ETA: apparently everyone thinks this guy’s work is wrong.
I thought his paper was nice and on a good track, but take with appropriate salt.
I am tempted to jest that the Voynich manuscript turned out to have been so difficult to decode because it was written by women, but this isn’t quite true.
It was just written in an extinct language of which we have almost no other written examples,
With an alphabet full of unknown characters,
And full of abbreviations and calligraphic shorthands.
If you’re not familiar with the Voynich manuscript, it’s a 240 page book that appears to have been written in Italy in the late 1400s. It’s filled with pictures of things like plants, bathing women, and a rather nice fold-out diagram of a volcano. It came to the world’s attention after Wilfrid Voynich purchased it from an old books dealer in 1912.
Because the Voynich manuscript is so weird, (especially the alphabet,) people have struggled for years to decipher it. Is it in code? Is it some non-European language like Chinese? Is it an elaborate hoax?
Given its resistance to all previous attempts at translation, I had written it off as probably a hoax–not a modern one perpetuated by Voynich, but a very old one played on some Medieval personage to sell them a worthless book full of supposed secret, magical knowledge for a handsome sum of money.
But it appears that Voynich has, at long last, been decoded by Gerard Cheshire.
It turns out that this “unknown language” isn’t Finnish, Basque, Navajo or something similarly difficult, but a kind of medieval Italian (or perhaps more accurately, late Latin,) known as proto-Romance. We have plenty of written examples of ancient Italian (otherwise known as Latin) and plenty of modern Italian, but few from the in-between period. It’s a bit like finding something written in Chaucerian English when you’re only familiar with modern English and Beowulf.
With this insight, the authors were able to decipher the strange alphabet, which employs no capitals but several extra symbols for dip- and tripthongs. (Kind of like Sequoia’s syllabary.)
The result is orthographically lovely, but very complicated. You should read the full article for an explanation for what all of the letters mean.
The really interesting thing is that this alphabet is nearly unique. Did the local nuns invent it for the purpose of the book? Were they literate in the regular alphabet used on the mainland, but felt it would be better to develop their own? Or was this commonly used in the area, but the vagaries of time destroyed all other remnants of it?
They found one of the keys to deciphering the manuscript lies in the map of the volcanic islands:
Within the manuscript there is a foldout pictorial map that provides the necessary information to date and locate the origin of the manuscript. It tells the adventurous, and rather inspiring, story of a rescue mission, by ship, to save the victims of a volcanic eruption in the Tyrrhenian Sea that began on the evening of the 4 February 1444 … The manuscript originates from Castello Aragonese, an island castle and citadel off Ischia, and was compiled for Maria of Castile, Queen of Aragon, (1401–58) who led the rescue mission as regent during the absence of her husband, King Alfonso V of Aragon (1396–1458) who was otherwise occupied, having only recently conquered and then taken control of Naples in February 1443. …
The island of Ischia is historically famous for its hot volcanic spas, which exist to this day. The manuscript has many images of naked women bathing in them, both recreationally and therapeutically. There are also images of Queen Maria and her court conducting trade negotiations whilst bathing. Clearly the spa lifestyle was highly regarded as a form of physical cleansing and spiritual communion, as well as a general means of relaxation and leisure. In many respects it would have been preferable to living in nearby Naples, which was the most important and cosmopolitan of cities in the Mediterranean at the time, but was still potentially dangerous for the spouse of an invading king. For example, in 1448 the barons of Naples launched a failed rebellion against Alfonso to reclaim their city.
In other words, while the menfolk were away, the Queen Maria of Ischia, a lovely little volcanic island off the coast of Naples, (the Wikipedia page is nice and has a couple of pictures of the castle where Queen Maria lived) had to lead the court, negotiate trade deals, and even led a rescue mission to an exploding volcano. She then decided to commission a local nun to write her a book on various matters of importance to the nearly all-female court. The various isolations inherent in island life probably account for several of the manuscripts peculiarities, from language to text.
Proto-Romance is thought to be ancestral not only to modern Italian, but to the various other romance languages, as well. It was a kind of lingua franca in the Mediterranean before modern political borders forced Italian, Spanish, Portuguese, etc., to fully differentiate. From the paper:
So, we have proto-Romance words surviving in the Mediterranean from Portugal, in the west, to Turkey, in the east. Clearly, it was a cosmopolitan lingua franca until the late Medieval period, when the political map began to inhibit meme flow, so that cultural isolation caused the modern languages to begin evolving. As a result, proto-Romance survived by vestigial fragmentation of its lexicon into the languages we see today. As such, manuscript MS408 is immensely important, because it is the only documentation of a language that was once ubiquitous over the Mediterranean and subsequently became the foundation for southern European linguistics in the present day.
There is another manuscript to introduce here, because it has similarity in calligraphic style and similarly combined letterforms. It is a memoire written by Loise De Rosa (1385–1475), who lived and worked in the court of Naples. It is titled De Regno di Napoli (The Kingdom of Naples) …
We can see that the calligraphic forms are quite legible and familiar to the modern eye and also noticeably different from those shared by manuscript MS408 and De Rosa. …
De Rosa’s work thus provides documentation of a writing system and a language akin to those of manuscript MS408, demonstrating that both evolved from the same naïve linguistic rootstock: i.e. both had emerged from Vulgar Latin, but in different ways due to their geographical and cultural separation. …
In fact we know, from De Rosa’s manuscript, that he fled to the safety of Castello Aragonese in 1441–42, when Alfonso was busy conquering Naples: He writes: ‘The patron said to me: “Son of mine, go to Ischia, for the great of age the place is safe”. I went to the marina and took a boat that travelled to the Castello di Ischia’. As incredible as it may seem, the chances are that De Rosa actually met the author of manuscript MS408 during his stay at the citadel.
So de Rosa met Maria and probably the nun who wrote the Voynich herself. It’s a really incredible story, both in the manuscript’s creation and the efforts it took to decaode it, and I encourage you to read the full article.
Watson parses questions into different keywords and sentence fragments in order to find statistically related phrases. 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. The more algorithms that find the same answer independently the more likely Watson is to be correct. 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.
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 …
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.
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, 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. Leibniz communicated his ideas of the binary system representing Christianity to the Emperor of China hoping it would convert him. Leibniz may be the only major Western philosopher who attempted to accommodate Confucian ideas to prevailing European beliefs.
Leibniz’s attraction to Chinese philosophy originates from his perception that Chinese philosophy was similar to his own. 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.
