss in depth next week, is that interbreeding with Neanderthals and other archaic species introduced new cognitive features to humanity.

And a fourth, related possibility is that humans, for some reason, suddenly developed language and thus the ability to form larger, more complex societies with a division of labor, trade, communication, and eventually agriculture and civilization.

We don’t really know when language evolved, since the process left behind neither bones nor artifacts, but if it happened suddenly (rather than gradually) and within the past 300,000 years or so, I would mark this as the moment Homo sapiens evolved.

While many animals can understand a fair amount of language (dogs, for instance) and some can even speak (parrots,) the full linguistic range of even the most intelligent apes and parrots is still only comparable to a human toddler. The difference between human language abilities and all other animals is stark.

There is great physical variation in modern humans, from Pygmies to Danes, yet we can all talk–even deaf people who have never been taught sign language seek to communicate and invent their own sign language more complex and extensive than that of the most highly trained chimps. Yet if I encountered a group of “humans” that looked just like some of us but fundamentally could not talk, could not communicate or understand language any more than Kanzi the Bonobo, I could not count them members of my species. Language is fundamental.

But just because we can all speak, that does not mean we are all identical in other mental ways–as you well know if you have ever encountered someone who is inexplicably wrong about EVERYTHING here on the internet.

But back to the book:

We intend to make the case that human evolution has accelerated int he past 10,000 years, rather than slowing or stopping, and is now happening about 100 times faster than its long term average over the 6 million years of our existence.

A tall order!

220px-San_tribesman
Some anthropologists refer to Bushmen as “gracile,” which means they are a little shorter than average Europeans and not stockily built

To summarize Cochran and Harpending’s argument: Evolution is static when a species has already achieved a locally-optimal fit with its environment, and the environment is fairly static.

Human environments, however, have not been static for the past 70,000 years or so–they have changed radically. Humans moved from the equator to the polar circle, scattered across deserts and Polynesian islands, adapting to changes in light, temperature, disease, and food along the way.

The authors make a fascinating observation about hunting strategies and body types:

…when humans hunted big game 100,000 years ago, they relied on close-in attacks with thrusting spears. Such attacks were highly dangerous and physically taxing, so in those days, hunters had to be heavily muscled and have thick bones. That kind of body had its disadvantages–if nothing else, it required more food–but on the whole, it was the best solution in that situation. … but new weapons like the atlatl (a spearthrower) and the bow effectively stored muscle-generated energy, which meant that hunters could kill big game without big biceps and robust skeletons. Once that happened, lightly built people, who were better runners and did not need as much food, became competitively superior. The Bushmen of southern Africa…are a small, tough, lean people, less than five feet tall. It seems likely that the tools made the man–the bow begat the Bushmen.

Cro-magnons (now called “European Early Modern Humans” by people who can’t stand a good name,) were of course quite robust, much more so than the gracile Bushmen (Aka San.) Cro-magnons were not unique in their robustness–in fact all of our early human ancestors seem to have been fairly robust, including the species we descended from, such as Homo heidelbergensis and Homo ergaster. (The debate surrounding where the exact lines between human species should be drawn is long and there are no definite answers because we don’t have enough bones.)

We moderns–all of us, not just the Bushmen–significantly less robust than our ancestors. Quoting from a review of Manthropology: The Science of the Inadequate Modern Male:

Twenty thousand years ago six male Australian Aborigines chasing prey left footprints in a muddy lake shore that became fossilized. Analysis of the footprints shows one of them was running at 37 kph (23 mph), only 5 kph slower than Usain Bolt was traveling at when he ran the 100 meters in world record time of 9.69 seconds in Beijing last year. But Bolt had been the recipient of modern training, and had the benefits of spiked running shoes and a rubberized track, whereas the Aboriginal man was running barefoot in soft mud. …

McAllister also presents as evidence of his thesis photographs taken by a German anthropologist early in the twentieth century. The photographs showed Tutsi initiation ceremonies in which young men had to jump their own height in order to be accepted as men. Some of them jumped as high as 2.52 meters, which is higher than the current world record of 2.45 meters. …

Other examples in the book are rowers of the massive trireme warships in ancient Athens who far exceeded the capabilities of modern rowers, Roman soldiers who completed the equivalent of one and a half marathons a day, carrying equipment weighing half their body weight …

McAllister attributes the decline to the more sedentary lifestyle humans have lived since the industrial revolution, which has made modern people less robust than before since machines do so much of the work. …

According to McAllister humans have lost 40 percent of the shafts of the long bones because they are no longer subjected to the kind of muscular loads that were normal before the industrial revolution. Even our elite athletes are not exposed to anywhere near the challenges and loads that were part of everyday life for pre-industrial people.

Long story short: humans are still evolving. We are not static; our bodies do not look like they did 100,000 years ago, 50,000 years ago, nor even 1,000 years ago. The idea that humans could not have undergone significant evolution in 50–100,000 years is simply wrong–dogs evolved from wolves in a shorter time.

Dogs are an interesting case, for despite their wide variety of physical forms, from Chihuahuas to Great Danes, from pugs to huskies, we class them all as dogs because they all behave as dogs. Dogs can interbreed with with wolves and coyotes (and wolves and coyotes with each other,) and huskies look much more like wolves than like beagles, but they still behave like dogs.

The typical border collie can learn a new command after 5 repetitions and responds correctly 95% of the time, whereas a basset hound takes 80-100 repetitions to achieve a 25 percent accuracy rate.

I understand why border collies are smart, but why are bassets so stupid?

Henry and Greg’s main argument depends on two basic facts: First, the speed of evolution–does evolution work fast enough to have caused any significant changes in human populations since we left Africa?

How fast evolution works depends on the pressure, of course. If everyone over 5 feet tall died tomorrow, the next generation of humans would be much shorter than the current one–and so would their children.

The end of the Ice Age also brought about a global rise in sea level. … As the waters rose, some mountains became islands.. These islands were too small to sustain populations of large predators, and in their absence the payoff for being huge disappeared. … Over a mere 5,000 years, elephants shrank dramatically, from an original height of 12 feet to as little as 3 feet.  It is worth noting that elephant generations are roughly twenty years long, similar to those of humans.

We have, in fact, many cases of evolution happening over a relatively short period, from dogs to corn to human skin tone.

No one is arguing about the evolution of something major, like a new limb or an extra spleen–just the sorts of small changes to the genome that can have big effects, like the minor genetic differences that spell the difference between a wolf and a poodle.

Second, human populations need to be sufficiently distinct–that is, isolated–for traits to be meaningfully different in different places. Of course, we can see that people look different in different places. This alone is enough to prove the point–people in Japan have been sufficiently isolated from people in Iceland that genetic changes affecting appearance haven’t spread from one population to the other.

What about the claim that “There’s more variation within races than between them”?

This is an interesting, non-intuitive claim. It is true–but it is also true for humans and chimps, dogs and wolves. That is, there is more variation within humans than between humans and chimps–a clue that this factoid may not be very meaningful.

Let’s let the authors explain:

Approximately 85 percent of human genetic variation is within-group rather than between groups, while 15 percent is between groups. … genetic variation is distributed in a similar way in dogs: 70 percent of genetic variation is within-breed, while 30 percent is between-breed. …

Information about the distribution of genetic variation tells you essentially nothing about the size or significance of trait differences. The actual differences we observe in height, weight, strength, speed, skin color, and so on are real: it is not possible to argue them away. …

It turns out that the correlations between these genetic differences matter. … consider malaria resistance in northern Europeans and central Africans. Someone from Nigeria may ave the sickle-cell mutation (a known defense against falciparum malaria,) while hardly anyone from northern Europe does, but even the majority of Nigerians who don’t carry the sickle cell are far more resistant to malaria than any Swede. They have malaria-defense versions of many genes. That is the typical pattern you get from natural selection–correlated changes in a population, change in the same general direction, all a response to the same selection pressure.

In other words: suppose a population splits and goes in two different directions. Population A encounters no malaria, and so develops no malria-resistant genes. Population B encounters malaria and quickly develops a hundred different mutations that all resist malaria. If some members of Population B have the at least some of the null variations found in Population A, then there’s very little variation between Pop A and B–all of Pop A’s variants are in fact found in Pop B. Meanwhile, there’s a great deal of variation within Pop B, which has developed 100 different ways to resist malaria. Yet the genetic differences between those populations is very important, especially if you’re in an area with malaria.

What if the differences between groups is just genetic drift?

Most or all of the alleles that are responsible for obvious differences in appearance between populations–such as the gene variants causing light skin color or blue eyes–have undergone strong selection. In these cases, a “big effect” on fitness means anything from a 2 or 3 percent increase on up. Judging from the rate at which new alleles have increased in frequency, this must be the case for genes that determine skin color (SLC24A5), eye color (HERC2), lactose tolerance (LCT), and dry earwax (ABCC11), of all things.

maps-europelighteyesIn fact, modern phenotypes are surprisingly young–blond hair, white skin, and blue eyes all evolved around a mere 10,000 years ago–maybe less. For these traits to have spread as far as they have, so quickly, they either confer some important evolutionary benefit or happen to occur in people who have some other evolutionarily useful trait, like lactose tolerance:

Lactose-tolerant Europeans carry a particular mutation that is only a few thousand years old, and so those Europeans also carry much of the original haplotype. In fact, the shared haplotype around that mutation is over 1 million bases long.

Recent studies have found hundreds of cases of long haplotypeles indicating recent selection: some have reached 100 percent frequency, more have intermediate frequencies, and most are regional. Many are very recent: The rate of origination peaks at around 5,500 years ago in the European and Chinese samples, and at about 8,500 years ago in the African sample.

(Note that the map of blue eyes and the map of lactose tolerance do not exactly correlate–the Baltic is a blue eyes hotspot, but not particularly a lactose hotspot–perhaps because hunter-gatherers hung on longer here by exploiting rich fishing spots.)

Could these explosions at a particular date be the genetic signatures of large conquering events? 5,5000 years ago is about right for the Indo-European expansion (perhaps some similar expansion happened in the East at the same time.) 8,000 years ago seems too early to have contributed to the Bantu Expansion–did someone else conquer west Africa around 8,500 years ago?

Let’s finish up:

Since we have sequenced the chimpanzee genome, we know the size of the genetic difference between chimps and humans. Since we also have decent estimates of the length of time since the two species split, we know the long-term rate of genetic change. The rate of change over the past few thousand years is far greater than this long-term rate over the past few million years, on the order of 100 times greater. …

The ultimate cause of this accelerated evolution was the set of genetic changes that led to an increased ability to innovate. …

Every major innovation led to new selective pressures, which led to more evolutionary change, and the most spectacular of those innovations was the development of agriculture.

Innovation itself has increased dramatically. The Stone Age lasted roughly 3.4 million years (you’ll probably note that this is longer than Homo sapiens has been around.) The most primitive stone tradition, the Oldowan, lasted for nearly 3 million of those 3.4; the next period, the Acheulean, lasted for about 1.5 million years. (There is some overlap in tool traditions.) By contrast, the age of metals–bronze, copper, iron, etc–has been going on for a measly 5,500 years, modern industrial society is only a couple of centuries old–at most.

