This is a timelapse multiple exposure photo of an arctic day, apparently titled “Six Suns” (even though there are 8 in the picture?) With credit to Circosatabolarc for posting the photo on Twitter, where I saw it. Photo by taken by Donald MacMillan of the Crocker Land Expedition, 1913-1917.
Attempting to resolve the name-suns discrepancy, I searched for “Six Suns” and found this photo, also taken by Donald MacMillan, from The Peary-MacMillian Arctic Museum, which actually shows six suns.
I hearby dub this photo “Eight Suns.”
A reverse image search turned up one more similar photo, a postcard titled “Midnight Sun and Moon,”taken at Fort McMurray on the Arctic Coast, sometime before 1943.
As you can see, above the arctic circle, the sun’s arc lies so low relative to the horizon that it appears to move horizontally across the sky. If you extended the photograph into a time-lapse movie, taken at the North Pole, you’d see the sun spiral upward from the Spring Equinox until it reaches 23.5 degrees above the horizon–about a quarter of the way to the top–on the Summer Solstice, and then spiral back down until the Fall Equinox, when it slips below the horizon for the rest of the year.
I love this graph; it is a beautiful demonstration of the mathematics underlying bodily shape and design, not just for one class of animals, but for all of us. It is a rule that applies to all moving creatures, despite the fact that running, flying, and swimming are such different activities.
I assume similar scaling laws apply to mechanical and aggregate systems, as well.
The more human density grows, the more space per person shrinks, the more human behavior must contract to avoid conflict with one’s neighbors.
If your neighbor is racist against you, but lives 20 miles away over an unpaved road through the mountains, he is less of a problem in your daily life than if he shares a bathroom with you in a college dorm.
As we rub against our neighbors, each individual contracts to avoid giving offense. More forms of behavior, speech, and by extension, thought, are proscribed. To live in close company is to always be aware of the thoughts, feelings, and intentions of hundreds of others or suffer consequences.
As our personal worlds shrink, so do our professions. The doctor no longer makes his rounds, seeing all manner of coughs and colds, appendixes and broken bones. Instead he has a narrow specialty, chosen while still in school. One wing of a hospital, one floor. Pediatric or geriatric. The farmer no longer builds his house, slaughters his animals, preserves his food, shears his sheep, and weaves his own clothes.
Each job is split off, done over and over–and better–by a single person. The Jack of All trades is master of none and the Jills of One Highly Specialized Sub-Trade quickly put Jack out of business. And thus the worker is alienated from the product of his labor.
An anthill cannot function if the ants are fighting; the Queen will not tolerate the workers attacking each other.
Government desire not citizens’ safety, but taxes.
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.
“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.
1. The Industrial Revolution and its consequences have been a disaster for the human race. They have greatly increased the life-expectancy of those of us who live in “advanced” countries, but they have destabilized society, have made life unfulfilling, have subjected human beings to indignities, have led to widespread psychological suffering (in the Third World to physical suffering as well) and have inflicted severe damage on the natural world. The continued development of technology will worsen the situation. It will certainly subject human beings to greater indignities and inflict greater damage on the natural world, it will probably lead to greater social disruption and psychological suffering, and it may lead to increased physical suffering even in “advanced” countries. –Kaczynski, Industrial Society and Its Future
“The quest to find and keep a “job for life”–stable, predictable work that pays enough to live on, is reachable by available transportation, and lends a sense of meaning to their daily lives–runs though every interview transcript, from those who are unemployed to those who have “made it” to steady jobs like firefighting or nursing. Traditional blue-collar work–whether as a factory worker or a police officer–has become increasingly scarce and competitive, destroyed by a technologically advanced and global capitalism that prioritizes labor market “flexibility”… Consequently, the post-industrial generation is forced to continuously grapple with flux and contingency, bending and adapting to the demands of they labor market until they feel that they are about to break.“ –Silva, Coming up Short: Working-Class Adulthood in an Age of Uncertainty
The historical record confirms that the realities of the ongoing processes of mechanization and industrialization, as noted early on by Lord Byron, were very different from the picture adherents to the wage fund theory held in their heads. While the long-term impact of the Industrial revolution had on the health and well-being of the English population was strongly positive, the first half of the nineteenth century was indeed a time of exceptional hardship for English workers. In a study covering the years 1770-1815, Stephen Nicholas and Richard Steckel report “falling heights of urban-and rural-born males after 1780 and a delayed growth spurt for 13- to 23-year old boys,” as well as a fall in the English workers’ height relative to that of Irish convicts. By the 1830s, the life expectancy of anyone born in Liverpool and Manchester was less than 30 years–as low as had been experienced in England since the Black Death of 1348. –Auerswald, The Code Economy
On the other hand:
Chapter 5 of The Code Economy,Substitution, explores the development of economic theories about the effects of industrialization and general attempts at improving the lives of the working poor.
… John Barton, a Quaker, published a pamphlet in 1817 titled, Observations on the Circumstances Which Influence the Condition of the Laboring Classes of Society. … Barton began by targeting the Malthusian assumption that population grows in response to increasing wages. … He began by noting that there was no a priori reason to believe that labor and capital were perfect complements, as classical economists implicitly assumed. The more sensible assumption was that, as wages increased, manufacturers and farmers alike would tend to substitute animals or machines for human labor. Rather than increasing the birth rate, the higher wages brought on by the introduction of new machinery would increase intergenerational differences in income and thus delay child-bearing. Contrary to the Malthusian line of argument, this is exactly what happened.