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?
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…
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.
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. …
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?
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. He therefore argues that all humans share the same underlying linguistic structure, irrespective of sociocultural differences. 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. 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. 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“.
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.
I recently finished three books on “big data”– Big Data: A Revolution That Will Transform How We Live, Work, and Think, by Viktor Mayer-Schönberger and Kenneth Cukier; Everybody Lies: Big Data, New Data, and What the Internet can tell us about who we Really Are, by Seth Stephens-Davidowitz; andBig Data At Work: Dispelling the Myths, Uncovering the opportunities, by Thomas H. Davenport.
None of these books was a whiz-bang thriller, but I enjoyed them.
Big Data was a very sensible introduction. What exactly is “big data”? It’s not just bigger data sets (though it is also that.) It’s the opportunity to get all the data.
Until now, the authors point out, we have lived in a data poor world. We have had to carefully design our surveys to avoid sampling bias because we just can’t sample that many people. There’s a whole bunch of math done over in statistics to calculate how certain we can be about a particular result, or whether it could just be the result of random chance biasing our samples. I could poll 10,000 people about their jobs, and that might be a pretty good sample, but if everyone I polled happens to live within walking distance of my house, is this a very representative sample of everyone in the country? Now think about all of those studies on the mechanics of sleep done on whatever college students or homeless guys a scientist could convince to sleep in a lab for a week. How representative are they?
Today, though, we suddenly live in a data rich world. An exponentially data rich world. A world in which we no longer need to correct for bias in our sample, because we don’t have to sample. We can just get… all the data. You can go to Google and find out how many people searched for “rabbit” on Tuesday, or how many people misspelled “rabbit” in various ways.
Data is being used in new and interesting (and sometimes creepy) ways. Many things that previously weren’t even considered data are now being quantitized–like one researcher quantitizing people’s backsides to determine whether a car is being driven by its owner, or a stranger.
One application I find promising is using people’s searches for various disease symptoms to identify people who may have various diseases before they seek out a doctor. Catching cancer patients earlier could save millions of lives.
I don’t have the book in front of me anymore, so I am just going by memory, but it made a good companion to Auerswald’s The Code Economy, since the modern economy runs so much on data.
Everybody Lies was a much more lighthearted, annecdotal approach to the subject, discussing lots of different studies. Davidowitz was inspired by Freakonomics, and he wants to use Big Data to uncover hidden truths of human behavior.
The book discusses, for example, people’s pornographic searches, (as per the title, people routinely lie about how much porn they look at on the internet,) and whether people’s pornographic preferences can be used to determine what percent of people in each state are gay. It turns out that we can get a break down of porn queries by state and variety, allowing a rough estimate of the gay and straight population of each state–and it appears that what people are willing to tell pollsters about their sexuality doesn’t match what they search for online. In more conservative states, people are less likely to admit to pollsters that they are gay, but plenty of supposedly “straight” people are searching for gay porn–about the same number of people as actually admit to being gay in more liberal states.
Stephens-Davidowitz uses similar data to determine that people have been lying to pollsters (or perhaps themselves) about whom they plan to vote for. For example, Donald Trump got anomalously high votes in some areas, and Obama got anomalously low votes, compared to what people in those areas told pollsters. However, both of these areas correlated highly with areas of the country where people made a lot of racist Google searches.
Most of the studies discussed are amusing, like the discovery of the racehorse American Pharaoh. Others are quite important, like a study that found that child abuse was probably actually going up at a time when official reports said it wasn’t–the reports probably weren’t showing abuse due to a decrease in funding for investigating abuse.
At times the author steps beyond the studies and offers interpretations of why the results are the way they are that I think go beyond what the data tells, like his conclusion that parents are biased against their daughters because they are more concerned with girls being fat than with boys, or because they are more likely to Google “is my son a genius?” than “is my daughter a genius?”
I can think of a variety of alternative explanations. eg, society itself is crueler to overweight women than to overweight men, so it is reasonable, in turn, for parents to worry more about a daughter who will face cruelty than a boy who will not. Girls are more likely to be in gifted programs than boys, but perhaps this means that giftedness in girls is simply less exceptional than giftedness in boys, who are more unusual. Or perhaps male giftedness is different from female giftedness in some way that makes parents need more information on the topic.
Now, here’s an interesting study. Google can track how many people make Islamophobic searches at any particular time. Compared against Obama’s speech that tried to calm outrage after the San Bernardino attack, this data reveals that the speech was massively unsuccessful. Islamophobic searches doubled during and after the speech. Negative searches about Syrian refugees rose 60%, while searches asking how to help dropped 35%.
In fact, just about every negative search we cold think to test regarding Muslims shot up during and after Obama’s speech, and just about every positive search we could think to test declined. …
Instead of calming the angry mob, as everybody thought he was doing, the internet data tells us that Obama actually inflamed it.
However, Obama later gave another speech, on the same topic. This one was much more successful. As the author put it, this time, Obama spent little time insisting on the value of tolerance, which seems to have just made people less tolerant. Instead, “he focused overwhelmingly on provoking people’s curiosity and changing their perceptions of Muslim Americans.”
People tend to react positively toward people or things they regard as interesting, and invoking curiosity is a good way to get people interested.
The author points out that “big data” is most likely to be useful in fields where the current data is poor. In the case of American Pharaoh, for examples, people just plain weren’t getting a lot of data on racehorses before buying and selling them. It was a field based on people who “knew” horses and their pedigrees, not on people who x-rayed horses to see how big their hearts and lungs were. By contrast, hedge funds investing in the stock market are already up to their necks in data, trying to maximize every last penny. Horse racing was ripe for someone to become successful by unearthing previously unused data and making good predictions; the stock market is not.
And for those keeping track of how many people make it to the end of the book, I did. I even read the endnotes, because I do that.
Big Data At Work was very different. Rather than entertain us with the success of Google Flu or academic studies of human nature, BDAW discusses how to implement “big data” (the author admits it is a silly term) strategies at work. This is a good book if you own, run, or manage a business that could utilize data in some way. UPS, for example, uses driving data to minimize package delivery routes; even a small saving per package by optimizing routes leads to a large saving for the company as a whole, since they deliver so many packages.