What triggered this shift from 3 million years of shitty stone tools with nary an innovation in sight to a society that split the atom and put a man on the moon? And once culture was in place, what traits did it select–and what traits are we selecting for right now?

Is the singularity yet to come, or did we hit it 10,000 years ago–or before?

 

By the way, if you haven’t started the book yet, I encourage you to go ahead–you’ve plenty of time before next week to catch up.

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Book Club Pick: The 10,000 Year Explosion

5172bf1dp2bnl-_sx323_bo1204203200_Our next Book Club pick is Cochran and Harpending’s The 10,000 Year Explosion: How Civilization Accelerated Human Evolution. From the book’s description on Amazon:

Scientists have long believed that the “great leap forward” that occurred some 40,000 to 50,000 years ago in Europe marked end of significant biological evolution in humans. In this stunningly original account of our evolutionary history, top scholars Gregory Cochran and Henry Harpending reject this conventional wisdom and reveal that the human species has undergone a storm of genetic change much more recently. Human evolution in fact accelerated after civilization arose, they contend, and these ongoing changes have played a pivotal role in human history. They argue that biology explains the expansion of the Indo-Europeans, the European conquest of the Americas, and European Jews’ rise to intellectual prominence. …

I just received the book, so I haven’t read it yet, but I’ve been a big fan of Greg and Henry’s blog (now Greg’s blog, since Henry passed away,) for a long time. I expect to finish reading and get the relevant discussion posts up, therefore, in about two months–I’ll update the time frame as we get closer.

Please let me know if you prefer short form discussion (like our discussion of Kurzweil’s How to Build a Mind,) or long form discussion (like Auerswald’s The Code Economy,) or something in between.

Book Club: How to Create a Mind, pt 2/2

Ray Kurzweil, writer, inventor, thinker

Welcome back to EvX’s Book Club. Today  are finishing Ray Kurzweil’s How to Create a Mind: The Secret of Human thought Revealed.

Spiders are interesting, but Kurzweil’s focus is computers, like Watson, which trounced the competition on Jeopardy!

I’ll let Wikipedia summarize Watson:

Watson was created as a question answering (QA) computing system that IBM built to apply advanced natural language processing, information retrieval, knowledge representation, automated reasoning, and machine learning technologies to the field of open domain question answering.[2]

The sources of information for Watson include encyclopedias, dictionaries, thesauri, newswire articles, and literary works. Watson also used databases, taxonomies, and ontologies. …

Watson parses questions into different keywords and sentence fragments in order to find statistically related phrases.[22] Watson’s main innovation was not in the creation of a new algorithm for this operation but rather its ability to quickly execute hundreds of proven language analysis algorithms simultaneously.[22][24] The more algorithms that find the same answer independently the more likely Watson is to be correct.[22] Once Watson has a small number of potential solutions, it is able to check against its database to ascertain whether the solution makes sense or not.[22]

Kurzweil opines:

That is at least one reason why Watson represents such a significant milestone: Jeopardy! is precisely such a challenging language task. … What is perhaps not evident to many observers is that Watson not only had to master the language in the unexpected and convoluted queries, but for the most part its knowledge was not hand-coded. It obtained that knowledge by actually reading 200 million pages of natural-language documents, including all of Wikipedia… If Watson can understand and respond to questions based on 200 million pages–in three seconds!–here is nothing to stop similar systems from reading the other billions of documents on the Web. Indeed, that effort is now under way.

A point about the history of computing that may be petty of me to emphasize:

Babbage’s conception is quite miraculous when you consider the era in which he lived and worked. However, by the mid-twentieth century, his ideas had been lost in the mists of time (although they were subsequently rediscovered.) It was von Neumann who conceptualized and articulated the key principles of the computer as we know it today, and the world recognizes this by continuing to refer to the von Neumann machine as the principal model of computation. Keep in mind, though, that the von Neumann machine continually communicates data between its various units and within those units, so it could not be built without Shannon’s theorems and the methods he devised for transmitting and storing reliable digital information. …

You know what? No, it’s not petty.

Amazon lists 57 books about Ada Lovelace aimed at children, 14 about Alan Turing, and ZERO about John von Neumann.

(Some of these results are always irrelevant, but they are roughly correct.)

“EvX,” you may be saying, “Why are you counting children’s books?”

Because children are our future, and the books that get published for children show what society deems important for children to learn–and will have an effect on what adults eventually know.

I don’t want to demean Ada Lovelace’s role in the development of software, but surely von Neumann’s contributions to the field are worth a single book!

*Slides soapbox back under the table*

Anyway, back to Kurzweil, now discussing quantum mechanics:

There are two ways to view the questions we have been considering–converse Western an Easter perspective on the nature of consciousness and of reality. In the Western perspective, we start with a physical world that evolves patterns of information. After a few billion years of evolution, the entities in that world have evolved sufficiently to become conscious beings In the Eastern view, consciousness is the fundamental reality, the physical word only come into existence through the thoughts of conscious beings. …

The East-West divide on the issue of consciousness has also found expression in opposing schools of thought in the field of subatomic physics. In quantum mechanics, particles exist in what are called probability fields. Any measurement carried out on them by a measuring device causes what is called a collapse of the wave function, meaning that the particle suddenly assumes a particular location. A popular view is that such a measurement constitutes observation by a conscious observer… Thus the particle assume a particular location … only when it is observed. Basically particles figure that if no one is bothering to look at them, they don’t need to decide where they are. I call this the Buddhist school of quantum mechanics …

Niels Bohr

Or as Niels Bohr put it, “A physicist is just an atom’s way of looking at itself.” He also claimed that we could describe electrons exercised free will in choosing their positions, a statement I do not think he meant literally; “We must be clear that when it comes to atoms, language can be used only as in poetry,” as he put it.

Kurzweil explains the Western interpretation of quantum mechanics:

There is another interpretation of quantum mechanics… In this analysis, the field representing a particle is not a probability field, but rather just a function that has different values in different locations. The field, therefore, is fundamentally what the particle is. … The so-called collapse of the wave function, this view holds, is not a collapse at all. … It is just that a measurement device is also made up of particles with fields, and the interaction of the particle field being measured and the particle fields of the measuring device result in a reading of the particle being in a particular location. The field, however, is still present. This is the Western interpretation of quantum mechanics, although it is interesting to note that the more popular view among physicists worldwide is what I have called the Eastern interpretation.

Soviet atomic bomb, 1951

For example, Bohr has the yin-yang symbol on his coat of arms, along with the motto contraria sunt complementa, or contraries are complementary. Oppenheimer was such a fan of the Bhagavad Gita that he read it in Sanskrit and quoted it upon successful completion of the Trinity Test, “If the radiance of a thousand suns were to burst at once into the sky, that would be like the splendor of the mighty one,” and “Now I am become death, the destroyer of worlds.” He credited the Gita as one of the most important books in his life.

Why the appeal of Eastern philosophy? Is it something about physicists and mathematicians? Leibnitz, after all, was fond of the I Ching. As Wikipedia says:

Leibniz was perhaps the first major European intellectual to take a close interest in Chinese civilization, which he knew by corresponding with, and reading other works by, European Christian missionaries posted in China. Having read Confucius Sinarum Philosophus on the first year of its publication,[153] he concluded that Europeans could learn much from the Confucian ethical tradition. He mulled over the possibility that the Chinese characters were an unwitting form of his universal characteristic. He noted with fascination how the I Ching hexagrams correspond to the binary numbers from 000000 to 111111, and concluded that this mapping was evidence of major Chinese accomplishments in the sort of philosophical mathematics he admired.[154] Leibniz communicated his ideas of the binary system representing Christianity to the Emperor of China hoping it would convert him.[84] Leibniz may be the only major Western philosopher who attempted to accommodate Confucian ideas to prevailing European beliefs.[155]

Leibniz’s attraction to Chinese philosophy originates from his perception that Chinese philosophy was similar to his own.[153] The historian E.R. Hughes suggests that Leibniz’s ideas of “simple substance” and “pre-established harmony” were directly influenced by Confucianism, pointing to the fact that they were conceived during the period that he was reading Confucius Sinarum Philosophus.[153]

Perhaps it is just that physicists and mathematicians are naturally curious people, and Eastern philosophy is novel to a Westerner, or perhaps by adopting Eastern ideas, they were able to purge their minds of earlier theories of how the universe works, creating a blank space in which to evaluate new data without being biased by old conceptions–or perhaps it is just something about the way their minds work.

As for quantum, I favor the de Broglie-Bohm interpretation of quantum mechanics, but obviously I am not a physicist and my opinion doesn’t count for much. What do you think?

But back to the book. If you are fond of philosophical ruminations on the nature of consciousness, like “What if someone who could only see in black and white read extensively about color “red,” could they ever achieve the qualia of actually seeing the color red?” or “What if a man were locked in a room with a perfect Chinese rulebook that told him which Chinese characters to write in response to any set of characters written on notes passed under the door? The responses are be in perfect Chinese, but the man himself understands not a word of Chinese,” then you’ll enjoy the discussion. If you already covered all of this back in Philosophy 101, you might find it a bit redundant.

Kurzweil notes that conditions have improved massively over the past century for almost everyone on earth, but people are increasingly anxious:

A primary reason people believe life is getting worse is because our information about the problems of the world has steadily improved. If there is a battle today somewhere on the planet, we experience it almost as if we were there. During World War II, tens of thousand of people might perish in a battle, and if the public could see it at all it was in a grainy newsreel in a movie theater weeks later. During World War I a small elite could read about the progress of the conflict in the newspaper (without pictures.) During the nineteenth century there was almost no access to news in a timely fashion for anyone.

As for the future of man, machines, and code, Kurzweil is even more optimistic than Auerswald:

The last invention that biological evolution needed to make–the neocortex–is inevitably leading to the last invention that humanity needs to make–truly intelligent machines–and the design of one is inspiring the other. … by the end of this century we will be able to create computation at the limits of what is possible, based on the laws of physics… We call matter and energy organized in this way “computronium” which is vastly more powerful pound per pound than the human brain. It will not jut be raw computation but will be infused with intelligent algorithms constituting all of human-machine knowledge. Over time we will convert much of the mass and energy in our tiny corner of the galaxy that is suitable for this purpose to computronium. … we will need to speed out to the rest of the galaxy and universe. …

How long will it take for us to spread our intelligence in its nonbiological form throughout the universe? … waking up the universe, and then intelligently deciding its fate by infusing it with our human intelligence in its nonbiological form, is our destiny.

Whew! That is quite the ending–and with that, so will we. I hope you enjoyed the book. What did you think of it? Will Humanity 2.0 be good? Bad? Totally different? Or does the Fermi Paradox imply that Kurzweil is wrong? Did you like this shorter Book Club format? And do you have any ideas for our next Book Club pick?

Book Club: How to Create a Mind by Ray Kurzweil pt 1/2

Welcome to our discussion of Ray Kurzweil’s How to Create a Mind: The Secret of Human thought Revealed. This book was requested by one my fine readers; I hope you have enjoyed it.

If you aren’t familiar with Ray Kurzweil (you must be new to the internet), he is a computer scientist, inventor, and futurist whose work focuses primarily on artificial intelligence and phrases like “technological singularity.”