There’s an end note that expands on this (you do read the end notes, right?) Quoting Barton, 1817:
A rise of wages then does not always increase population… For every rise of wages tends to decrease the effectual demand for labor… Suppose that by a general agreement among farmers the rate of agricultural wage were raised from 12 shillings to 24 shillings per week–I cannot imagine any circumstance calculated more effectually to discourage marriage. For it would immediately become a a most important object to cultivate with as few hands as possible; wherever the use of machinery, or employment of horses could be substituted for manual labor, it would be done; and a considerable portion of existing laborers would be out of work.
This is the “raising the minimum wage will put people out of work” theory. Barton also points out that when people do manage to get these higher-paid jobs, they will tend to be older, more experienced laborers rather than young folks looking to marry and start a family.
A quick perusal of minimum wage vs. unemployment rate graphs reveals some that are good evidence against minimum wages, and some that are good evidence in favor of them. Here’s a link to a study that found no effects of minimum wage differences on employment. The American minimum wage data is confounded by things like “DC is a shithole.” DC has the highest minimum wage in the country and the highest unemployment rate, but Hawaii also has a very high minimum wage and the lowest unemployment rate. In general, local minimum wages probably reflect local cost of living/cost of living reflects wages. If we adjust for inflation, minimum wage in the US peaked around 1968 and was generally high throughout the 60s and 70s, but has fallen since then. Based on conversations with my parents, I gather the 60s and 70s were a good time to be a worker, when unskilled labor could pretty easily get a job and support a family; unemployment rates do not seem to have fallen markedly since then, despite lower real wages. A quick glance at a map of minimum wages by country reveals that countries with higher minimum wage tend to be nicer countries that people actually want to live in, but the relationship is not absolute.
We might say that this contradicts Barton, but why have American wages stagnated or gone down since the 60s?
2. Emergence of other economic competitors as Europe and Japan recovered from WWII
3. Related: Outsourcing to cheaper workers in China
4. Labor market growth due to entry of women, immigrants, and Boomers generally
Except for 2, that sounds a lot like what Barton said would happen. Wages go up => people move where the good jobs are => labor market expands => wages go down. If labor cannot move, then capitalists can either move the businesses to the labor or invest in machines to replace the labor.
On the other hand, the standard of living is clearly higher today than it was in 1900, even if wages, like molecules diffusing through the air, tend to even out over time. Why?
First, obviously, we learned to extract more energy from sources like oil, coal, and nuclei. A loom hooked up (via the electrical grid) to an electric turbine can make a lot more cloth per hour than a mere human working with shuttles and thread.
Second, we have gotten better at using the energy we extract–Auerswald would call this “code.”
Standards of living may thus have more to do with available resources (including energy) and our ability to use those resources (both the ‘code” we have developed and our own inherent ability to interact with and use that code,) than with the head-scratching entropy of minimum wages.
Auerswald discusses the evolution of David Ricardo’s economic ideas:
By incorporating the potential for substitution between capital and labor, Ricardo led the field of economics in rejecting the wage fund theory, along with its Dickensian implications for policy. He accepted the notion the introduction of new machinery would result in the displacement of workers. The upshot was that the workers were still assumed to be doomed, but the reason was now substitution of machines for labor, not scarcity of a Malthusian variety.
Enter Henry George, with a radically different perspective:
“Like a flash it came over me that there was the reason of advancing poverty with advancing wealth. With the growth of population, land grows in value, and the men who work it must pay more for the privilege.” …
George asserted that increasing population density, (not, as Malthus claimed, population decline) was the source of increased prosperity in human societies: “Wealth is greatest where population is densest… the production of wealth to a given amount of labor increases as population increases.” The frequent interactions among people in densely populated cities accelerates the emergence and evolution of code. However, while population growth and increased density naturally bring increased prosperity, they also, just as naturally, bring increasing inequality and poverty. Why? Because the fruits of labor are inevitably gathered by the owners of land.
In other words, increasing wages => increasing rents and the workers are right back where they started while the landlords are sitting pretty.
In sharp contrast with Karl Marx, … George stated that “the antagonism of interest is not between labor and capital… but is in reality between labor and capital on the one side and the land ownership on the other.” The implication of his analysis was as simple as it was powerful: to avoid concentrating wealth in the hands of the few, it was the government’s responsibility to eliminate all taxes on capital and laborers, the productive elements of the economy, and to replace those taxes with a single tax on land.
Note: not a flat tax on land, but a tax relative to the land’s sale value.
I was glad to see Henry George in the book because I enjoy George’s theories and they are under-discussed, especially relative to Marxism. You will find massive online communities of Marxists despite the absolute evidence that Marxism is a death machine, but relatively few enthusiastic Georgists. One of the things I rather appreciate about Georgism is its simplicity; the complication of the tax code is its own, additional burden on capitalists and workers alike. Almost any simplified tax code, no matter how “unfair,” would probably improve maters a great deal.
But there’s more, because this is a dense chapter. Auerswald notes that the increasing complexity of code (ie, productivity) has lead to steadily increasing standards of living over the past two centuries, at least after the Industrial Revolution’s initial cataclysm.