The author points out that “big data” often isn’t big so much as unstructured. Photographs, call logs, Facebook posts, and Google searches may all be “data,” but you will need some way to quantitize these before you can make much use of them. For example, companies may want to gather customer feedback reports, feed them into a program that recognizes positive or negative language, and then quantitizes how many people called to report that they liked Product X vs how many called to report that they disliked it.
I think an area ripe for this kind of quantitization is medical data, which currently languishes in doctors’ files, much of it on paper, protected by patient privacy laws. But people post a good deal of information about their medical conditions online, seeking help from other people who’ve dealt with the same diseases. Currently, there are a lot of diseases (take depression) where treatment is very hit-or-miss, and doctors basically have to try a bunch of drugs in a row until they find one that works. A program that could trawl through forum posts and assemble data on patients and medical treatments that worked or failed could help doctors refine treatment for various difficult conditions–“Oh, you look like the kind of patient who would respond well to melatonin,” or “Oh, you have the characteristics that make you a good candidate for Prozac.”
The author points out that most companies will not be able to keep the massive quantities of data they are amassing. A hospital, for example, collects a great deal of data about patient’s heart rates and blood oxygen levels every day. While it might be interesting to look back at 10 years worth of patient heart rate data, hospitals can’t really afford to invest in databanks to store all of this information. Rather, what companies need is real-time or continuous data processing that analyzes current data and makes predictions/recommendations for what the company (or doctor) should do now.
For example, one of the books (I believe it was “Big Data”) discussed a study of premature babies which found, counter-intuitively, that they were most likely to have emergencies soon after a lull in which they had seemed to be doing rather well–stable heart rate, good breathing, etc. Knowing this, a hospital could have a computer monitoring all of its premature babies and automatically updating their status (“stable” “improving” “critical” “likely to have a big problem in six hours”) and notifying doctors of potential problems.
The book goes into a fair amount of detail about how to implement “big data solutions” at your office (you may have to hire someone who knows how to code and may even have to tolerate their idiosyncrasies,) which platforms are useful for data, the fact that “big data” is not all that different from standard analytics that most companies already run, etc. Once you’ve got the data pumping, actual humans may not need to be involved with it very often–for example you may have a system that automatically updates drives’ routes with traffic reports, or sprinklers that automatically turn on when the ground gets too dry.
It is easy to see how “big data” will become yet another facet of the algorithmization of work.
Overall, Big Data at Work is a good book, especially if you run a company, but not as amusing if you are just a lay reader. If you want something fun, read the first two.
Welcome to our final discussion of Auerswald’s The Code Economy. Today we will be finishing the text, chapters 13-15. Please feel free to jump in even if you haven’t read the book.
After a hopefully entertaining digression about Peruvian Poutine and Netflix’s algorithms*, we progress to the discussion of Bitcoin and the Blockchain. Now, I don’t know anything about Bitcoin other than the vague ideas I have picked up by virtue of being a person on the internet, but it was an interesting discussion nonetheless.
Auerswald likens blockchain to an old-fashioned accountant’s ledger; the “blocks” are the rectangles in which a business’s earnings and expenses are recorded. If there is any question about a company’s profits, you can look back at the information recorded in the chain of blocks.
The problem with this system is that there is only one ledger. If the accountant has made a mistake (or worse, a theft,) there is nothing else to compare it to in order to determine the mistake.
In the modern, distributed version, there are many copies of the blockchain. If on most of these copies of the chain, block 22 says -$400, and on one copy it says +$400, we conclude that the one that disagrees is most likely in error. Like the works of Shakespeare, there are so many copies out there that a discrepancy a single copy cannot be claimed to be authoritative; it is the collective body of work that matters.
“Blockchain” is probably going to get used here as a metaphor for “distributed systems of confirming authority” a lot. For example, “Democracy is a blockchain for deciding who gets to rule a country.” Or “science is a blockchain.”
In Rhodes’s “The Making of the Atomic Bomb,” he recounts the process by which something becomes accepted as “true” (or reasonably likely to be true,) in the scientific community. Let’s suppose scientist M is the foremost authority in his field–perhaps organic LEDs. Scientists L and N are doing work that overlaps M’s, and can therefore basically evaluate M’s work and vouch for whether they think it is sound or not. Scientists J, K, O, and P do work that overlaps a lot with L and N and a little with M; they can evaluate M’s work a little and vouch for whether they think L and N are trustworthy. The chain continues down to little cats scientists A and Z, who can’t really evaluate scientist M, but can tell you whether or not they think B and Y’s results are trustworthy.
This community of science has both good and bad. In general, the structure of science has been extremely successful at inventing things like computers, atomic bombs, and penicillin; at times it creates resistance to new ideas just because they are so far outside of the mainstream of what other scientists are doing. For example, Ignaz Semmelweis, a physician, discovered that he could reduce maternal deaths at his hospital from around 10-18% to 2% simply by insisting that obstetricians wash their hands between dissecting cadavers and delivering babies. Unfortunately, the rest of the medical establishment had not yet accepted the Germ Theory of disease and believed that disease was caused by imbalanced humors. Semmelweis’s idea that invisible corpse particles were somehow transferring corpse-ness from dead people to live people seemed absurd, and further, blamed the doctors themselves for the deaths of their patients. Semmelweis’s tragic tail ends with him being stomped to death in an insane asylum. (His mental ill-health was probably induced by a combination of the stress of being rejected by his profession; and syphilis, contracted via charity work delivering babies for destitute prostitutes.)
Luckily for mothers everywhere, medical science eventually caught up with Semmelweis and puerperal fever is no longer a major concern for laboring women. Science, it seems, can correct itself. (We may want to be cautious about being too eager to reject new ideas–especially in cases where there is clearly a lot of room for improvement, like an 18% death rate.)
Niti Aayog is working with Apollo Hospitals and information technology major Oracle on applying blockchain (decentralised) technology in pharmaceutical supply chain management to detect spurious drugs, Chief Executive Officer of NITI Aayog Amitabh Kant said here today.
Addressing a gathering through video-conferencing at the inaugural session of International Blockchain Congress 2018 for which Niti Aayog was a co-host, Kant said the organisation was working on applying the blockchain technology to pressing problems of the country in areas such as land registry, health records and fertiliser subsidy distribution m among others.
Blockchain technology can enable India to find solutions to huge logjams in courts …
With two-thirds of all civil cases pertaining to registration of property or land, the country’s policy think-tank is working with judiciary to find disruptive ways to expedite registrations, mutations and enable a system of smart transactions that is free of corruption and middlemen.