Wikipedia really likes him.

The book is part neuroscience, part explanations of how various AI programs work. Kurzweil uses models of how the brain works to enhance his pattern-recognition programs, and evidence from what works in AI programs to build support for theories on how the brain works.

The book delves into questions like “What is consciousness?” and “Could we recognize a sentient machine if we met one?” along with a brief history of computing and AI research.

My core thesis, which I call the Law of Accelerating Returns, (LOAR), is that fundamental measures of of information technology follow predictable and exponential trajectories…

I found this an interesting sequel to Auerswald’s The Code Economy and counterpart to Gazzaniga’s Who’s In Charge? Free Will and the Science of the Brain, which I listened to in audiobook form and therefore cannot quote very easily. Nevertheless, it’s a good book and I recommend it if you want more on brains.

The quintessential example of the law of accelerating returns is the perfectly smooth, doubly exponential growth of the price/performance of computation, which has held steady for 110 years through two world was, the Great Depression, the Cold War, the collapse of the Soviet Union, the reemergence of China, the recent financial crisis, … Some people refer to this phenomenon as “Moore’s law,” but… [this] is just one paradigm among many.

From Ray Kurzweil,

Auerswald claims that the advance of “code” (that is, technologies like writing that allow us to encode information) has, for the past 40,000 years or so, supplemented and enhanced human abilities, making our lives better. Auerswald is not afraid of increasing mechanization and robotification of the economy putting people out of jobs because he believes that computers and humans are good at fundamentally different things. Computers, in fact, were invented to do things we are bad at, like decode encryption, not stuff we’re good at, like eating.

The advent of computers, in his view, lets us concentrate on the things we’re good at, while off-loading the stuff we’re bad at to the machines.

Kurzweil’s view is different. While he agrees that computers were originally invented to do things we’re bad at, he also thinks that the computers of the future will be very different from those of the past, because they will be designed to think like humans.

A computer that can think like a human can compete with a human–and since it isn’t limited in its processing power by pelvic widths, it may well out-compete us.

But Kurzweil does not seem worried:

Ultimately we will create an artificial neocortex that has the full range and flexibility of its human counterpart. …

When we augment our own neocortex with a synthetic version, we won’t have to worry about how much additional neocortex can physically fit into our bodies and brains, as most of it will be in the cloud, like most of the computing we use today. I estimated earlier that we have on the order of 300 million pattern recognizers in our biological neocortex. That’s as much as could b squeezed into our skulls even with the evolutionary innovation of a large forehead and with the neocortex taking about 80 percent of the available space. As soon as we start thinking in the cloud, there will be no natural limits–we will be able to use billions or trillions of pattern recognizers, basically whatever we need. and whatever the law of accelerating returns can provide at each point in time. …

Last but not least, we will be able to back up the digital portion of our intelligence. …

That is kind of what I already do with this blog. The downside is that sometimes you people see my incomplete or incorrect thoughts.

On the squishy side, Kurzweil writes of the biological brain:

The story of human intelligence starts with a universe that is capable of encoding information. This was the enabling factor that allowed evolution to take place. …

The story of evolution unfolds with increasing levels of abstraction. Atoms–especially carbon atoms, which can create rich information structures by linking in four different directions–formed increasingly complex molecules. …

A billion yeas later, a complex molecule called DNA evolved, which could precisely encode lengthy strings of information and generate organisms described by these “programs”. …

The mammalian brain has a distinct aptitude not found in any other class of animal. We are capable of hierarchical thinking, of understanding a structure composed of diverse elements arranged in a pattern, representing that arrangement with a symbol, and then using that symbol as an element in a yet more elaborate configuration. …

I really want to know if squids or octopuses can engage in symbolic thought.

Through an unending recursive process we are capable of building ideas that are ever more complex. … Only Homo sapiens have a knowledge base that itself evolves, grow exponentially, and is passe down from one generation to another.

Kurzweil proposes an experiment to demonstrate something of how our brains encode memories: say the alphabet backwards.

If you’re among the few people who’ve memorized it backwards, try singing “Twinkle Twinkle Little Star” backwards.

It’s much more difficult than doing it forwards.

This suggests that our memories are sequential and in order. They can be accessed in the order they are remembered. We are unable to reverse the sequence of a memory.

Funny how that works.

On the neocortex itself:

A critically important observation about the neocortex is the extraordinary uniformity of its fundamental structure. … In 1957 Mountcastle discovered the columnar organization of the neocortex. … [In 1978] he described the remarkably unvarying organization of the neocortex, hypothesizing that it was composed of a single mechanism that was repeated over and over again, and proposing the cortical column as the basic unit. The difference in the height of certain layers in different region noted above are simply differences in the amount of interconnectivity that the regions are responsible for dealing with. …

extensive experimentation has revealed that there are in fact repeating units within each column. It is my contention that the basic unit is a pattern organizer and that this constitute the fundamental component of the neocortex.

As I read, Kurzweil’s hierarchical models reminded me of Chomsky’s theories of language–both Ray and Noam are both associated with MIT and have probably conversed many times. Kurzweil does get around to discussing Chomsky’s theories and their relationship to his work:

Language is itself highly hierarchical and evolved to take advantage of the hierarchical nature of the neocortex, which in turn reflects the hierarchical nature of reality. The innate ability of human to lean the hierarchical structures in language that Noam Chomsky wrote about reflects the structure of of the neocortex. In a 2002 paper he co-authored, Chomsky cites the attribute of “recursion” as accounting for the unique language faculty of the human species. Recursion, according to Chomsky, is the ability to put together small parts into a larger chunk, and then use that chunk as a part in yet another structure, and to continue this process iteratively In this way we are able to build the elaborate structure of sentences and paragraphs from a limited set of words. Although Chomsky was not explicitly referring here to brain structure, the capability he is describing is exactly what the neocortex does. …

This sounds good to me, but I am under the impression that Chomsky’s linguistic theories are now considered outdated. Perhaps that is only his theory of universal grammar, though. Any linguistics experts care to weigh in?

According to Wikipedia:

Within the field of linguistics, McGilvray credits Chomsky with inaugurating the “cognitive revolution“.[175] McGilvray also credits him with establishing the field as a formal, natural science,[176] moving it away from the procedural form of structural linguistics that was dominant during the mid-20th century.[177] As such, some have called him “the father of modern linguistics”.[178][179][180][181]

The basis to Chomsky’s linguistic theory is rooted in biolinguistics, holding that the principles underlying the structure of language are biologically determined in the human mind and hence genetically transmitted.[182] He therefore argues that all humans share the same underlying linguistic structure, irrespective of sociocultural differences.[183] In adopting this position, Chomsky rejects the radical behaviorist psychology of B. F. Skinner which views the mind as a tabula rasa (“blank slate”) and thus treats language as learned behavior.[184] Accordingly, he argues that language is a unique evolutionary development of the human species and is unlike modes of communication used by any other animal species.[185][186] Chomsky’s nativist, internalist view of language is consistent with the philosophical school of “rationalism“, and is contrasted with the anti-nativist, externalist view of language, which is consistent with the philosophical school of “empiricism“.[187][174]

Anyway, back to Kuzweil, who has an interesting bit about love:

Science has recently gotten into the act as well, and we are now able to identify the biochemical changes that occur when someone falls in love. Dopamine is released, producing feelings of happiness and delight. Norepinephrin levels soar, which lead to a racing heart and overall feelings of exhilaration. These chemicals, along with phenylethylamine, produce elevation, high energy levels, focused attention, loss of appetite, and a general craving for the object of one’s desire. … serotonin level go down, similar to what happens in obsessive-compulsive disorder….

If these biochemical phenomena sound similar to those of the flight-or-fight syndrome, they are, except that we are running toward something or someone; indeed, a cynic might say toward rather than away form danger. The changes are also fully consistent with those of the early phase of addictive behavior. …  Studies of ecstatic religious experiences also show the same physical phenomena, it can be said that the person having such an experiences is falling in love with God or whatever spiritual connection on which they are focused. …

Religious readers care to weigh in?

Consider two related species of voles: the prairie vole and the montane vole. They are pretty much identical, except that the prairie vole has receptors for oxytocin and vasopressin, whereas the montane vole does not. The prairie vole is noted for lifetime monogamous relationships, while the montane vole resorts almost exclusively to one-night stands.

Learning by species:

A mother rat will build a nest for her young even if she has never seen another rat in her lifetime. Similarly, a spider will spin a web, a caterpillar will create her own cocoon, and a beaver will build a damn, even if no contemporary ever showed them how to accomplish these complex tasks. That is not to say that these are not learned behavior. It is just that he animals did not learn them in a single lifetime… The evolution of animal behavior does constitute a learning process, but it is learning by the species, not by the individual and the fruits of this leaning process are encoded in DNA.

I think that’s enough for today; what did you think? Did you enjoy the book? Is Kurzweil on the right track with his pattern recognizers? Are non-biological neocortexes on the horizon? Will we soon convert the solar system to computronium?

Let’s continue this discussion next Monday–so if you haven’t read the book yet, you still have a whole week to finish.

 

Book Club Announcement: How to Create a Mind

Next Book Club pick: How to Create a Mind: The Secret of Human Thought Revealed, by Ray Kurzweil. (This time we will be taking a different approach, and the discussion will be much shorter.)

From the Amazon blurb:

Ray Kurzweil is arguably today’s most influential—and often controversial—futurist. In How to Create a Mind, Kurzweil presents a provocative exploration of the most important project in human-machine civilization—reverse engineering the brain to understand precisely how it works and using that knowledge to create even more intelligent machines.

Discussion starts September 24th.

Book Club: The Code Economy, Chapter 11: Education and Death

Welcome back to EvX’s book club. Today we’re reading Chapter 11 of The Code Economy, Education.

…since the 1970s, the economically fortunate among us have been those who made the “go to college” choice. This group has seen its income row rapidly and its share of the aggregate wealth increase sharply. Those without a college education have watched their income stagnate and their share of the aggregate wealth decline. …

Middle-age white men without a college degree have been beset by sharply rising death rates–a phenomenon that contrasts starkly with middle-age Latino and African American men, and with trends in nearly every other country in the world.

It turns out that I have a lot of graphs on this subject. There’s a strong correlation between “white death” and “Trump support.”

White vs. non-white Americans

American whites vs. other first world nations

source

But “white men” doesn’t tell the complete story, as death rates for women have been increasing at about the same rate. The Great White Death seems to be as much a female phenomenon as a male one–men just started out with higher death rates in the first place.

Many of these are deaths of despair–suicide, directly or through simply giving up on living. Many involve drugs or alcohol. And many are due to diseases, like cancer and diabetes, that used to hit later in life.

We might at first think the change is just an artifact of more people going to college–perhaps there was always a sub-set of people who died young, but in the days before most people went to college, nothing distinguished them particularly from their peers. Today, with more people going to college, perhaps the destined-to-die are disproportionately concentrated among folks who didn’t make it to college. However, if this were true, we’d expect death rates to hold steady for whites overall–and they have not.

Whatever is affecting lower-class whites, it’s real.