Quoting economist Paul Douglas, some years later:
“The increased use of mechanical appliances in offices has tended to lower the skill required. An old-fashioned bookkeeper, for instance, had to write a good hand, he had to be able to multiply and divide with absolute accuracy. Today his place is taken by a girl who operates a book-keeping machine, and it has taken her a few weeks at mot to become a skilled bookkeeper.” In other words, the introduction of machinery displaced skilled workers for the very same reason it enhanced human capabilities: it allowed a worker with relatively rudimentary training to perform tasks that previously required a skilled worker.
…”Another way of looking at it, is this: Where formerly the skill used in bookkeeping was exercised by the bookkeeper, today that skill is exercised by the factory employees who utilize it to manufacture a machine which can do the job of keeping books, when operated by someone of skill far below that of the former bookkeeper. And because of this transfer of skill form the office to the factory, the rewards of skill are likewise transferred to the wage-earner at the plant.”
This is a vitally important pint… The essence of this insight is that introducing more powerful machines into the workplace does more than simply encode into the machine the skills or capabilities that previously resided only in humans; it also shifts the burden of skill from one domain of work to another. … A comparable shift in recent decades has been from the skill of manufacturing computing machines (think IBM or Dell in their heydays) to that of creating improved instructions for computing machines’ the result has been a relative growth in programmers’ wages. The underlying process is the same. Improvements in technology will predictably reduce demand for the skills held by some workers, but they also will enhance the capabilities of other workers and shift the requirements of skill from one domain of work to the other.”
The problem with this is that the average person puts in 15-20 years of schooling (plus $$$) in order to become skilled at a job, only to suddenly have that job disappear due to accelerating technological change/improvement, and then some asshole one comes and tells them they should just “learn to code” spend another two to four years unemployed and paying for the privilege of learning another job and don’t see how fucking dispiriting this is to the already struggling.
The struggle for society is recognizing that even as standards of living may be generally rising, some people may absolutely be struggling with an economic system that offers much less certainty and stability than our ancestors enjoyed.
A final word from Auerswald:
… work divides or “bifurcates” as code advances in a predictable and repeatable way. The bifurcation of work in a critical mechanism by which the advance of code yields improvements in human well-being at the same time as it increases human reliance on code.
Writing, which is itself a form of code, enable humans to communicate code. Cities grow as code evolves. –Auerswald
Welcome to The Code Economy: A Forty-Thousand Year History, by Philip E. Auerswald. Chapter Two: Code looks at two epochal developments in human history: writing and cities.
One of the earliest pieces of writing we have uncovered is the Sumerian Hymn to Ninkasi, Goddess of Beer, which contains, yes, a recipe for making beer (translation by Miguel Civil):
Your father is Enki, Lord Nidimmud,
Your mother is Ninti, the queen of the sacred lake.
Ninkasi, your father is Enki, Lord Nidimmud,
Your mother is Ninti, the queen of the sacred lake.
You are the one who handles the dough [and] with a big shovel,
Mixing in a pit, the bappir with sweet aromatics,
Ninkasi, you are the one who handles the dough [and] with a big shovel,
Mixing in a pit, the bappir with [date] – honey,
You are the one who bakes the bappir in the big oven,
Puts in order the piles of hulled grains,
Ninkasi, you are the one who bakes the bappir in the big oven,
Puts in order the piles of hulled grains,
You are the one who waters the malt set on the ground,
The noble dogs keep away even the potentates,
Ninkasi, you are the one who waters the malt set on the ground,
The noble dogs keep away even the potentates,
You are the one who soaks the malt in a jar,
The waves rise, the waves fall.
Ninkasi, you are the one who soaks the malt in a jar,
The waves rise, the waves fall.
You are the one who spreads the cooked mash on large reed mats,
Ninkasi, you are the one who spreads the cooked mash on large reed mats,
You are the one who holds with both hands the great sweet wort,
Brewing [it] with honey [and] wine
(You the sweet wort to the vessel)
Ninkasi, (…)(You the sweet wort to the vessel)
The filtering vat, which makes a pleasant sound,
You place appropriately on a large collector vat.
Ninkasi, the filtering vat, which makes a pleasant sound,
You place appropriately on a large collector vat.
When you pour out the filtered beer of the collector vat,
It is [like] the onrush of Tigris and Euphrates.
Ninkasi, you are the one who pours out the filtered beer of the collector vat,
It is [like] the onrush of Tigris and Euphrates.
You guys requested beer or wine with your books, so here you go.
The hymn contains two layers of code–first, there is the code which allows each symbol or character to stand for a particular sound, which let the author write down the recipe and you, thousands of years later, decode and read the recipe; and second, there is the recipe itself, a code for producing beer.
The recipe’s code likely far predates the hymn itself, as humans had begun brewing beer at least a couple thousand years earlier.
Writing and cities go hand in hand; it is difficult to imagine managing the day-to-day need to import food (and water) for thousands of people without some ability to encode information. As cities grow larger, complexity grows: one man in the woods may relieve himself behind a tree; thousands of people packed into a square mile cannot.
Each solved problem, once routinized, becomes its own layer of code, building up as the city itself expands; a city of thousands or millions of people cannot solve each person’s problems anew each day.
But which came first, the city or the alphabet? Did the growth of cities spur innovations that improved agricultural output, or did agricultural innovations spur the growth of cities?