… There are three crore cases currently pending in Indian courts, including 42.5 lakh cases in high courts and 2.6 crore* cases in lower courts. Even if 100 cases are disposed off every hour without sleeping and eating, it would take more than 35 years to catch up, he said. …
On transforming the land registry system using blockchain, Niti Aayog is in advanced stage of implementing proof of concept pilot in Chandigarh to assess its potential to solve the problem of India’s land-based registry system. …
“It’s powerful because it allows multiple parties to collaborate and come to consensus without any need of third party,” he said.
*A crore is an Indian unit equivalent to 10 million.
I probably do not need to review Auerswald’s summary of Bitcoin’s history, as you are probably already well aware of it, but the question of “is Bitcoin real money?” is interesting. In 1875, Jevons, “cofounder of the neoclassical school in economics,” wrote that a material used as money should have the following traits:
“1 Utility and value
6 Stability of value
I am not sure about all of the items on this list; cigarettes and ramen noodles, for example, are used as currency in prisons, even though they are very easy to destroy. It seems like using a currency that you are going to eat would be problematic, yet the pattern recurs over and over in prisons (where perhaps people cannot get their hands on non-consumable goods, or perhaps people simply have no desire for non-consumable ornaments like gold.)
Gold–the “gold standard” of currencies–is a big odd to me, because it has very few practical uses. You can’t eat it. You can’t plant with it, cure parasites with it, or build with it. Lots of people talk about how you’d want a hard currency like gold in the case of societal collapse in which people stop accepting fiat currency, but if zombies were invading, the gas stations had run out of gasoline, and the grocery stores were out of food, I can’t imagine that I’d trade what few precious commodities I had left for a pile of rocks.
People argue that fiat currency is “just paper,” but gold is “just rocks,” and unless you’re a jeweler, the value of either is dependent entirely on your expectation that other people will accept them as currency.
For the past 40 years the world’s currencies have been untethered from gold or any other metal. National “fiat” currencies are nothing more or less than tradeable trust, whose function as currency is based entirely on government-enforced scarcity an verifiability not tethered to its intrinsic usefulness.
I think Auerswald overlooks the role of force in backing fiat currencies. We don’t use Federal Reserve Notes because we trust the government like it’s our best friend from the army who pulled us out of a burning foxhole that one time. We use Federal Reserve Notes because the US government has a lot of guns and bombs to back up its claim that this is real money.
Which means the power of a dollar is dependent on the US government’s ability to enforce that value.
As for Bitcoin:
Bitcoin… satisfies all the criteria for being “money” that William Stanley Jevon set forth… with on exception intrinsic utility and value. That does not mean that Bitcoin will grow in significance as a means of exchange, much less achieve any position of dominance. But with digital transactions via mobile phones–Apple Pay and the like–becoming ever more command the concept of a digital currency not backed by any government gaining rapid acceptance, the prospect of one or another digital currency competing successfully with fiat currencies is not nearly as far-fetched today as it was even three years ago.
The biggest problems I see for digital currencies:
Keeping value–if people decide they won’t accept DogeCoin, then what do you do with all of your DogeCoins?
Ease of entry into the market makes it difficult for any one Coin to retain value
Most people are happy using currencies not associated with illegal activity
The upside to digital currencies is they may be a real blessing for people caught in countries where local fiat currencies are being manipulated all to hell.
Anyway, Auerswald envisions a world in which blockchains (with coins or not) enable a world of peer-to-peer authentication and transactions:
By their very structure, Peer-to-peer platforms start out being distributed. The challenge is how to organize all of the energy contained in such networks so that people are rewarded fairly for their contributions. …Blockchain-based systems for governing peer0to0eer networks hold the promise–so far unrealized–of incorporating the best features of markets when it comes to rewarding contribution and of organizations when it comes to keeping track of reputations.
In other words, in areas where economies are held back because the local governments do a bad job of enforcing contracts and securing property rights, “blockchain”-like algorithms may be able to step into the gap and provided an accepted, distributed, alternative system of enforcement and authentication.
(This is the point where I start ranting to anyone within earshot about communists not recognizing the necessity of secure property rights so that people can turn their property into capital in order to start businesses. Without that seed money to start a business, you can’t get started. Even something simple, like driving for Uber, requires a car to start with, and cars cost money. If you can’t depend on having money tomorrow because all of your property just got confiscated, or you can’t depend on having a car tomorrow because private property is for bourgeois scum, then you can’t get a job driving for Uber. If no one can convert property to capital and thus to businesses, then you don’t get business and you have no economy and people suffer.
Communists see that some people have property that they can convert to capital and other people don’t have said capital, and their solution is to just take everybody’s stuff away and declare the problem fixed, when what they really want is for everyone to have enough basic property and capital to be able to start their own business.]
But back to Auerswald:
Earlier… I alluded to the significant advance in democracy, science, and financial systems that occurred simultaneously during …the Age of Enlightenment. That systems of governance, inquiry, and economics should have advanced all at the same time… is no coincidence at all. Each of these foundational developments in human social evolution is, at its core, an algorithm for authentication and verification. …
It is only because of the disciplinary fragmentation of inquiry that has occurred in the past century that we do not immediately perceive in the evolved historical record the patterns connecting systems of authentication and verification in politics, science, and economics as they have jointly evolved. … Illuminating those patterns has been the point of this book.
Chapter 14 begins with a history of Burning Man, which the author defends thus:
Still, it makes for an interesting case study in the building of cities (and why laws get enacted): Like everything about Black Rock City, the layout is the product of both planning and evolution. Cities are what physicists refer to as dissipative structures: highly complex organisms worse existence depend on a constant throughput of energy. If you were to close down all bridges and tunnels into New your City … grocery stores would have only a three-day supply of food. The same is generally true of a city’s other energy requirements. All cities are temporary, and they survive only because we feed them. …
The evolution of Black Rock is for urbanists what a real-life Jurassic Park would be for a Paleontologist. We really have no idea what the experience of living in humanity’s first cities might have been–whether Uruk in Mesopotamia or Catalhoyuk in Anatolia. And yet all cities also have elements of planning. Where Black Rock City has its Larry Harvey, London had its Robert Hooke and Washington, D.C., had its Pierre L’Enfant. Each had a notion of how to bound a space, build symmetry and flow, and in so doing provide a platform where the human experience can unfold.