Auerswald then discusses the “Permanent income hypothesis”, developed by Milton Friedman: Children and young adults devote their time to education, (even going into debt,) which allows us to get a better job in mid-life. When we get a job, we stop going to school and start saving for retirement. Then we retire.

The permanent income hypothesis is built into the very structure of our society, from Public Schools that serve students between the ages of 5 and 18, to Pell Grants for college students, to Social Security benefits that kick in at 65. The assumption, more or less, is that a one-time investment in education early in life will pay off for the rest of one’s life–a payout of such returns to scale that it is even sensible for students and parents to take out tremendous debt to pay for that education.

But this is dependent on that education actually paying off–and that is dependent on the skills people learn during their educations being in demand and sufficient for their jobs for the next 40 years.

The system falls apart if technology advances and thus job requirements change faster than once every 40 years. We are now looking at a world where people’s investments in education can be obsolete by the time they graduate, much less by the time they retire.

Right now, people are trying to make up for the decreasing returns to education (a highschool degree does not get you the same job today as it did in 1950) by investing more money and time into the single-use system–encouraging toddlers to go to school on the one end and poor students to take out more debt for college on the other.

This is probably a mistake, given the time-dependent nature of the problem.

The obvious solution is to change how we think of education and work. Instead of a single, one-time investment, education will have to continue after people begin working, probably in bursts. Companies will continually need to re-train workers in new technology and innovations. Education cannot be just a single investment, but a life-long process.

But that is hard to do if people are already in debt from all of the college they just paid for.

Auerswald then discusses some fascinating work by Bessen on how the industrial revolution affected incomes and production among textile workers:

… while a handloom weaver in 1800 required nearly forty minutes to weave a yard of coarse cloth using a single loom, a weaver in 1902 could do the same work operating eighteen Nothrop looms in less than a minute, on average. This striking point relates to the relative importance of the accumulation of capital to the advance of code: “Of the roughly thirty-nine-minute reduction in labor time per yard, capital accumulation due to the changing cost of capital relative to wages accounted for just 2 percent of the reduction; invention accounted for 73 percent of the reduction; and 25 percent of the time saving came from greater skill and effort of the weavers.” … “the role of capital accumulation was minimal, counter to the conventional wisdom.”

Then Auerswald proclaims:

What was the role of formal education in this process? Essentially nonexistent.

Boom.

New technologies are simply too new for anyone to learn about them in school. Flexible thinkers who learn fast (generalists) thus benefit from new technologies and are crucial for their early development. Once a technology matures, however, it becomes codified into platforms and standards that can be taught, at which point demand for generalists declines and demand for workers with educational training in the specific field rises.

For Bessen, formal education and basic research are not the keys to the development of economies that they are often represented a being. What drives the development of economies is learning by doing and the advance of code–processes that are driven at least as much by non-expert tinkering as by formal research and instruction.

Make sure to read the endnotes to this chapter; several of them are very interesting. For example, #3 begins:

“Typically, new technologies demand that a large number of variables be properly controlled. Henry Bessemer’s simple principle of refining molten iron with a blast of oxygen work properly only at the right temperatures, in the right size vessel, with the right sort of vessel refractory lining, the right volume and temperature of air, and the right ores…” Furthermore, the products of these factories were really one that, in the United States, previously had been created at home, not by craftsmen…

#8 states:

“Early-stage technologies–those with relatively little standardized knowledge–tend to be used at a smaller scale; activity is localized; personal training and direct knowledge sharing are important, and labor markets do not compensate workers for their new skills. Mature technologies–with greater standardized knowledge–operate at large scale and globally, market permitting; formalized training and knowledge are more common; and robust labor markets encourage workers to develop their own skills.” … The intensity of of interactions that occur in cities is also important in this phase: “During the early stages, when formalized instruction is limited, person-to-person exchange is especially important for spreading knowledge.”

This reminds me of a post on Bruce Charlton’s blog about “Head Girl Syndrome“:

The ideal Head Girl is an all-rounder: performs extremely well in all school subjects and has a very high Grade Point Average. She is excellent at sports, Captaining all the major teams. She is also pretty, popular, sociable and well-behaved.

The Head Girl will probably be a big success in life…

But the Head Girl is not, cannot be, a creative genius.

*

Modern society is run by Head Girls, of both sexes, hence there is no place for the creative genius.

Modern Colleges aim at recruiting Head Girls, so do universities, so does science, so do the arts, so does the mass media, so does the legal profession, so does medicine, so does the military…

And in doing so, they filter-out and exclude creative genius.

Creative geniuses invent new technologies; head girls oversee the implementation and running of code. Systems that run on code can run very smoothly and do many things well, but they are bad at handling creative geniuses, as many a genius will inform you of their public school experience.

How different stages in the adoption of new technology and its codification into platforms translates into wages over time is a subject I’d like to see more of.

Auerswald then turns to the perennial problem of what happens when not only do the jobs change, they entirely disappear due to increasing robotification:

Indeed, many of the frontier business models shaping the economy today are based on enabling a sharp reduction in the number of people required to perform existing tasks.

One possibility Auerswald envisions is a kind of return to the personalized markets of yesteryear, when before massive industrial giants like Walmart sprang up. Via internet-based platforms like Uber or AirBNB, individuals can connect directly with people who’d like to buy their goods or services.

Since services make up more than 84% of the US economy and an increasingly comparable percentage in coutnries elsewhere, this is a big deal.

It’s easy to imagine this future in which we are all like some sort of digital Amish, continually networked via our phones to engage in small transactions like sewing a pair of trousers for a neighbor, mowing a lawn, selling a few dozen tacos, or driving people to the airport during a few spare hours on a Friday afternoon. It’s also easy to imagine how Walmart might still have massive economies of scale over individuals and the whole system might fail miserably.

However, if we take the entrepreneurial perspective, such enterprises are intriguing. Uber and Airbnb work by essentially “unlocking” latent assets–time when people’s cars or homes were sitting around unused. Anyone who can find other, similar latent assets and figure out how to unlock them could become similarly successful.

I’ve got an underutilized asset: rural poor. People in cities are easy to hire and easy to direct toward educational opportunities. Kids growing up in rural areas are often out of the communications loop (the internet doesn’t work terribly well in many rural areas) and have to drive a long way to job interviews.

In general, it’s tough to network large rural areas in the same ways that cities get networked.

On the matter of why peer-to-peer networks have emerged in certain industries, Auerswald makes a claim that I feel compelled to contradict:

The peer-to-peer business models in local transportation, hospitality, food service, and the rental of consumer goods were the first to emerge, not because they are the most important for the economy but because these are industries with relatively low regulatory complexity.

No no no!

Food trucks emerged because heavy regulations on restaurants (eg, fire code, disability access, landscaping,) have cut significantly into profits for restaurants housed in actual buildings.

Uber emerged because the cost of a cab medallion–that is, a license to drive a cab–hit 1.3 MILLION DOLLARS in NYC. It’s a lucrative industry that people were being kept out of.

In contrast, there has been little peer-to-peer business innovation in healthcare, energy, and education–three industries that comprise more than a quarter of the US GDP–where regulatory complexity is relatively high.

Again, no.

There is a ton of competition in healthcare; just look up naturopaths and chiropractors. Sure, most of them are quacks, but they’re definitely out there, competing with regular doctors for patients. (Midwives appear to be actually pretty effective at what they do and significantly cheaper than standard ob-gyns.)

The difficulty with peer-to-peer healthcare isn’t regulation but knowledge and equipment. Most Americans own a car and know how to drive, and therefore can join Uber. Most Americans do not know how to do heart surgery and do not have the proper equipment to do it with. With training I might be able to set a bone, but I don’t own an x-ray machine. And you definitely don’t want me manufacturing my own medications. I’m not even good at making soup.

Education has tons of peer-to-peer innovation. I homeschool my children. Sometimes grandma and grandpa teach the children. Many homeschoolers join consortia that offer group classes, often taught by a knowledgeable parent or hired tutor. Even people who aren’t homeschooling their kids often hire tutors, through organizations like Wyzant or afterschool test-prep centers like Kumon. One of my acquaintances makes her living primarily by skype-tutoring Koreans in English.

And that’s not even counting private schools.

Yes, if you want to set up a formal “school,” you will encounter a lot of regulation. But if you just want to teach stuff, there’s nothing stopping you except your ability to find students who’ll pay you to learn it.

Now, energy is interesting. Here Auerswsald might be correct. I have trouble imagining people setting up their own hydroelectric dams without getting into trouble with the EPA (not to mention everyone downstream.)

But what if I set up my own windmill in my backyard? Can I connect it to the electric grid and sell energy to my neighbors on a windy day? A quick search brings up WindExchange, which says, very directly:

Owners of wind turbines interconnected directly to the transmission or distribution grid, or that produce more power than the host consumes, can sell wind power as well as other generation attributes.

So, maybe you can’t set up your own nuclear reactor, and maybe the EPA has a thing about not disturbing fish, but it looks like you can sell wind and solar energy back to the grid.

I find this a rather exciting thought.

Ultimately, while Auerswald does return to and address the need to radically change how we think about education and the education-job-retirement lifepath, he doesn’t return to the increasing white death rate. Why are white death rates increasing faster than other death rates, and will transition to the “gig economy” further accelerate this trend? Or was the past simply anomalous for having low white death rates, or could these death rates be driven by something independent of the economy itself?

Now, it’s getting late, so that’s enough for tonight, but what are your thoughts? How do you think this new economy–and educational landscape–will play out?

Book Club: Code Economy, Ch. 10: In which I am Confused

Welcome back to EvX’s Book Club. Today we start the third (and final) part of Auerswald’s The Code Economy: The Human Advantage.

Chapter 10: Complementarity discuses bifurcation, a concept Auerswald mentions frequently throughout the book. He has a graph of the process of bifurcation, whereby the development of new code (ie, technology), leads to the creation of a new “platform” on the one hand, and new human work on the other. With each bifurcation, we move away from the corner of the graph marked “simplicity” and “autonomy,” and toward the corner marked “complexity” and “interdependence.” It looks remarkably like a graph I made about energy inputs vs outputs at different complexity levels, based on a memory of a graph I saw in a textbook some years ago.

There are some crucial differences between our two graphs, but I think they nonetheless related–and possibly trying to express the same thing.

Auerswald argues that as code becomes platform, it doesn’t steal jobs, but becomes the new base upon which people work. The Industrial Revolution eliminated the majority of farm laborers via automation, but simultaneously provided new jobs for them, in factories. Today, the internet is the “platform” where jobs are being created, not in building the internet, but via businesses like Uber that couldn’t exist without the internet.

Auerswald’s graph (not mine) is one of the few places in the book where he comes close to examining the problem of intelligence. It is difficult to see what unintelligent people are going to do in a world that is rapidly becoming more complicated.

On the other hand people who didn’t have access to all sorts of resources now do, due to internet-based platforms–people in the third world, for example, who never bought land-line telephones because their country couldn’t afford to build the infrastructure to support them, are snapping up mobile and smartphones at an extraordinary rate:

And overwhelming majorities in almost every nation surveyed report owning some form of mobile device, even if they are not considered “smartphones.”

And just like Auerswald’s learning curves from the last chapter, technological spread is speeding up. It took the landline telephone 64 years to go from 0% to 40% of the US market. Mobile phones took only 20 years to accomplish the same feat, and smartphones did it in about 10. (source.)