For example, settlement and construction appear to have gotten underway at Jericho (one of the world’s oldest inhabited cities) around 9 or 10,000 BC and at the mysterious Gobekli Tepe site began around 7-9,000 BC, before agriculture emerged in the region.
Writing developed a fair bit later, developing from clay shapes to shapes impressed in clay between 8,000 and 4,000 BC.
Others of the world’s earliest civilizations had either no or very little writing. The Norte Chico civilization of Peru, for example; by the time the Spaniards arrived, the Inca had an accounting system based on the quipu, a kind of string abacus, but appear to have not yet developed a true writing system, despite their palaces, cities, roads, emperor, and tax collectors. (Here is my previous post on Norte Chico.)
The extensive Indus Valley civilization had some form of symbolic encoding, but few of their inscriptions are longer than 4 or 5 characters–the longest inscription found so far is 26 symbols, spread over three different sides of an object. Not exactly an epic–but the Indus Valley Civilization was nevertheless quite large and impressive, supporting perhaps 5 million people. (Previous post on the Indus Valley.)
Auerswald documents some of the ways cities appear to drive innovation–and to “live”:
The Santa Fe team found that cities are like biological organisms when it comes to “metabolic” urban processes that are analogous to nutrient supply and waste removal–transportation, for example, ha a branching structure much like veins or bronchi–but that cities differ fundamentally from biological organisms when it comes to indicators reflecting the creation and transmission of code. measuring the size of cities based on population and on the urban “metabolism” using metrics such as wages, GDP, electric power and gasoline consumption, and total road surface, the team found a systematic relationship between city size and indicators of the supply of “nutrients” and waste removal… However, while metabolic indicators do not keep pace with the size of cities as they grow, indicators relating to the creation and transmission of code increase at a greater rate than city size. … In short, the creation of ideas accelerates with city growth, whereas the cost of new infrastructure is minimized.
This intriguing macro-level departure from the inverse relationships that hold for organisms ends up risking more questions about the evolution of cities than it answers: What mechanism enables larger cities to produce disproportionately more innovation and wealth than smaller cities?
An amalgam of terms that have been used for parallel conceptions of the Smart City among them cyberville, digital city, electronic communities, flexicity, information city, intelligent city, knowledge-based city, MESH city, telecity, teletopia, ubiquitous city, wired city.
However the one I would like to propose, with population movement in mind, is The Learning City.
The term is based on a combination of two theories The Ego City and The Flynn Effect.
In 2009 Neurobiologist Mark Changizi from the Rensselaer Polytechnic Institute released a paper entitled Ego City: Cities Are Organized Like Human Brain.
Changizi sees strikingly real similarities between the brain and a city.
The central idea being that they organise and evolve similarly due to the need for efficiency.
As brains grow more complex from one species to the next, they change in structure and organisation in order to achieve the right level of reciprocity.
This is analogous to the widening of streets in cities.
The research team found mutual “scaling laws” for brains and cities.
For example, as the surface area of a brain or city grows, the number of connectors (neurons or highways) increased at a similar rate for each.
Likewise, a bigger city needs more highway exits in the same proportion as a bigger brain needs more synapses connecting neurons.
“The brain is like a city.
Cities develop and grow bigger and may get problems with roads and infrastructure, which is similar to what happens to our brains when we get older”, notes Håkan Fischer, Professor of Biological Psychology at the Department of Psychology at Stockholm University.
The learning city
This is curious when taken in the context of The Flynn Effect.
Intelligence Researcher James Flynn found that every decade without fail the human population scored higher on IQ tests.
An average increase of 3 points per decade.
His thesis suggests that the more information we as humans have to absorb and compute leads to an increase in IQ.
In this instance the increased information is data collected within the city.
As cities gain more data they adapt and in turn get smarter.
Human brains faced with a busier world filled with more information brings about an increase in IQ from generation to generation.
As people migrant to cities creating a more complex environment for the city it to must gather this data, learn and raise its Smart City IQ.
This is The Learning City.
On the other hand, the data Auerswald cites–from the “Santa Fe Team”–only looks at cities from the US, China, the EU, and Germany. How would this data look if it incorporated other megacities, like Manila, Philippines (the world’s densest city); Sao Paolo, Brazil; Bombay, India; Caracas, Venezuela; Karachi, Pakistan; or Jakarta, Indonesia? Of the world’s ten biggest cities, only two–Seoul, #1, and Tokyo, #10–are in the first world. (#9 Shanghai, is well on its way.)
#2 Sao Paolo might be more energy efficient than villages in the Brazilian hinterland (or it may not, as such towns may not even have electricity,) but does it produce more innovation than #11 New York City? (No American city made the top 10 by population.)
If cities are drivers of innovation, why are so many of the biggest in the third world? Perhaps third world countries offer their citizens so little that they experience a form of extreme brain drain, with everyone who can fleeing to the most productive regions. Or perhaps these cities are simply on their way–in a century, maybe Sao Paolo will be the world’s next Shanghai.
The city, by definition, is civilization–but does the city itself spur innovation? And are cities, themselves, living things?
Geoffrey West has some interesting things to say on this theme:
“How come it is very hard to kill a city? You can drop an atom bomb on a city, and 30 years later, it’s surviving.”