I have a somewhat dim view of “Burning Man” as a communist utopia that’s only open to rich people, filled with environmentalist hippies leaving an enormous carbon footprint in order to get high with a close-knit community of 70,000 other people, but maybe my sight is obscured from the outside.
The question remains, though: will code be a blessing, or a curse? What happens to employment as “traditional” jobs disappear? Will blockchain and other new platforms and technologies make us freer, or simply find new ways to control us?
The advance of code reduces individual power and autonomy while it increases individual capabilities and freedom.
So far, Auerswald points out, there has been good reason to be optimistic:
In 1990, a staggeringly high 43 percent of people in the “developing world,” approximately 1.9 billion people, lived in extreme poverty. By 2010, that number had fallen to 21 percent. …
For the past two centuries, the vehicle for that progress has been the continual capacity of economies to generate more and better jobs. … “Gallup has discovered that having a good job is now the great global dream … ‘A good job’ is now more important than having a family, more compelling than democracy and freedom, religion, peace and so on… Stimulating job growth is the new currency of all leaders because if you don’t deliver on it you will experience instability, brain drain, sometimes revolution…
There is something concerning about this, though. “Jobs creation” is now widely agreed to be in the hands of national leaders, not individuals. Ordinary people are no longer seen as drivers of innovation. People can start businesses, of course, but whether those businesses survive or fail depends on the government; for the average person, jobs are no longer created by human ingenuity but awarded by an opaque power structure.
Thus the liberal claim that “structural racism” (rather than “individual racism”) is the real cause of continued black impoverishment and high unemployment rates. In a world where employment is granted or withheld by the powerful based on whether or not they like you, not based on your own innate ability to make your own economic contribution to the world, then it is imperative to make sure that the powerful see it as important to employ people like you.
It is, in sum, an admission of the powerlessness of the individual.
Still, Auerswald is hopeful that with the rise of the Peer-to-Peer economy and end of traditional factory work, not only will work be more interesting (as boring, repetitive jobs are most easily automated,) but also that people will no longer be dependent on the whims of a small set of powerful people for access to jobs.
I think he underestimates how useful it is to have steady, long-term employment and how difficult it is for individuals to compete against established corporations that have much larger economies of scale and access to far more relevant data than they do. Take, for example, YouTube vs. Netflix. Netflix can use its troves of data to determine which kinds of shows customers would like to watch more of, then hire people to make those shows. This is pretty nice work if you can get it. YouTube, of course, just lets pretty much anyone put up any video they want, and most of the videos are probably pretty dull, but a few YouTubers put up quality material and an even smaller few actually make a decent amount of money. YouTuber PewDiePie, for example, holds the record at 61+ million subscribers, which has earned him $124 million. But most people who try to become YouTube stars do not become PewDiePie; most earn very little. And why should they, when most of them are amateurs low-budget amateurs with no data on what audiences are interested in going up against other TV options like Orange is the New Black, Breaking Bad, and yes, PewDs himself?
I have a friend who is a very talented amateur clothing designer and dressmaker. I have encouraged her to open a shop on Etsy and try sell some of her creations, but can she really compete with Walmart, The Gap, or Nordstrom? Big Clothing has a massive lead in terms of factories mass-producing clothes for sale. (Her only hope would be to extremely upscale–wedding dresses, movie costumes, etc.)
So what does the future hold?
In the next round of digital disruption, tasks that can be automated (the “high-volume, low-price” option resulting from ongoing code-driven bifurcations…) will yield only small dividends for most people. The exception is the relatively small number of people who will maintain the platforms on which such tasks are performed…
The promising pathway for inclusive well-being is humanized work (the “low-volume, high-price” pathway resulting from ongoing code-driven bifurcations…) this pathway includes everything about value creation that is differentiated, personal, and human.
In his Conclusion, Auerswald writes:
To be human is to think critically. To collaborate, to Communicate. To be creative. What we call “the economy’ is one extension of these activities. It is the domain in which we develop and advance code.
But the singularity approaches:
We are not at the center of our cognitive universe. Our own creations are eclipsing us.
For each of us, redefining work requires nothing less than redefining identity. This is because production is not something human beings do just to consume. In fact, the opposite is true. We are living beings. We consume in order to produce.
Well, that’s the end of the book. I hope you have enjoyed it as much as I have. What do you think the future holds? Where do you think code is taking the economy? What are the best–and worst–opportunities for growth? And what (if anything) should we read next?
*An Aside On Netflix and the use of algorithms to produce movies/TV:
…consider the fate of two films that premiered the same night at the 2015 Sundance Film Festival. … One of these films, What Happened, Miss Simone? was a documentary about singer and civil rights icon Nina Simone. That film was funded by Netflix, whose corporate decision to back the film was based in part on insights algorithmically gleaned from the vast trove of data it has collected on users of its streaming video and movie rental services. The second film was a comedy titled The Bronze, which featured television star Melissa Rauch as a vulgar gymnast. The Bronze was produced by Duplass Brothers production and privately financed by “a few wealthy individual” whose decision to back the film was presumably not based on complex impersonal algorithms but rather, as has been the Hollywood norm, on business intuition.
I’ve often wondered why so many terrible movies get made.
A documentary about a Civil Rights leader might not be everyone’s cup of tea (people like to say they watch intellectual movies more than they actually do,) but plenty of people will at least abstractly like it. By contrast, a “vulgar gymnast” is not an interesting premise for a movie. Vulgarity can be funny when it is contrasted with something typically not vulgar–eg, “A vulgar mobster and a pious nun team up to save an orphanage,” or even “A vulgar nun and pious mobster…” The humor lies in the contrast between purity and vulgarity. But gymnasts aren’t known for being particularly pure or vulgar–they’re neutral–so there’s no contrast in this scenario. A vulgar gymnast doesn’t sound funny, it sounds rude and unpleasant. And this is the one sentences summary chosen to represent the movie? Not a good sign.
As you might have guessed already, What Happened, Miss Simone, did very well, and The Bronze was a bomb. It has terrible reviews on IMDB and Rotten Tomatoes. As folks have put it, it’s just not funny.
Davidowitz notes in Everybody Lies that the industries most ripe for “big data”fication are the ones where the current data is not very good. Industries where people work more on intuition than analysis. For example, the choice of horses in horse racing, until recently, was based on pedigree and intuition–what experienced horse people thought seemed promising in a foal. There was a lot of room in horse racing for quantification and analysis–and the guy who started using mobile x-ray machines to measure horse’s heart and lung sizes was able to make significantly better predictions than people who just looked at the horses’s outsides. By contrast, hedge funds have already put significant effort into quantifying what the prices of different stocks are going to do, and so it is very hard to do better data analysis than they already are.