There are now more mobile phones in the developing world than the first world, and people aren’t just buying just buying these phones to chat. People who can’t afford to open bank accounts now use their smarphones as “mobile wallets”:

According to the GSMA, an industry group for the mobile communications business, there are now 79 mobile money systems globally, mostly in Africa and Asia. Two-thirds of them have been launched since 2009.

To date, the most successful example is M-Pesa, which Vodafone launched in Kenya in 2007. A little over three years later, the service has 13.5 million users, who are expected to send 20 percent of the country’s GDP through the system this year. “We proved at Vodafone that if you get the proposition right, the scale-up is massive,” says Nick Hughes, M-Pesa’s inventor.

But let’s get back to Auerswald. Chapter 10 contains a very interesting description of the development of the development of the Swiss Watch industry. Of course, today, most people don’t go out of their way to buy watches, since their smartphones have clocks built into them. Have smartphones put the Swiss out of business? Not quite, says Auerswald:

Switzerland… today produces fewer than 5 percent of the timepieces manufactured for export globally. In 2014, Switzerland exported 29 million watches, as compaed to China’ 669 million… But what of value? … Swiss watch exports were worth 24.3 billion in 2014, nearly five times as much as all Chinese watches combined.

Aside from the previously mentioned bifurcation of human and machine labor, Auerswald suggests that automation bifurcates products into cheap and expensive ones. He claims that movies, visual art services (ie, copying and digitization of art vs. fine art,) and music have also undergone bifurcation, not extinction, due to new technology.

In each instance, disruptive advances in code followed a consistent and predictable pattern: the creation of a new high-volume, low-price option creates a new market for the low-volume, high-price option. Every time this happens, the new value created through improved code forces a bifurcation of markets, and of work.

Detroit

He then discusses a watch-making startup located in Detroit, which I feel completely and totally misses the point of whatever economic lessons we can draw from Detroit.

Detroit is, at least currently, a lesson in how people fail to deal with increasing complexity, much less bifurcation.

Even that word–bifurcation–contains a problem: what happens to the middle? A huge mass of people at the bottom, making and consuming cheap products, and a small class at the top, making and consuming expensive products–well I will honor the demonstrated preferences of everyone involved for stuff, of whatever price, but what about the middle?

Is this how the middle class dies?

But if the poor become rich enough… does it matter?

Because work is fundamentally algorithmic, it is capable of almost limitless diversification though both combinatorial and incremental change. The algorithms of work become, fairly literally, the DNA of the economy. …

As Geoff Moore puts it, “Digital innovation is reengineering our manufacturing-based product-centric economy to improve quality, reduce cost, expand markets, … It is doing so, however, largely at the expense of traditional middle class jobs. This class of work is bifurcating into elite professions that are highly compensated but outside the skillset of the target population and commoditizing workloads for which the wages fall well below the target level.”

It is easy to take the long view and say, “Hey, the agricultural revolution didn’t result in massive unemployment among hunter-gatherers; the bronze and iron ages didn’t result in unemployed flint-knappers starving in the streets, so we’ll probably survive the singularity, too,” and equally easy to take the short view and say, “screw the singularity, I need a job that pays the bills now.”

Auerswald then discusses the possibilities for using big data and mobile/wearable computers to bring down healthcare costs. I am also in the middle of a Big Data reading binge, and my general impression of health care is that there is a ton of data out there (and more being collected every day,) but it is unwieldy and disorganized and doctors are too busy to use most of it and patients don’t have access to it. and if someone can amass, organize, and sort that data in useful ways, some very useful discoveries could be made.

Then we get to the graph that I didn’t understand,”Trends in Nonroutine Task Input, 1960 to 1998,” which is a bad sign for my future employment options in this new economy.

My main question is what is meant by “nonroutine manual” tasks, and since these were the occupations with the biggest effect shown on the graph, why aren’t they mentioned in the abstract?:

We contend that computer capital (1) substitutes for a limited and well-defined set of human activities, those involving routine (repetitive) cognitive and manual tasks; and (2) complements activities involving non-routine problem solving and interactive tasks. …Computerization is associated with declining relative industry demand for routine manual and cognitive tasks and increased relative demand for non-routine cognitive tasks.

Yes, but what about the non-routine manual? What is that, and why did it disappear first? And does this graph account for increased offshoring of manufacturing jobs to China?

If you ask me, it looks like there are three different events recorded in the graph, not just one. First, from 1960 onward, “non-routine manual” jobs plummet. Second, from 1960 through 1970, “routine cognitive” and “routine manual” jobs increase faster than “non-routine analytic” and “non-routine interactive.” Third, from 1980 onward, the routine jobs head downward while the analytic and interactive jobs become more common.

*Downloads the PDF and begins to read* Here’s the explanation of non-routine manual:

Both optical recognition of objects in a visual field and bipedal locomotion across an uneven surface appear to require enormously sophisticated algorithms, the one in optics and the other in mechanics, which are currently poorly understood by cognitive science (Pinker, 1997). These same problems explain the earlier mentioned inability of computers to perform the tasks of long haul truckers.

In this paper we refer to such tasks requiring visual and manual skills as ‘non-routine manual activities.’

This does not resolve the question.

Discussion from the paper:

Trends in routine task input, both cognitive and manual, also follow a striking pattern. During the  1960s, both forms of input increased due to a combination of between- and within-industry shifts. In the 1970s, however, within-industry input of both tasks declined, with the rate of decline accelerating.

As distinct from the other four task measures, we observe steady within- and between-industry shifts against non-routine manual tasks for the entire four decades of our sample. Since our conceptual framework indicates that non-routine manual tasks are largely orthogonal to computerization, we view
this pattern as neither supportive nor at odds with our model.

Now, it’s 4 am and the world is swimming a bit, but I think “we aren’t predicting any particular effect on non-routine manual tasks” should have been stated up front in the thesis portion. Sticking it in here feels like ad-hoc explaining away of a discrepancy. “Well, all of the other non-routine tasks went up, but this one didn’t, so, well, it doesn’t count because they’re hard to computerize.”

Anyway, the paper is 62 pages long, including the tables and charts, and I’m not reading it all or second-guessing their math at this hour, but I feel like there is something circular in all of this–“We already know that jobs involving routine labor like manufacturing are down, so we made a models saying they decreased as a percent of jobs because of computers and automation, looked through jobs data, and low and behold, found that they had decreased. Confusingly, though, we also found that non-routine manual jobs decreased during this time period, even though they don’t lend themselves to automation and computerization.”

I also searched in the document and could find no instance of the words “offshor-” “China” “export” or “outsource.”

Also, the graph Auerswald uses and the corresponding graph in the paper have some significant differences, especially the “routine cognitive” line. Maybe the authors updated their graph with more data, or Auerswald was trying to make the graph clearer. I don’t know.

Whatever is up with this paper, I think we may provisionally accept its data–fewer factory workers, more lawyers–without necessarily accepting its model.

The day after I wrote this, I happened to be reading Davidowitz’s Everybody Lies: Big Data, New Data, and What the Internet Can Tell us about who we Really Are, which has a discussion of the best places to raise children.

Talking about Chetty’s data, Davidowitz writes:

The question asked: what is the chance that a person with parents in the bottom 20 percent of the income distribution reaches the top 20 percent of the income distribution? …

So what is it about part of the United States where there is high income mobility? What makes some places better at equaling the playing field, of allowing a poor kid to have a pretty good life? Areas that spend more on education provide a better chance to poor kids. Places with more religious people and lower crime do better. Places with more black people do worse. Interestingly, this has an effect on not just the black kids but on the white kids living there as well.

Here is Chetty’s map of upward mobility (or the lack thereof) by county. Given how closely it matches a map of “African Americans” + “Native Americans” I have my reservations about the value of Chetty’s research on the bottom end (is anyone really shocked to discover that black kids enjoy little upward mobility?) but it still has some comparative value.

Davidowitz then discusses Chetty’s analysis of where people live the longest:

Interestingly, for the wealthiest Americans, life expectancy is hardly affected by where you live. …

For the poorest Americans, life expectancy varies tremendously…. living in the right place can add five years to a poor person’s life expectancy. …

religion, environment, and health insurance–do not correlate with longer life spans for the poor. The variable that does matter, according to Chetty and the others who worked on this study? How many rich people live in a city. More rich people in a city means the poor there live longer. Poor people in New York City, for example, live longer than poor people in Detroit.

Davidowitz suggests that maybe this happens because the poor learn better habits from the rich. I suspect the answer is simpler–here are a few possibilities:

1. The rich are effectively stopping the poor from doing self-destructive things, whether positively, eg, funding cultural that poor people go to rather than turn to drugs or crime out of boredom, or negatively, eg, funding police forces that discourage life-shortening crime.

2. The rich fund/support projects that improve general health, like cleaner water systems or better hospitals.

3. The effect is basically just a measurement error that doesn’t account for rich people driving up land prices. The “poor” of New York would be wealthier if they had Detroit rents.

(In general, I think Davidowitz is stronger when looking for correlations in the data than when suggesting explanations for it.)

Now contrast this with Davidowitz’s own study on where top achievers grow up:

I was curious where the most successful Americans come from, so one day I decided to download Wikipedia. …

[After some narrowing for practical reasons] Roughly 2,058 American-born baby boomers were deemed notable enough to warrant a Wikipedia entry. About 30 percent made it through achievements in art or entertainment, 29 percent through sports, 9 percent via politics, and 3 percent in academia or science.

And this is why we are doomed.

The first striking fact I noticed in the data was the enormous geographic variation in the likelihood of becoming a big success …

Roughly one in 1,209 baby boomers born in California reached Wikipedia. Only one in 4,496 baby boomers born in West Virginia did. … Roughly one in 748 baby boomers born in Suffolk County, MA, here Boston is located, made it to Wikipedia. In some counties, the success rate was twenty times lower. …

I closely examined the top counties. It turns out that nearly all of them fit into one of two categories.

First, and this surprised me, many of these counties contained a sizable college town. …

I don’t know why that would surprise anyone. But this was interesting:

Of fewer than 13,000 boomers born in Macon County, Alabama, fifteen made it to Wikipedia–or one in 852. Every single one of them is black. Fourteen of them were from the town of Tuskegee, home of Tuskegee University, a historically black college founded by Booker . Washington. The list included judges, writers, and scientists. In fact, a black child born in Tuskegee had the same probability of becoming a notable in a field outside of ports as a white child born in some of the highest-scoring, majority-white college towns.

The other factor that correlates with the production of notables?

A big city.

Being born in born in San Francisco County, Los Angeles County, or New York City all offered among the highest probabilities of making it to Wikipedia. …

Suburban counties, unless they contained major college towns, performed far worse than their urban counterparts.

A third factor that correlates with success is the proportion of immigrants in a county, though I am skeptical of this finding because I’ve never gotten the impression that the southern border of Texas produces a lot of famous people.

Migrant farm laborers aside, though, America’s immigrant population tends to be pretty well selected overall and thus produces lots of high-achievers. (Steve Jobs, for example, was the son of a Syrian immigrant; Thomas Edison was the son of a Canadian refugee.)