I don’t think we’re heading toward a micro-industrial revolution, a 3D printer in every backyard, but they have many interesting possibilities:
3D printing seems perfect for reasonably small, individually customized items like prosthetics, dentures, hearing aids, and shoe inserts. I can easily imagine a vending machine at the shoe store that takes an impression of your feet and then prints custom inserts while you wait–like the photo booth that prints silly pictures of you and your friends.
But let’s discuss possibilities later–today we’re discussing MakerBot, IP, and learning curve. MakerBot was founded in 2009 by Adam Mayer, Zach Smith, and Bre Pettis. The original MakerBot Printers were quite cute, with a DIY, home-hobbiest feel. (They were, in fact, DIY-kits you assembled yourself.) Later MakerBots, by contrast, look like digital ovens and come pre-assembled–aimed at the “professional consumer” market.
In its early days, MakerBot was a creativity-driven startup cobbling everything together in a warehouse where, as they put it, new employees even had to build their own chair when they arrived.
In those days, MakerBot attracted the sorts of hacker nerds who wanted to work in a startup warehouse, and adhered to hacker ethics: the hardware was open-source.
Open-Source hardware meant you could copy their design and build your own, or you could modify your MakerBot and share your innovations with the broader MakerBot community. With an enthusiastic community working together, it wasn’t long before user-created innovations were incorporated back into the MakerBot’s products.
MakerBot also launched Thingiverse, essentially the MakerBot Open-Source community’s home and database on the web.
As MakerBot moved from startup dream to reality, the culture changed. By mid-2011, they had sold about 3,500 bots. In late 2011, The Foundry Group (venture capitalists) invested $10 million and joined the company’s board. In 2012, Bre Pettis pushed out co-founder Zach Smith, (who wanted to remain true to their founding principles.) A month later, the company moved from its startup garage to a New York apartment office headquarters in the sky.
Bre Pettis fired about 100 people (they only had 125 employees when they moved) and hired far more. These new employees weren’t hacker nerds; they were the kinds of people who wanted to wear suits and work in an office.
By June of 2013, they had sold 22,000 printers, and competitor Stratasys Incorporated decided to eliminate them by buying them for $604 million. Good deal for Bre Pettis; shitty for Zach Smith and all of the folks in the MakerBot community whose Open Source hardware ideas eventually made Pettis rich.
Did MakerBot do wrong by transitioning from Open to Closed source? Did they cheat the people who helped them grow, or did they make a wise economic decision?
The growth curve for new startups is initially quite flat:
This is actually the growth curve for yeast, but it’s the same for companies. In their first few years, companies experience little–even negative–growth. Only once they reach a particular size and level of competence do corporations enter a period of rapid growth (until, at maturity, they have captured as much of the market as they reasonably can.)
Much of the difficulty for a new company–especially a company that is building a new product–is informational. Where do I buy parts? Where do I buy 10,000 parts? Where can I hire workers? How do I withhold income taxes from paycheques? Where did I put the receipts for those 10,000 widgets I ordered? What do you mean you threw out all of the steel because it wasn’t good enough?
Solving problems and then routinizing those solutions–as Auerswald would put it, developing code–is critical to early growth. More employees means more knowledge and ideas, but employees cost money, and new companies don’t have a lot of money.
Here’s where Open-Source comes in: by expanding the number of people effectively working on the problem (at least on the hardware end), the open-source community greatly increased MakerBot’s effective company size without increasing costs. Free expertise=faster growth. The community also fostered growth by increasing demand for the bots themselves, as each person who contributed quality printing ideas to the Thingiverse databases increased the realm of ideas other makers and potential makers had to be inspired by.
Once the hardware designs were basically perfected, Open-Source could no longer contribute to hardware innovation, and became a liability, as people could simply download blueprints and make their own bots without paying any money to MakerBot. At this point, as MakerBot entered its rapid growth phase, moved to bigger offices and hired a ton of new employees, it abandoned open-source.
There is a very similar phenomenon in the world of writing, but the ethics are regarded very differently. Many aspiring novelists are members of writer’s clubs, critique groups, or fandoms where they post, share, read, and give feedback on each other’s work. This creative foment and mixing of ideas spurs innovation–as when fan works take on a life of their own nearly independent of the original–and refinement, as when a novel is finally polished and sent out to publishers.
In some cases, very popular writers initially built up followings by publishing in fandoms based around established books or movies before transferring that audience to their own, original works. 50 Shades of Gray, for example, started as Twilight fan-fiction before morphing into its own book.
In other words, in their initial, creative phases, many novels are essentially “open;” this allows the writer to draw on the knowledge and expertise of dozens of other writers. When the novel is good enough to consider publication, it becomes “closed;” a published novel costs money. (It is considered good manners, though, to offer a free copy of the novel a a thank-you gift to anyone who gave significant help along the way.)
This is the same open and closed process as MakerBot pursued, but since it is considered normal and completely expected in writing communities for people to take suggestions, incorporate them into their stories, and then try to pitch the stories to agents, no one looks askance at it. I myself have edited many novels, one of which is now an Actually Published Book by a Real Author. I don’t resent that the book I once read for free and offered feedback for now costs money; I’m just happy on behalf of the author and glad I could help.
By contrast, people were surprised by MakerBot’s pivot, even though it made sound business sense. Surprising people tends to piss them off.