The selection of movies and TV pilots to fund fall more into the “racing horses picked by intuition” category than the “extremely quantified hedge funds” category, which means there’s lots of room for improvement for anyone who can get good data on the subject.
Incidentally, “In 2015… Netflix accounted for almost 37 percent of all downstream internet traffic in North America during peak evening hours.”
Welcome back to EvX’s Book Club. Today we are discussing ch. 12 of Auerswald’s The Code Economy: Equity: Progress and Poverty.
We have discussed before the Georgist notion that the increase in poverty that accompanies progress (or development) is due to skyrocketing rents in urban (that is, productive) areas, which lead to rentiers capturing an increasing percent of the wealth created by development.
Indeed, as has been noted elsewhere and in Auerswald’s discussion of Piketty’s Capital in the Twenty-First Century:
The much discussed increase in inequality since the 1970s that Piketty documents is primarily about one thing: the increasing value of real estate, an asset that is disproportionately held by the wealthy.
Auerswald has an interesting discussion of Total Factor Productivity (TFP) that I’d like to pause to discuss:
The calculation of TFP requires measures of aggregate output, capital, and labor. The measurement of each of these is inherently difficult.
Auerswald argues that TFP is particularly bad at measuring the value added by the internet. Quoting economics blogger Justin Fox:
Forty years ago the cost to copy [an S&P 500 firm] as about 5/6 of the total stock price of that firm. So 1/6 of that stock price represented the value of things you couldn’t easily copy, like patents, customer goodwill, employee goodwill, regulator favoritism, and hard to see features of company methods and culture. Today it costs only 1/6 of the stock price to copy all of a firm’ visible items and features that you can legally copy. So today the other 5/6 of the stock price represents the value of all those things you can’t copy.
(Or these companies are massively over-valued.)
In other words, if you owned a textile mill, the value of the company would be based on the value of the physical objects inside your mill. A mill with ten state of the art looms could produce twice as much cloth as a mill with only 5 looms. A mill with 100 looms would produce 10 times as much cloth. The comany’s value and its physical capital would be directly linked.
By contrast, if you suddenly became the sole owner of Twitter, your physical capital and the company’s value would hardly be related. What is Twitter’s physical capital? A bunch of computers in a building somewhere? An entrepreneur could not create a company with twice Twitter’s value by simply buying twice as many computers and putting them in twice as many buildings.
Whatever Twitter’s value may be, very little of it lies in physical equipment. Very little of it lies in buildings or land. Much of it, though, lies in digital land. Just as landlords derive their wealth from the benefits people derive from being near other, economically productive people, so Twitter’s value lies in the desire of people to be near other people in digital spaces:
Economic geography has taught us that the “best localities” will be the place where the returns to density are greatest… Land in “the best localities” increases in value because cities offer people tangible economic returns that derive from density and interconnection.
Please discuss the implications for
1. Third world mega-cities like Karachi or Lagos.
2. Immigration from third world to first world.
3. Digital real estate, like Twitter.
About the digital economy Second Life, Auerswald writes:
Second Life had nearly seven million registered users… Second Life sustained an economy consisting of the production and exchange of virtual goods and service’ it had a GDP equivalent to $500 million, benchmarked by $6 million per month of monetized trade with the real world.
I have been thinking about in-game economies for years, ever since discovering that many online games have their own currencies, which may or may not be legally tradeable for US dollars. But I had not, until this moment, thought of these games as actually modellable like real countries, with economies, exchange rates, and trade with the outside world.
The virtual and real worlds of entrepreneurship and work are converging in similar ways… “As soon as tens of hundreds of U.S. dollars were sufficient to start a business in Second Life, thousands of people began to tr. Compare this to the real world, where a primary source of funding for small businesses is a second mortgage.” …
Seven years later… The Economist published an article about entrepreneurial startups in the US titled “The Cambrian Explosion,” … This article described how an array of new platforms had dramatically lowered the cost of launching and growing a real-world business: “One explanation for the Cambrian explosion of 540m years ago is that the that time the basic building blocks of life had just been perfected, allowing more complex organisms to be assembled more rapidly. Similarly,t he basic building blocks for digital services and products… have become so evolved, cheap and ubiquitous that they can be easily combined and recombined.”
Auerswald then moves on to the matter of “big data,” which is a big part of how companies like Twitter and Linked In hope to actually make any profits. As I’ve mentioned, I’ve taken a side-tour into “Big Data” that I think was a useful complement to this book; Big Data is the best of what I’ve read so far, nothing has stood out as whiz-bang fabulous. The relevant summary version is that companies like LinkedIn and Facebook are really about the data they gather, rather than the fun you have looking at memes your grandmother reposted. That data, in turn, will probably have a variety of economic uses–though maybe not to you:
And yet, while a large number of people contribute to the value Big Data creates,a relatively small number captures most of the gains. Why is that?
Just as the rentier class gathers most of the benefits from living in a valuable city in close proximity to the engines of human productivity, so do the owners of digital platforms, like Facebook, benefit from the creation of data wealth by their millions of digital citizens.
Digital platforms are the new land; will they also be the new Monopoly?
Auerswald then makes a very interesting observation:
Physical land is yours if, and only if, you have both the right and the practical capacity to prevent other people from accessing it. The same is true of digital land. … That capacity for exclusion–the source of all monetized value derived from digital exchange–depends on the existence of reliable protocols for authentication and verification. … “Open leads to value creation… To capture value you have to find something to close.”
This is so important, I’m tempted to repeat it a few times. Exclusion is the source of all monetized value.
The “brand” (ie, Nike, Apple, Harley Davidson, Harvard,) is modern society’s solution to authentication and verification in modern, anonymous markets. Our ancestors, who engaged primarily in face-to-face transactions with people they knew from their own villages, had no need of brands. They didn’t worry whether they were being tricked into buying knockoff-brand potatoes from farmer Joe; they just bought potatoes.
In the modern economy, it makes a difference whether you get a real Apple computer or a knockoff with an apple sticker slapped on. It matters whether you get real Acetaminophen or a mysterious pill that may or may not contain morphine. It matters whether you buy a brand new Ford or a car cobbled together from the corpses of three totaled station wagons with a new coat of paint.