The variable that didn’t predict notability:

One I found more than a little surprising was how much money a state spends on education. In states with similar percentages of its residents living in urban areas, education spending did not correlate with rates of producing notable writers, artists, or business leaders.

Of course, this is probably because 1. districts increase spending when students do poorly in school, and 2. because rich people in urban send their kids to private schools.

BUT:

It is interesting to compare my Wikipedia study to one of Chetty’s team’s studies discussed earlier. Recall that Chetty’s team was trying to figure out what areas are good at allowing people to reach the upper middle class. My study was trying to figure out what areas are good at allowing people to reach fame. The results are strikingly different.

Spending a lot on education help kids reach the upper middle class. It does little to help them become a notable writer, artist, or business leader. Many of these huge successes hated school. Some dropped out.

Some, like Mark Zuckerberg, went to private school.

New York City, Chetty’s team found, is not a particularly good place to raise a child if you want to ensure he reaches the upper middle class. it is a great place, my study found, if you want to give him a chance at fame.

A couple of methodological notes:

Note that Chetty’s data not only looked at where people were born, but also at mobility–poor people who moved from the Deep South to the Midwest were also more likely to become upper middle class, and poor people who moved from the Midwest to NYC were also more likely to stay poor.

Davidowitz’s data only looks at where people were born; he does not answer whether moving to NYC makes you more likely to become famous. He also doesn’t discuss who is becoming notable–are cities engines to make the children of already successful people becoming even more successful, or are they places where even the poor have a shot at being famous?

I reject Davidowitz’s conclusions (which impute causation where there is only correlation) and substitute my own:

Cities are acceleration platforms for code. Code creates bifurcation. Bifurcation creates winners and losers while obliterating the middle.

This is not necessarily a problem if your alternatives are worse–if your choice is between poverty in NYC or poverty in Detroit, you may be better off in NYC. If your choice is between poverty in Mexico and poverty in California, you may choose California.

But if your choice is between a good chance of being middle class in Salt Lake City verses a high chance of being poor and an extremely small chance of being rich in NYC, you are probably a lot better off packing your bags and heading to Utah.

But if cities are important drivers of innovation (especially in science, to which we owe thanks for things like electricity and refrigerated food shipping,) then Auerswald has already provided us with a potential solution to their runaway effects on the poor: Henry George’s land value tax. As George accounts, one day, while overlooking San Francisco:

I asked a passing teamster, for want of something better to say, what land was worth there. He pointed to some cows grazing so far off that they looked like mice, and said, “I don’t know exactly, but there is a man over there who will sell some land for a thousand dollars an acre.” Like a flash it came over me that there was the reason of advancing poverty with advancing wealth. With the growth of population, land grows in value, and the men who work it must pay more for the privilege.[28]

Alternatively, higher taxes on fortunes like Zuckerberg’s and Bezos’s might accomplish the same thing.

Book Club: Code Economy: Economics as Information Theory

If the suggestion… that the economy is “alive” seems fanciful or far-fetched, it is much less so if we consider the alternative: that it is dead.

Welcome back to your regularly scheduled discussion of Auerswald’s The Code Economy: a Forty-Thousand-Year History. Today we are discussing Chapter 9: Platforms, but feel free to jump into the discussion even if you haven’t read the book.

I loved this chapter.

We can safely answer that the economy–or the sum total of human provisioning, consumptive, productive and social activities–is neither “alive” nor, exactly, “non-living.”

The economy has a structure, yes. (So does a crystal.) It requires energy, like a plant, sponge, or macaque. It creates waste. But like a beehive, it is not “conscious;” voters struggle to make any kind of coherent policies.

Can economies reproduce themselves, like a beehive sending out swarms to found new hives? Yes, though it is difficult in a world where most of the sensible human niches have already been filled.

Auerswald notes that his use of the word “code” throughout the book is not (just) because of its modern sense in the coding of computer programs, but because of its use in the structure of DNA–we are literally built from the instructions in our genetic “code,” and society is, on top of that, layers and layers of more code, for everything from “how to raise vegetables” to “how to build an iPhone” to, yes, Angry Birds.

Indeed, as I have insisted throughout, this is more than an analogy: the introduction of production recipes into economics is… exactly what the introduction of DNA i to molecular biology. it is the essential first step toward a transformation of economics into a branch of information theory.

I don’t have much to say about information theory because I haven’t studied information theory, beyond once reading a problem about a couple of people named Alice and Bob who were trying to send messages to each other, but I did read Viktor Mayer-Schönberger and, Kenneth Cukier‘s Big Data: A revolution that will transform how we live, work, and think a couple of weeks ago. It doesn’t rise to the level of “OMG this was great you must read it,” but if you’re interested in the subject, it’s a good introduction and pairs nicely with The Code Economy, as many of the developments in “big data” are relevant to recent developments in code. It’s also helpful in understanding why on earth anyone sees anything of value in companies like Facebook and LinkedIn, which will be coming up soon.

You know, we know that bees live in a hive, but do bees know? (No, not for any meaningful definition of “knowing.”) But imagine being a bee, and slowly working out that you live in a hive, and that the hive “behaves” in certain ways that you can model, just like you can model the behavior of an individual bee…

Anyway:

Economics has a lot to say about how to optimize the level of inputs to get output, but what about the actual process of tuning inputs into outputs? … In the Wonderful World of Widgets that i standard economic, ingredients combine to make a final product, but the recipe by which the ingredients actually become the product is nowhere explicitly represented.

After some papers on the NK model and th shift in organizational demands from pre-industrial economic production to post-industrial large-scale production by mega firms, (or in the case of communism, by whole states,) Auerswald concludes that

…the economy manages increasing complexity by “hard-wiring” solution into standards, which in turn define platforms.

Original Morse Telegraph machine, circa 1835 https://en.wikipedia.org/wiki/Samuel_Morse

This is an important insight. Electricity was once a new technology, whose mathematical rules were explored by cutting-edge scientists. Electrical appliances and the grid to deliver the electricity they run on were developed by folks like Edison and Tesla.

But today, the electrical grid reaches nearly every house in America. You don’t have to understand electricity at all to plug in your toaster. You don’t have to be Thomas Edison to lay electrical lines. You just have to follow instructions.

Electricity + electrical appliances replaced many jobs people used to do, like candle making or pony express delivery man, but electricity has not resulted in an overall loss of jobs. Rather, far more jobs now exist that depend on the electrical grid (or “platform”) than were eliminated.

(However, one of the difficulties or problems with codifying things into platforms is then, systems have difficulty handling other, perfectly valid methods of doing things. Early codification may lock-in certain ways of doing things that are actually suboptimal, like how our computer keyboard layout is intentionally difficult to use not because of anything to do with computers, but because typewriters in the 1800s jammed if people typed on them too quickly. Today, we would be better off with a more sensible keyboard layout, but the old one persists because too many systems use it.)

The Industrial Revolution was a time of first technological development, and then encoding into platforms of many varieties–transportation networks of water and rail; electrical, sewer, and fresh water grids; the large-scale production of antibiotics and vaccines; and even the codification of governments.

The English, a nation of a couple thousand years or so, are governed under a system known as “Common Law,” which is just all of the legal precedents and traditions built up over that time that have come into customary use.

When America was founded, it didn’t have a thousand years of experience to draw on because, well, it had just been founded, but it did have a thousand years of cultural memory of England’s government. English Common Law was codified as the base of the American legal system.

The Articles of Confederation, famous only for not working very well, were the fledgling country’s first attempt at codifying how the government should operate. They are typically described as failing because they allocated insufficient power to the federal government, but I propose a more nuanced take: the Articles laid out insufficient code for dealing with nation-level problems. The Constitution solved these problems and instituted the basic “platform” on which the rest of the government is built. Today, whether we want to ratify a treaty or change the speed limit on i-405, we don’t have to re-derive the entire decisions-making structure from scratch; legitimacy (for better or for worse) is already built into the system.

Since the days of the American and French revolutions, new countries have typically had “constitutions,” not because Common Law is bad, but because there is no need to re-derive from scratch successful governing platforms–they can just be copied from other countries, just as one firm can copy another firms organizational structure.

Continuing with Auerswald and the march of time:

Ask yourself what the greatest inventions were over the past 150 years: Penicillin? The transistor? Electrical power? Each of these has been transformative, but equally compelling candidates include universal time, container shipping, the TCP/IP protocols underlying the Internet, and the GSM and CDMA standards that underlie mobile telephony. These are the technologies that make global trade possible by making code developed in one place interoperable with code developed in another. Standards reduce barriers among people…

Auerswald, as a code enthusiast, doesn’t devote much space to the downsides of code. Clearly, code can make life easier, by reducing the number of cognitive tasks required to get a job done. Let’s take the matter of household management. If a husband and wife both adhere to “traditional gender norms,” such as an expectation that the wife will take care of internal household chores like cooking and vacuuming, and the husband will take care of external chores, like mowing the lawn, taking out the trash, and pumping gas, neither spouse has to ever discuss “who is going to do this job” or wonder “hey, did that job get done?”

Following an established code thus aids efficiency and appears to decrease marital stress (there are studies on this,) but this does not mean that the code itself is optimal. Perhaps men make better dish-washers than women. Or for a couple with a disabled partner, perhaps all of the jobs would be better performed by reversing the roles.

Technological change also encourages code change:

The replacement of manual push-mowers with gas-powered mowers makes mowing the lawn easier for women, so perhaps this task would be better performed by housewives. (Even the Amish have adopted milking machines on the grounds that by pumping the milk away from the cow for you, the machines enable women to participate equally in the milking–a task that previously involved picking up and carrying around 90 lb milkjugs.)

But re-writing the entire code is work and involves a learning curve as both parties sort out and get used to new expectations. (See my previous thread on “emotional labor” and its relation to gender norms.) So even if you think the old code isn’t fair or optimal, it still might be easier than trying to make a new code–and this extends to far more human relations than just marriage.

And then you get cases where the new technology is incompatible with the old code. Take, for example, the relationship between transportation, weights and measures, and the French Revolution.

A country in which there is no efficient way to get from Point A to Point B has no need for a standardized set of weights and measures, as people in Community A will never encounter or contend with whatever system they are using over in Community B. Even if a king wanted to create a standard system, he would have difficulty enforcing it. Instead, each community tends to evolve a system that works well for its own needs. A community that grows bananas, for example, will come up with measures suitable to bananas, like the “bunch,” a community that deals in grain will invent the “bushel,” and a community that enumerates no goods, like the Piraha, will not bother with large quantities quantities.

(Diamonds are measured in “carats,” which have nothing to do with the orange vegetable, but instead are derived from the seeds of the carob tree, which apparently are small enough to be weighed against small stones.)

Since the French paid taxes, there was some demand for standardized weights and measures within each province–if your taxes are “one bushel of grain,” you want to make sure “bushel” is well defined so the local lord doesn’t suddenly define this year’s bushel as twice as big as last year’s bushel–and likewise, the lord doesn’t want this year’s bushel to be defined as half the size as last year’s.

But as roads improved and trade increased, people became concerned with making sure that a bushel of grain sold in Paris was the same as a bushel purchased in Nice, or that 5 carats of diamonds in Bordeaux was still 5 carats when you reached Cognac.