Traditional IP is structured so that copyright/patent protection starts at the time of innovation and eventually runs out; it doesn’t really include an open or semi-open period after which the work becomes closed. In writing this is handled by a convention that so long as the entire novel is not openly posted on the internet or elsewhere, the author can still sell the rights to it. I don’t know how things work over in patents, but given the number of patent infringement lawsuits filed every year, attempting to share designs that you would later like to make closed sounds like a potential nightmare.
Nevertheless, I think something like this Open-Closed process would be beneficial for many new companies, especially as they struggle to grow, learn, and optimize. If it were expected, as in writers’ communities, then the pivot to closed-source wouldn’t be seen as a betrayal, but as a sign of success–a company that had made it big.
Spurring innovation doesn’t just help companies and their owners. We all benefit from better products. Amputees benefit from better, cheaper prosthesis. Sick people benefit from better, cheaper medicines. Poor people benefit from better, cheaper houses.
Just imagine three of these, joined together, located anywhere you want to live…
Welcome to EvX’s Book Club. Today we begin our exciting tour of Philip E. Auerswald’s The Code Eoconomy: A Forty-Thousand-Year History. with the introduction, Technology = Recipes, and Chapter one, Jobs: Divide and Coordinate if we get that far.
I’m not sure exactly how to run a book club, so just grab some coffee and let’s dive right in.
First, let’s note that Auerswald doesn’t mean code in the narrow sense of “commands fed into a computer” but in a much broader sense of all encoded processes humans have come up with. His go-to example is the cooking recipe.
The Code Economy describes the evolution of human productive activity from simplicity to complexity over the span of more than 40,000 years. I call this evolutionary process the advance of code.
I find the cooking example a bit cutesy, but otherwise it gets the job done.
How… have we humans managed to get where we are today despite our abundant failings, including wars, famine, and a demonstrably meager capacity for society-wide planning and coordination? … by developing productive activities that evolve into regular routines and standardized platforms–which is to say that we have survived, and thrived, by creating and advancing code.
There’s so much in this book that almost every sentence bears discussion. First, as I’ve noted before, social organization appears to be a spontaneous emergent feature of every human group. Without even really meaning to, humans just naturally seem compelled organize themselves. One day you’re hanging out with your friends, riding motorcycles, living like an outlaw, and the next thing you know you’re using the formal legal system to sue a toy store for infringement of your intellectual property.
At the same time, our ability to organize society at the national level is completely lacking. As one of my professors once put it, “God must hate communists, because every time a country goes communist, an “act of god” occurs and everyone dies.”
It’s a mystery why God hates communists so much, but hate ’em He does. Massive-scale social engineering is a total fail and we’ll still be suffering the results for a long time.
This creates a kind of conflict, because people can look at the small-scale organizing they do, and they look at large-scale disorganization, and struggle to understand why the small stuff can’t simply be scaled up.
And yet… society still kind of works. I can go to the grocery store and be reasonably certain that by some magical process, fresh produce has made its way from fields in California to the shelf in front of me. By some magical process, I can wave a piece of plastic around and use it to exchange enough other, unseen goods to pay for my groceries. I can climb into a car I didn’t build and cruise down a network of streets and intersections, reasonably confident that everyone else driving their own two-ton behemoth at 60 miles an hour a few feet away from me has internalized the same rules necessary for not crashing into me. Most of the time. And I can go to the gas station and pour a miracle liquid into my car and the whole system works, whether or not I have any clue how all of the parts manage to come together and do so.
The result is a miracle. Modern society is a miracle. If you don’t believe me, try using an outhouse for a few months. Try carrying all of your drinking water by hand from the local stream and chopping down all of the wood you need to boil it to make it potable. Try fighting off parasites, smallpox, or malaria without medicine or vaccinations. For all my complaints (and I know I complain a lot,) I love civilization. I love not worrying about cholera, crop failure, or dying from cavities. I love air conditioning, refrigerators, and flush toilets. I love books and the internet and domesticated strawberries. All of these are things I didn’t create and can’t take credit for, but get to enjoy nonetheless. I have been blessed.
But at the same time, “civilization” isn’t equally distributed. Millions (billions?) of the world’s peoples don’t have toilets, electricity, refrigerators, or even a decent road from their village to the next.
Auerswald is a passionate champion of code. His answer to unemployment problems is probably “learn to code,” but in such a broad, metaphorical way that encompasses so many human activities that we can probably forgive him for it. One thing he doesn’t examine is why code takes off in some places but not others. Why is civilization more complex in Hong Kong than in Somalia? Why does France boast more Fields Medalists than the DRC?
In our next book (Niall Ferguson’s The Great Degeneration,) we’ll discuss whether specific structures like legal and tax codes can affect how well societies grow and thrive (spoiler alert: they do, just see communism,) and of course you are already familiar with the Jared Diamond environmentalist theory that folks in some parts of the world just had better natural resources to work than in other parts (also true, at least in some cases. I’m not expecting some great industry to get up and running on its own in the arctic.)
But laying these concerns aside, there are obviously other broad factors at work. A map of GDP per capita looks an awful lot like a map of average IQs, with obvious caveats about the accidentally oil-rich Saudis and economically depressed ex-communists.