This, Auerswald argues, is why the government imposes such stiff penalties on people who violate trademarks–violation of the Trademark Counterfeiting act of 1984 can incur a fine of 5 million dollars or 20 years imprisonment.
Yet just as the advance of code has created brands, code is now in the process of undoing them. How? By converting trust directly into code–into algorithmic system for verification and authentication.
Basically, he thinks we’re going to blockchain and Yelp our way into a peer-to-peer economy where people’s online ratings serve as an effective substitute for brands–a world in which angry twitter mobs can crash one’s entire career by giving a bunch of one-star Yelp reviews.
Remember: everything else is downstream from territory.
That’s all for today. Bitcoin and the Blockchain are chapter 13.
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
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.
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…
“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.”
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.
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?
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.
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.
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.
Alternatively, higher taxes on fortunes like Zuckerberg’s and Bezos’s might accomplish the same thing.
“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.”
Big Data describes another war of attrition:
McNamara epitomized the hyper-rational executive who relied on numbers rather than sentiments, and who could apply his quantitative skills to any industry he turned them to. In 1960 he was named president of Ford, a position he held for only a few weeks before being tapped to join President Kennedy’s cabinet as secretary of defense.
As the Vietnam conflict escalated and the United States sent more troops, it became clear that this was a war of wills, not of territory. America’s strategy was to pound the Viet Cong to the negotiation table. The way to measure progress, therefore, was by the number of enemy killed. The body count was published daily in the newspapers. To the war’s supporters it was proof of progress; to critics, evidence of its immorality. The body count was the data point that defined an era.
McNamara relied on the figures, fetishized them. … McNamara felt he could comprehend what was happening on the ground only by staring at a spreadsheet—at all those orderly rows and columns, calculations and charts, whose mastery seemed to bring him one standard deviation closer to God.
In 1977, two years after the last helicopter lifted off the rooftop of the U.S. embassy in Saigon, a retired Army general, Douglas Kinnard, published a landmark survey called The War Managers that revealed the quagmire of quantification. A mere 2 percent of America’s generals considered the body count a valid way to measure progress. “A fake—totally worthless,” wrote one general in his comments. “Often blatant lies,” wrote another. “They were grossly exaggerated by many units primarily because of the incredible interest shown by people like McNamara,” said a third. — Viktor Mayer-Schönberger and Kenneth Cukier, Big Data
Humans are reasonably smart creatures, but we so easily get stuck in terrible modes of thinking.
On a battlefield men die quickly, they fight back, they are sustained by fellowship and a sense of duty. Here I saw people dying in solitude by slow degrees, dying hideously, without the excuse of sacrifice for a cause. They had been trapped and left to starve, each in his home, by a political decision made in a far-off capital around conference and banquet tables. […] The most terrifying sights were the little children with skeleton limbs dangling from balloon – like abdomens. Starvation had wiped every trace of youth from their faces, turning them into tortured gargoyles; only in their eyes still lingered the reminder of childhood. Everywhere we found men and women lying prone, their faces and bellies bloated, their eyes utterly expressionless. Anger lashed my mind as I drove back to the village. Butter being sent abroad in the midst of the famine! In London, Berlin, Paris I could see with my mind’s eye people eating butter stamped with a Soviet trademark. “They must be rich to be able to send out butter,” I could hear them saying. “Here, friends, is the proof of socialism in action.” Driving through the fields, I did not hear the lovely Ukrainian songs so dear to my heart. These people had forgotten how to sing. I could hear only the groans of the dying, and the lip-smacking of fat foreigners enjoying our butter… — Kravchenko, Victor. I Chose Freedom: The Personal And Political Life Of A Soviet Official
Like human sacrifice and cannibalism:
The word tzompantli is Nahuatl and was used by the Aztecs to refer to the skull-racks found in many Aztec cities; The first and most prominent example is the Huey Tzompantli (Great Skull-rack) located the Aztec capital of Tenochtitlan and described by the early conquistadors. … Excavations at Templo Mayor in the Aztec capital Tenochtitlan have revealed many skulls belonging to women and children, in addition to those of men, a demonstration of the diversity of the human sacrifices in Aztec culture. After displaying severed heads, many scholars have determined that limbs of Aztec victims would be cannibalized
… based on numbers given by Taipa and Fray Diego Durán, Bernard Ortiz de Montellano has calculated that there were at most 60,000 skulls on the “Hueyi Tzompantli” (Great Skullrack) of Tenochtitlan. … There were at least five more skull racks in Tenochtitlan but by all accounts they were much smaller. —Wikipedia
All of the individual parts of a system can seem logical, and yet the end result can still be grotesque, inhuman, and insane.
I am on holiday so your normal Book Club post will resume next Wednesday.
“DNA builds products with a purpose. So do people.” –Auerswald, The Code Economy
McDonald’s is the world’s largest restaurant chain by revenue, serving over 69 million customers daily in over 100 countries across approximately 36,900 outlets as of 2016. … According to a BBC report published in 2012, McDonald’s is the world’s second-largest private employer (behind Walmart with 1.9 million employees), 1.5 million of whom work for franchises. …
There are currently a total of 5,669 company-owned locations and 31,230 franchised locations… Notably, McDonald’s has increased shareholder dividends for 25 consecutive years, making it one of the S&P 500 Dividend Aristocrats. …
According to Fast Food Nation by Eric Schlosser (2001), nearly one in eight workers in the U.S. have at some time been employed by McDonald’s. … Fast Food Nation also states that McDonald’s is the largest private operator of playgrounds in the U.S., as well as the single largest purchaser of beef, pork, potatoes, and apples. (Wikipedia)
How did a restaurant whose only decent products are french fries and milkshakes come to dominate the global corporate landscape?
In The Code Economy, Auerswald suggests that the secret to McDonald’s success isn’t (just) the french fries and milkshake machines:
Kroc opened his first McDonald’s restaurant in 1955 in Des Plaines, California. Within five years he had opened two hundred new franchises across the country. [!!!] He pushed his operators obsessively to adhere to a system that reinforced the company motto: “Quality, service, cleanliness, and value.”
Quoting Kroc’s1987 autobiography,
“It’s all interrelated–our development of the restaurant, the training, the marketing advice, the product development, the research that has gone into each element of the equipment package. Together with our national advertising and continuing supervisory assistance, it forms an invaluable support system. Individual operators pay 11.5 percent of their gross to the corporation for all of this…”
The process of operating a McDonald’s franchise was engineered to be as cognitively undemanding as possible. …
Kroc created a program that could be broken into subroutines…. Acting like the DNA of the organization, the manual allowed the Speedee Service System to function in a variety of environments without losing essential structure or function.