But the established local power of the local nobility made it very hard to change whatever measures people were using in each individual place. That is, the existing code made it hard to change to a more efficient code, probably because local lords were concerned the new measures would result in fewer taxes, and the local peasants concerned it would result in higher taxes.

Thus it was only with the decapitation of the Ancien Regime and wiping away of the privileges and prerogatives of the nobility that Revolutionary France established, as one of its few lasting reforms, a universal system of weights and measures that has come down to us today as the metric or SI system.

Now, speaking as an American who has been trained in both Metric and Imperial units, using multiple systems can be annoying, but is rarely deadly. On the scale of sub-optimal ideas, humans have invented far worse.

Quoting Richard Rhodes, The Making of the Atomic Bomb:

“The end result of the complex organization that was the efficient software of the Great War was the manufacture of corpses.

This essentially industrial operation was fantasized by the generals as a “strategy of attrition.” The British tried to kill Germans, the Germans tried to kill British and French and so on, a “strategy” so familiar by now that it almost sounds normal. It was not normal in Europe before 1914 and no one in authority expected it to evolve, despite the pioneering lessons of the American Civil War. Once the trenches were in place, the long grave already dug (John Masefield’s bitterly ironic phrase), then the war stalemated and death-making overwhelmed any rational response.

“The war machine,” concludes Elliot, “rooted in law, organization, production, movement, science, technical ingenuity, with its product of six thousand deaths a day over a period of 1,500 days, was the permanent and realistic factor, impervious to fantasy, only slightly altered by human variation.”

No human institution, Elliot stresses, was sufficiently strong to resist the death machine. A new mechanism, the tank, ended the stalemate.”

Russian Troops waiting for death

On the Eastern Front, the Death Machine was defeated by the Russian Revolution, as the canon fodder decided it didn’t want to be turned into corpses anymore.

I find World War I more interesting than WWII because it makes far less sense. The combatants in WWII had something resembling sensible goals, some chance of achieving their goals, and attempted to protect the lives of their own people. WWI, by contrast, has no such underlying logic, yet it happened anyway–proof that seemingly logical people can engage in the ultimate illogic, even as it reduces whole countries to nothing but death machines.

Why did some countries revolt against the cruel code of war, and others not? Perhaps an important factor is the perceived legitimacy of the government itself (though regular food shipments are probably just as critical.) Getting back to information theory, democracy itself is a kind of blockchain for establishing political legitimacy, (more on this in a couple of chapters) which may account for why some countries perceived their leadership as more legitimate, and other countries suddenly discovered, as information about other people’s opinions became easier to obtain, that the government enjoyed very little legitimacy.

But I am speculating, and have gotten totally off-topic (Auerswald was just discussing the establishment of TCP/IP protocols and other similar standards that aid international trade, not WWI!)

Returning to Auerswald, he cites a brilliant quote from Alfred North Whitehead

“Civilization advances by extending the number of operations we can perform without thinking about them.”

As we were saying, while sub-optimal (or suicidal) code can and does destroy human societies, good code can substantially increase human well-being.

The discovery and refinement of new inventions, technologies, production recipes, etc., involves a steep learning curve as people first figure out how to make the thing work and to source and put together all of the parts necessary to build it, (eg, the invention of the automobile in the late 1800s and early 1900s,) but once the technology spreads, it simply becomes part of the expected infrastructure of everyday life (eg, the building of interstate highways and gas stations, allowing people to drive cars all around the US,) a “platform” on which other, future innovations build. Post-1950, most automobile-driven innovation was located not in refinements to the engines or brakes, but in things you can do with vehicles, like long-distance shipping.

Interesting things happen to income as code becomes platforms, but I haven’t worked out all of the details.

Continuing with Auerswald:

Note that, in code economics, a given country’s level of “development” is not sensibly measured by the total monetary value of all goods and services it produces…. Rather, the development of a country consists of … it capacity to execute more complex code. …

Less-developed countries that lack the code to produce complex products will import them, and they will export simpler intermediate products and raw materials in order to pay for the required imports.

By creating and adhering to widely-observed “standards,” increasing numbers of countries (and people) are able to share inventions, code, and development.

Of the drivers of beneficial trade, international standards are at once among the most important and the least appreciated. … From the invention of bills of exchange in the Middle Ages … to the creation of twenty-first-century communications protocols, innovations in standards have lowered the cost and enhanced the value of exchange across distance. …

For entrepreneurs in developing countries, demonstrated conformity with international standards… is a universally recognized mark of organizational capacity that substantially eases entry into global production and distribution networks.

In other words, you are more likely to order steel from a foreign factory if you have some confidence that you will actually receive the variety you ordered, and the factory can signal that it knows what it is doing and will actually deliver the specified steel by adhering to international standards.

On the other hand, I think this can degenerate into a reliance on the appearance of doing things properly, which partially explains the Elizabeth Holmes affair. Holmes sounded like she knew what she was doing–she knew how to sound like she was running a successful startup because she’d been raised in the Silicon Valley startup culture. Meanwhile, the people investing in Holmes’s business didn’t know anything about blood testing (Holmes’s supposed invention tested blood)–they could only judge whether the company sounded like it was a real business.

Auerswald then has a fascinating section comparing each subsequent “platform” that builds on the previous “platform” to trophic levels in the environment. The development of each level allows for the development of another, more complex level above it–the top platform becomes the space where newest code is developed.

If goods and services are built on platforms, one atop the other, then it follows that learning at higher levels of the system should be faster than learning at lower levels, for the simple reason that leaning at higher levels benefits from incremental learning all the way down.

There are two “layers” of learning. Raw material extraction shows high volatility around a a gradually increasing trend, aka slow learning. By contrast, the delivery of services over existing infrastructure, like roads or wireless networks, show exponential growth, aka fast learning.

In other words, the more levels of code you already have established and standardized into platforms, the faster learning goes–the basic idea behind the singularity.

 

That’s all for today. See you next week!

Book Club: The Code Economy, Chs. 6-7: Learning Curves

Welcome back to EvX’s Book Club: The Code Economy, by Philip Auerswald. Today’s entry is going to be quick, because summer has started and I don’t have much time. Ch 6 is titled Information, and can perhaps be best summarized:

The challenge is to build a reliable economy with less reliable people. In this way, the economy is an information processing organism. …

When I assert that economics must properly be understood as a branch of information theory, I am reffing to the centrality of the communication problem that exists whenever one person attempts to share know-how with another. I am referring, in other words, to the centrality of code.

Auerswald goes on to sketch some relevant background on “code” as a branch of economics:

The economics taught in undergraduate courses is a great example of history being written by the victors. Because the methodologies of neoclassical economics experienced numerous triumphs in the middle of the twentieth century, the study of the distribution of resources within the economy–choice rather than code and, to a lesser extent, consumption rather than  production–came to be taught as the totality of economics. However, the reality is that choice and code have always coexisted, not only in the economy itself but in the history of economic thought.

And, an aside, but interesting:

Indeed, from 1807 to the time Jevons was born, the volume of shipping flowing through the port of Liverpool more than doubled.

However:

And yet, while both the city’s population and worker’s wages increased steadily, a severe economic rift occurred that separated the haves from the have-nots. As wealthy Liverpudlians moved away from the docks to newly fashionable districts on the edges of the city, those left behind in the center of town faced miserable conditions.From 1830 through 1850, life expectancy actually decreased in Liverpool proper from an already miserable 32 years to a shocking 25 years.

(You should see what happened to life expectancies in Ireland around that time.)

A reliable economy built with less reliable people is one in which individuals have very little autonomy, because autonomy means unreliable people messing things up.

Thereafter, a series of economists, Herbert Simon foremost among them, put the challenges of gathering, sharing, and processing economically relevant information at the center of their work.

Taken together and combined with foundational insights from other fields–notably evolutionary biology and molecular biology–the contributions of these economists constitute a distinct domain of inquiry within economics. These contributions have focused on people as producers and on the algorithms that drive the development of the economy.

This domain of inquiry is Code Economics.

I am rather in love with taking the evolutionary model and applying it to other fields, like the spread of ideas (memes) or the growth of cities, companies, economies, or whole countries. That is kind of what we do, here at EvolutionistX.

Chapter 7 is titled Learning: The Dividend of Doing. It begins with an amusing tale about Julia Child, who did not even learn to cook until her mid to late thirties, and then became a rather famous chef/cookbook writer. (Cooking recipes are one of Auerswald’s favored examples of “code” in action.)

Next Auerswald discusses Francis Walker, first president of the American Economic Association. Walker disagreed with the “wage fund theory” and with Jevons’s simplifying assumption that firms can be modeled as simply hiring workers until it cannot make any more money by hiring more workers than by investing in more capital.

Jevons’s formulation pushes production algorithms–how businesses are actually being run–into the background and tradeoffs between labor and capital to the foreground. But as Walker notes:

“We have the phenomenon in every community and in every trade, in whatever state of the market,” Walker observes, “of some employers realizing no profits at all, while other are making fair profits; others, again, large profits; others, still, colossal profits. Side by side, in the same business, with equal command of capital, with equal opportunities, one man is gradually sinking a fortune, while another is doubling or trebling his accumulations.”

The relevant economic data, when it finally became available, confirmed Walker’s belief about the distribution of profits, yet the difference between the high-profit and low-profit firms does not appear to hinge primarily on the question of how much labor should be substituted for capital and vice versa.

Walker argued that more profitable entrepreneurs are that way because they are able to solve a difficult problem more effectively than other entrepreneurs. … three core mechanisms for the advance of code: learning, evolution, and the layering of complexity though the development of platforms.

Moreover:

…in the empirical economics of production, few discoveries have been more universal or significant than that of the firm-level learning curve. As economist James Bessen notes, “Developing the knowledge and skills needed to implement new technologies on a large scale is a difficult social problem that takes a long time to resolve… A new technology typically requires more than an invention in order to be designed, built, installed, operated, and maintained. Initially, much of this new technical knowledge develops slowly because it is learned through experience, not in the classroom.”

Those of you who are familiar with business economics probably find learning curves and firm growth curves boring and old-hat, but they’re new and quite fascinating to me. Auerswald has an interesting story about the development of airplanes and a challenge to develop cheaper, two-seat planes during the Depression–could a plane be built for under a $1,000? $700?

(Make sure to read the footnote on the speed of production of “Liberty Ships.”)

The rest of the chapter discusses the importance of proper firm management for maximizing efficiency and profits. Now, I have an instinctual aversion to managers, due to my perception that they tend to be parasitic on their workers or at least in competition with them for resources/effort, but I can admit that a well-run company is likely more profitable than a badly run one. Whether it is more pleasant for the workers is another matter, as the folks working in Amazon’s warehouses can tell you.

So why are some countries rich and others poor?