Auerswald believes that the past 40,000 years of code have not been disasters for the human race, but rather a cascade of successes, as each new invention and expansion to our repertoir of “recipes” or “codes” has enabled a whole host of new developments. For example, the development of copper tools didn’t just put flint knappers out of business, it also opened up whole new industries because you can make more varieties of tools out of copper than flint. Now we had copper miners, copper smelters (a new profession), copper workers. Copper tools could be sharpened and, unlike stone, resharpened, making copper tools more durable. Artists made jewelry; spools of copper wires became trade goods, traveling long distances and stimulating the prehistoric “economy.” New code bequeaths complexity and even more code, not mass flint-knapper unemployment.
Likewise, the increase in reliable food supply created by farming didn’t create mass hunter-gatherer unemployment, but stimulated the growth of cities and differentiation of humans into even more professions, like weavers, cobblers, haberdashers, writers, wheelwrights, and mathematicians.
It’s a hopeful view, and I appreciate it in these anxious times.
But it’s very easy to say that the advent of copper or bronze or agriculture was a success because we are descended from the people who succeeded. We’re not descended from the hunter-gatherers who got displaced or wiped out by agriculturalists. In recent cases where hunter-gatherer or herding societies were brought into the agriculturalist fold, the process has been rather painful.
Elizabeth Marshall Thomas’s The Harmless People, about the Bushmen of the Kalahari, might overplay the romance and downplay the violence, but the epilogue’s description of how the arrival of “civilization” resulted in the deaths and degradation of the Bushmen brought tears to my eyes. First they died of dehydration because new fences erected to protect “private property” cut them off from the only water. No longer free to pursue the lives they had lived for centuries, they were moved onto what are essentially reservations and taught to farm and herd. Alcoholism and violence became rampant.
Among the book’s many characters was a man who had lost most of his leg to snakebite. He suffered terribly as his leg rotted away, cared for by his wife and family who brought him food. Eventually, with help, he healed and obtained a pair of crutches, learned to walk again, and resumed hunting: providing for his family.
And then in “civilization” he was murdered by one of his fellow Bushmen.
It’s a sad story and there are no easy answers. Bushman life is hard. Most people, when given the choice, seem to pick civilization. But usually we aren’t given a choice. The Bushmen weren’t. Neither were factory workers who saw their jobs automated and outsourced. Some Bushmen will adapt and thrive. Nelson Mandela was part Bushman, and he did quite well for himself. But many will suffer.
What to do about the suffering of those left behind–those who cannot cope with change, who do not have the mental or physical capacity to “learn to code” or otherwise adapt remains an unanswered question. Humanity might move on without them, ignoring their suffering because we find them undeserving of compassion–or we might get bogged down trying to save them all. Perhaps we can find a third route: sympathy for the unfortunate without encouraging obsolete behavior?
In The Great Degeneration, Ferguson wonders why the systems (“code”) that supports our society appears to be degenerating. I have a crude but answer: people are getting stupider. It takes a certain amount of intelligence to run a piece of code. Even a simple task like transcribing numbers is better performed by a smarter person than a dumber person, who is more likely to accidentally write down the wrong number. Human systems are built and executed by humans, and if the humans in them are less intelligent than the ones who made them, then they will do a bad job of running the systems.
Unfortunately for those of us over in civilization, dysgenics is a real thing:
Whether you blame IQ itself or the number of years smart people spend in school, dumb people have more kids (especially the parents of the Baby Boomers.) Epigone here only looks at white data (I believe Jayman has the black data and it’s just as bad, if not worse.)
Of course we can debate about the Flynn effect and all that, but I suspect there two competing things going on: First, a rising 50’s economic tide lifted all boats, making everyone healthier and thus smarter and better at taking IQ tests and making babies, and second, declining infant mortality since the late 1800s and possibly the Welfare state made it easier for the children of the poorest and least capable parents to survive.
The effects of these two trends probably cancel out at first, but after a while you run out of Flynn effect (maybe) and then the other starts to show up. Eventually you get Greece: once the shining light of Civilization, now defaulting on its loans.
Well, we have made it a page in!
What do you think of the book? Have you finished it yet? What do you think of the way Auersbach conceptualizes of “code” and its basis as the building block of pretty much all human activity? Do you think Auersbach is essentially correct to be hopeful about our increasingly code-driven future, or should we beware of the tradeoffs to individual autonomy and freedom inherent in becoming a glorified colony of ants?
EvX’s Book Club is reading Philip Auerswald’s The Code Economy: A 40,000 Year History looks at how everything humans produce, from stone tools to cities to cryptocurrencies like bitcoin, requires the creation, transmission, and performance of “code,” and explores the notion that human societies–and thus civilization–is built on a mountain of of encoded processes.
I loved this book and am re-reading it, so I would like to invite you to come read it, too.
Discussion of Chapter 1 Jobs: Divide and Coordinate, will begin on May 23 and last as long as we want it to.
Here’s Amazon’s blurb about the book:
What is “code”? Code is the DNA of human civilization as it has evolved from Neolithic simplicity to modern complexity. It is the “how” of progress. It is how ideas become things, how ingredients become cookies. It is how cities are created and how industries develop.
In a sweeping narrative that takes readers from the invention of the alphabet to the advent of the Blockchain, Philip Auerswald argues that the advance of code is the key driver of human history. Over the span of centuries, each major stage in the advance of code has brought a shift in the structure of society that has challenged human beings to reinvent not only how we work but who we are.
We are in another of those stages now. The Code Economy explains how the advance of code is once again fundamentally altering the nature of work and the human experience. Auerswald provides a timely investigation of value creation in the contemporary economy-and an indispensable guide to our economic future.