McDonald’s is big because it figured out how to reproduce.
I’m not sure why IKEA is so big (I don’t think it’s a franchise like McDonald’s,) but based on the information posted on their walls, it’s because of their approach to furniture design. First, think of a problem, eg, People Need Tables. Second, determine a price–IKEA makes some very cheap items and some pricier items, to suit different customers’ needs. Third, use Standard IKEA Wooden Pieces to design a nice-looking table. Fourth, draw the assembly instructions, so that anyone, anywhere, can assemble the furniture themselves–no translation needed.
IKEA furniture is kind of like Legos, in that much of it is made of very similar pieces of wood assembled in different ways. The wooden boards in my table aren’t that different in size and shape from the ones in my dresser nor the ones in my bookshelf, though the items themselves have pretty different dimensions. So on the production side, IKEA lowers costs by producing not actual furniture, but collections of boards. Boards are easy to make–sawmills produce tons of them.
Furniture is heavy, but mostly empty space. By contrast, piles of boards stack very neatly and compactly, saving space both in shipping and when buyers are loading the boxes into their cars. (I am certain that IKEA accounts for common car dimensions in designing and packing their furniture.)
And the assembly instruction allow the buyer to ultimately construct the furniture.
In other words, IKEA has hit upon a successful code that allows them to produce many different designs from a few basic boards and ship them efficiently–keeping costs low and allowing them to thrive.
The company is also looking for ways to maximize warehouse efficiency.
“We have (only) two pallet sizes,” Marston said, referring to the wooden platforms on which goods are placed. “Our warehouses are dimensioned and designed to hold these two pallet sizes. It’s all about efficiencies because that helps keep the price of innovation down.”
In Europe, some IKEA warehouses utilize robots to “pick the goods,” a term of art for grabbing products off very high shelves.
These factories, Marston said, are dark, since no lighting is needed for the robots, and run 24 hours a day, picking and moving goods around.
“You (can) stand on a catwalk,” she said, “and you look out at this huge warehouse with 12 pallets (stacked on top of each other) and this robot’s running back and forth running on electronic eyebeams.”
IKEA’s code and McDonald’s code are very different, but both let the companies produce the core items they sell quickly, cheaply, and efficiently.
The difficulty with evolution is that systems are complicated; successful mutations or even just combinations of existing genes must work synergistically with all of the other genes and systems already operating in the body. A mutation that increases IQ by tweaking neurons in a particular way might have the side effect of causing neurons outside the brain to malfunction horribly; a mutation that protects against sickle-cell anemia when you have one copy of it might just kill you itself if you have two copies.
Auerswald quotes Kauffman and Levin:
“Natural selection does not work as an engineer works… It works like a tinkereer–a tinkerer who does not know exactly what he is going to produce but uses… everything at his disposal to produce some kind of workable object.” This process is progressive, moving form simpler to more complex forms: “Evolution doe not produce novelties from scratch. It works on what already exists, either transforming a system to give it new functions or combining several systems to produce a more elaborate one [as] during the passage from unicellular to multicellular forms.”
The Kauffman and Levin model was as simple as it was powerful. Imagine a genetic code of length N, where each gene might occupy one of two possible “states”–for example, “o” and “i” in a binary computer. The difficulty of the evolutionary problem was tunable with the parameter K, which represented the average number of interactions among genes. The NK model, as it came to be called, was able to reproduce a number of measurable features of evolution in biological systems. Evolution could be represented as a genetic walk on a fitness landscape, in which increasing complexity was now a central parameter.
Local optima–or optimums, if you prefer–are an illusion created by distance. A man standing on the hilltop at (approximately) X=2 may see land sloping downward all around himself and think that he is at the highest point on the graph. But hand him a telescope, and he discovers that the fellow standing on the hilltop at X=4 is even higher than he is. And hand the fellow at X=4 a telescope, and he’ll discover that X=6 is even higher.
A global optimum is the best possible way of doing something; a local optimum can look like a global optimum because all of the other, similar ways of doing the same thing are worse.
Some notable examples of cultures that were stuck at local optima but were able, with exposure, to jump suddenly to a higher optima: The “opening of Japan” in the late 1800s resulted in breakneck industrialization and rising standards of living; the Cherokee invented their own alphabet (technically a syllabary) after glimpsing the Roman one, and achieved mass literacy within decades; European mathematics and engineering really took off after the introduction of Hindu-Arabic numerals and the base-ten system.
If we consider each culture its own “landscape” in which people (and corporations) are finding locally optimal solutions to problems, then it becomes immediately obvious that we need both a large number of distinct cultures working out their own solutions to problems and occasional communication and feedback between those cultures so results can transfer. If there is only one, global, culture, then we only get one set of solutions–and they will probably be sub-optimal. If we have many cultures but they don’t interact, we’ll get tons of solutions, and many of them will be sub-optimal. But many cultures developing their own solutions and periodically interacting can develop many solutions and discard sub-optimal ones for better ones.
Life constantly makes us take decisions under conditions of uncertainty. We can’t simply compute every possible outcome, and decide with perfect accuracy what the path forward is. We have to use heuristics. Religion is seen as a record of heuristics that have worked in the past. …
But while every generation faces new circumstances, there are also some common problems that every living being is faced with: survival and reproduction, and these are the most important problems because everything else depends on them. Mess with these, and everything else becomes irrelevant.
This makes religion an evolutionary record of solutions which persisted long enough, by helping those who held them to persist.
This is not saying “All religions are perfect and good and we should follow them,” but it is suggesting, “Traditional religions (and cultures) have figured out ways to solve common problems and we should listen to their ideas.”
Back in The Code Economy, Auerswald asks:
Might the same model, derived from evolutionary biology, explain the evolution of technology?
… technology may also be nothing else but the capacity for invariant reproduction. However, in order for more complex forms of technology to be viable over time, technology also must possess a capacity for learning and adaptation.
Evolutionary theory as applied to the advance of code is the focus of the next chapter. Kauffman and Levin’s NK model ends up providing a framework for studying the creation and evolution of code. Learning curves act as the link between biology and economics.
Will the machines become sentient? Or McDonald’s? And which should we worry about?