Whereas dominant variants of the neoclassical production model emphasize categories such as public knowledge and organization, which can be copied and implemented at zero cost, code economics suggests that such categories are unlikely to be significantly relevant in the practical work of creating the business entities that drive the progress of human society. This is because code at the level of a single company–what I term a “production algorithm”–includes firm-specific components. Producers far from dominant production clusters must learn to produce through a costly process of trial and error. Market-driven advances in production recipes, from which venture with proprietary value can be created, require a tenacious will to experiment, to learn, and to document carefully the results of that learning. Heterogeneity among managers… is thus central to understanding observed differences between regions and nations. …

Management and the development of technical standards combined to enable not just machines but organizations to be interoperable and collaborative. Companies thus could become far bigger and supply chains far more complex than every before.

As someone who actually likes shopping at Ikea, I guess I should thank a manager somewhere.

Auerswald points out that if communication of production algorithms and company methods were perfect and costless, then learning curves wouldn’t exist:

All of these examples underscore the following point, core to code economics: The imperfection of communication is not a theory. It is a ubiquitous and inescapable physical reality.

That’s all for now, but how are you enjoying the book? Do you have any thoughts on these chapters? I enjoyed them quite a bit–especially the part about Intel and the graphs of the distribution of management scores by country. What do you think of France and the UK’s rather lower “management” scores than the US and Germany?

Join us next week for Ch. 8: Evolution–should be exciting!

Book Club: The Industrial Revolution and its Discontents, Code Economy, ch. 5

1. The Industrial Revolution and its consequences have been a disaster for the human race. They have greatly increased the life-expectancy of those of us who live in “advanced” countries, but they have destabilized society, have made life unfulfilling, have subjected human beings to indignities, have led to widespread psychological suffering (in the Third World to physical suffering as well) and have inflicted severe damage on the natural world. The continued development of technology will worsen the situation. It will certainly subject human beings to greater indignities and inflict greater damage on the natural world, it will probably lead to greater social disruption and psychological suffering, and it may lead to increased physical suffering even in “advanced” countries. –Kaczynski, Industrial Society and Its Future

The quest to find and keep a “job for life”–stable, predictable work that pays enough to live on, is reachable by available transportation, and lends a sense of meaning to their daily lives–runs though every interview transcript, from those who are unemployed to those who have “made it” to steady jobs like firefighting or nursing. Traditional blue-collar work–whether as a factory worker or a police officer–has become increasingly scarce and competitive, destroyed by a technologically advanced and global capitalism that prioritizes labor market “flexibility”… Consequently, the post-industrial generation is forced to continuously grapple with flux and contingency, bending and adapting to the demands of they labor market until they feel that they are about to break. –Silva, Coming up Short: Working-Class Adulthood in an Age of Uncertainty

The historical record confirms that the realities of the ongoing processes of mechanization and industrialization, as noted early on by Lord Byron, were very different from the picture adherents to the wage fund theory held in their heads. While the long-term impact of the Industrial revolution had on the health and well-being of the English population was strongly positive, the first half of the nineteenth century was indeed a time of exceptional hardship for English workers. In a study covering the years 1770-1815, Stephen Nicholas and Richard Steckel report “falling heights of urban-and rural-born males after 1780 and a delayed growth spurt for 13- to 23-year old boys,” as well as a fall in the English workers’ height relative to that of Irish convicts. By the 1830s, the life expectancy of anyone born in Liverpool and Manchester was less than 30 years–as low as had been experienced in England since the Black Death of 1348. –Auerswald, The Code Economy

On the other hand:

Chapter 5 of The Code Economy, Substitution, explores the development of economic theories about the effects of industrialization and general attempts at improving the lives of the working poor.

… John Barton, a Quaker, published a pamphlet in 1817 titled, Observations on the Circumstances Which Influence the Condition of the Laboring Classes of Society. … Barton began by targeting the Malthusian assumption that population grows in response to increasing wages. … He began by noting that there was no a priori reason to believe that labor and capital were perfect complements, as classical economists implicitly assumed. The more sensible assumption was that, as wages increased, manufacturers and farmers alike would tend to substitute animals or machines for human labor. Rather than increasing the birth rate, the higher wages brought on by the introduction of new machinery would increase intergenerational differences in income and thus delay child-bearing. Contrary to the Malthusian line of argument, this is exactly what happened.

There’s an end note that expands on this (you do read the end notes, right?) Quoting Barton, 1817:

A rise of wages then does not always increase population… For every rise of wages tends to decrease the effectual demand for labor… Suppose that by a general agreement among farmers the rate of agricultural wage were raised from 12 shillings to 24 shillings per week–I cannot imagine any circumstance calculated more effectually to discourage marriage. For it would immediately become a a most important object to cultivate with as few hands as possible; wherever the use of machinery, or employment of horses could be substituted for manual labor, it would be done; and a considerable portion of existing laborers would be out of work.

This is the “raising the minimum wage will put people out of work” theory. Barton also points out that when people do manage to get these higher-paid jobs, they will tend to be older, more experienced laborers rather than young folks looking to marry and start a family.

A quick perusal of minimum wage vs. unemployment rate graphs reveals some that are good evidence against minimum wages, and some that are good evidence in favor of them. Here’s a link to a study that found no effects of minimum wage differences on employment. The American minimum wage data is confounded by things like “DC is a shithole.” DC has the highest minimum wage in the country and the highest unemployment rate, but Hawaii also has a very high minimum wage and the lowest unemployment rate. In general, local minimum wages probably reflect local cost of living/cost of living reflects wages. If we adjust for inflation, minimum wage in the US peaked around 1968 and was generally high throughout the 60s and 70s, but has fallen since then. Based on conversations with my parents, I gather the 60s and 70s were a good time to be a worker, when unskilled labor could pretty easily get a job and support a family; unemployment rates do not seem to have fallen markedly since then, despite lower real wages. A quick glance at a map of minimum wages by country reveals that countries with higher minimum wage tend to be nicer countries that people actually want to live in, but the relationship is not absolute.

We might say that this contradicts Barton, but why have American wages stagnated or gone down since the 60s?

1. Automation

2. Emergence of other economic competitors as Europe and Japan recovered from WWII

3. Related: Outsourcing to cheaper workers in China

4. Labor market growth due to entry of women, immigrants, and Boomers generally

Except for 2, that sounds a lot like what Barton said would happen. Wages go up => people move where the good jobs are => labor market expands => wages go down. If labor cannot move, then capitalists can either move the businesses to the labor or invest in machines to replace the labor.

On the other hand, the standard of living is clearly higher today than it was in 1900, even if wages, like molecules diffusing through the air, tend to even out over time. Why?

First, obviously, we learned to extract more energy from sources like oil, coal, and nuclei. A loom hooked up (via the electrical grid) to an electric turbine can make a lot more cloth per hour than a mere human working with shuttles and thread.

Second, we have gotten better at using the energy we extract–Auerswald would call this “code.”

Standards of living may thus have more to do with available resources (including energy) and our ability to use those resources (both the ‘code” we have developed and our own inherent ability to interact with and use that code,) than with the head-scratching entropy of minimum wages.

Auerswald discusses the evolution of David Ricardo’s economic ideas:

By incorporating the potential for substitution between capital and labor, Ricardo led the field of economics in rejecting the wage fund theory, along with its Dickensian implications for policy. He accepted the notion the introduction of new machinery would result in the displacement of workers. The upshot was that the workers were still assumed to be doomed, but the reason was now substitution of machines for labor, not scarcity of a Malthusian variety.

Enter Henry George, with a radically different perspective:

“Like a flash it came over me that there was the reason of advancing poverty with advancing wealth. With the growth of population, land grows in value, and the men who work it must pay more for the privilege.” …

George asserted that increasing population density, (not, as Malthus claimed, population decline) was the source of increased prosperity in human societies: “Wealth is greatest where population is densest… the production of wealth to a given amount of labor increases as population increases.”  The frequent interactions among people in densely populated cities accelerates the emergence and evolution of code. However, while population growth and increased density naturally bring increased prosperity, they also, just as naturally, bring increasing inequality and poverty. Why? Because the fruits of labor are inevitably gathered by the owners of land.

In other words, increasing wages => increasing rents and the workers are right back where they started while the landlords are sitting pretty.

In sharp contrast with Karl Marx, … George stated that “the antagonism of interest is not between labor and capital… but is in reality between labor and capital on the one side and the land ownership on the other.” The implication of his analysis was as simple as it was powerful: to avoid concentrating wealth in the hands of the few, it was the government’s responsibility to eliminate all taxes on capital and laborers, the productive elements of the economy, and to replace those taxes with a single tax on land.

Note: not a flat tax on land, but a tax relative to the land’s sale value.

I was glad to see Henry George in the book because I enjoy George’s theories and they are under-discussed, especially relative to Marxism. You will find massive online communities of Marxists despite the absolute evidence that Marxism is a death machine, but relatively few enthusiastic Georgists. One of the things I rather appreciate about Georgism is its simplicity; the complication of the tax code is its own, additional burden on capitalists and workers alike. Almost any simplified tax code, no matter how “unfair,” would probably improve maters a great deal.

But there’s more, because this is a dense chapter. Auerswald notes that the increasing complexity of code (ie, productivity) has lead to steadily increasing standards of living over the past two centuries, at least after the Industrial Revolution’s initial cataclysm.

Quoting economist Paul Douglas, some years later:

“The increased use of mechanical appliances in offices has tended to lower the skill required. An old-fashioned bookkeeper, for instance, had to write a good hand, he had to be able to multiply and divide with absolute accuracy. Today his place is taken by a girl who  operates a book-keeping machine, and it has taken her a few weeks at mot to become a skilled bookkeeper.” In other words, the introduction of machinery displaced skilled workers for the very same reason it enhanced human capabilities: it allowed a worker with relatively rudimentary training to perform tasks that previously required a skilled worker.

…”Another way of looking at it, is this: Where formerly the skill used in bookkeeping was exercised by the bookkeeper, today that skill is exercised by the factory employees who utilize it to manufacture a machine which can do the job of keeping books, when operated by someone of skill far below that of the former bookkeeper. And because of this transfer of skill form the office to the factory, the rewards of skill are likewise transferred to the wage-earner at the plant.”

This is a vitally important pint… The essence of this insight is that introducing more powerful machines into the workplace does more than simply encode  into the machine the skills or capabilities that previously resided only in humans; it also shifts the burden of skill from one domain of work to another. … A comparable shift in recent decades has been from the skill of manufacturing computing machines (think IBM or Dell in their heydays) to that of creating improved instructions for computing machines’ the result has been a relative growth in programmers’ wages. The underlying process is the same. Improvements in technology will predictably reduce demand for the skills held by some workers, but they also will enhance the capabilities of other workers and shift the requirements of skill from one domain of work to the other.”

The problem with this is that the average person puts in 15-20 years of schooling (plus $$$) in order to become skilled at a job, only to suddenly have that job disappear due to accelerating technological change/improvement, and then some asshole one comes and tells them they should just “learn to code” spend another two to four years unemployed and paying for the privilege of learning another job and don’t see how fucking dispiriting this is to the already struggling.

The struggle for society is recognizing that even as standards of living may be generally rising, some people may absolutely be struggling with an economic system that offers much less certainty and stability than our ancestors enjoyed.

A final word from Auerswald:

… work divides or “bifurcates” as code advances in a predictable and repeatable way. The bifurcation of work in a critical mechanism by which the advance of code yields improvements in human well-being at the same time as it increases human reliance on code.