Spotted Toad posed a question on the loss of Social Capital, my response to which I have been encouraged to encapsulate in a post:
Do you tend to think of reduced social capital as more the result of overgrown education, government, etc “crowding out” other institutions or those institutions withering on the vine of themselves?
Using Toad’s definition of Social Capital as, “the networks of relationships that guide individuals’ behavior and identity (particularly outside of formal economic relationships),” here goes:
First, I’d like to note that Toad’s basic premise is correct. For example, in Social Isolation in America: Changes in Core Discussion Networks over Two Decades, researchers found that:
In 1985, the General Social Survey (GSS) collected the first nationally representative data on the confidants with whom Americans discuss important matters. In the 2004 GSS the authors replicated those questions to assess social change in core network structures. Discussion networks are smaller in 2004 than in 1985. The number of people saying there is no one with whom they discuss important matters nearly tripled. … Both kin and non-kin confidants were lost in the past two decades, but the greater decrease of non-kin ties leads to more confidant networks centered on spouses and parents, with fewer contacts through voluntary associations and neighborhoods.
Things have only gotten worse since 2004.
(Of course, Robert Putnam noticed this in Bowling Alone, which I’ll get to in a minute.)
We could look at a number of other metrics of loneliness/connection: number of kids people have; number of siblings; percent of people who are married; age of first married. Spoiler alert: all of the data is bad. We’re so atomized, we make actual “atoms” look positively social.
The causes of our decrease in social capital are obviously multi-factoral, but here are some important elements I see:
1. Few people live where they grew up, much less where their grandparents grew up. People used to live in communities (or houses!) with multiple generations of the same family–cousins, 2nd cousins, etc.
For example, the Northwest Coast Cultures, ie the Tlingit, Haida, Eyak, and Tsimshian peoples, built clan houses that held 20-50 people, most extended family members. Clan houses are found around the world, from Pakistan to China and even Melanesia.
A friend of mine grew up in an actual multi-generational household, including grandparents, parents, aunts, uncles, and cousins, (and liked it there.)
Another friend who moved back in with her parents after graduation once received a surprise phone call from an old friend she hadn’t seen since elementary school. That friend had tracked down her grandparents’ phone number from 25 years ago, and as her grandparents had only moved a block away in that time, it only took a few minutes for the house’s new residents to reconnect the old friends.
But today, most of us expect to move across the country for school and jobs. My grandparents live a thousand miles from where they grew up, so do my parents, and so do I. Calling my grandparents’ old house wouldn’t get you anywhere. Many of us go through decades where we move every year.
In a community where you grew up, and your parents grew up, and your friends grew up, and their parents grew up, you get the classic case of “everyone knows everyone.” Sure, that can be annoying–but it’s also useful when you’re looking for a skilled plumber and you can just hire the guy who did a great job on your grandma’s plumbing last year.
Communities have simultaneously become bigger and more transient. What’s the point of learning your neighbor’s name if they’re just going to move out in a few years?
2. On top of that, we have technology that makes staying inside more pleasant than going outside. We used to go on the porch to stay cool in the summer, giving us a chance to meet our neighbors; now we stay in with the AC on and watch TV/Twitter.
I like being outside and am often vaguely surprised when, on a particularly pleasant evening, suddenly neighbors I’ve never seen before are in their yards.
Even when we do go out, we’re often still immersed in our phones, ignoring the other humans around us.
3. Community Breakup
Of course Putnam wrote the book on declining social capital (linked at the top of the post.) Among the many causes he investigated, diversity has what appears to be the biggest negative effect:
Prof. PUTNAM: Well, I’ve been interested in the questions of our connections with one another for a long time. I sometimes use the jargon of social capital to refer to the connections – our ties with our friends and neighbors and community and institutions and so on.
And about seven or eight years ago, at the request of communities all across America – big communities and small communities – we did a very large national survey, trying to measure the level of civic engagement and the number of friends people have and how they got along with their local government and so on, in 40 very different communities, places you’ve heard of like Los Angeles or Boston or Atlanta or Detroit or Chicago, and places you haven’t heard, little rural counties in the South Dakota or up in the Appalachias in West Virginia, or villages in New Hampshire – places all over. …
But what we discovered in this research, somewhat to our surprise, was that in the short run the more ethnically diverse the neighborhood you live in, the more you – every – all of us tend to hunker down, to pull in. The more diverse – and when I say all of us, I mean all of us. I mean blacks and whites and Asians and Latinos, all of us. The more diverse the group around us, ethnically, in our neighborhood, the less we trust anybody, including people who look like us. Whites trust whites less. Blacks trust blacks less, in more diverse settings.
3 main factors: first, people from other cultures are literally not from yours; you don’t have the same cultural background and normative expectations as they do. Often people don’t even speak the same language. For a multi-cultural society to work entails creating a new, meta-culture that includes the norms and background knowledge of everyone involved–and that takes time.
Second, the presence of non-cultural members in your community means your community has been physically split apart. Consider an Irish Catholic neighborhood in which all of the locals can walk to the same church, restaurants, shops, and school, where they frequently meet and socialize. Now consider what happens when a new group moves in–let’s say Lutherans. The physical presence of the Lutherans means some of the Irish no longer live near the church or the shops. The old pub gets bought out and replaced with restaurant catering to Lutheran palates. Now you have to go two miles over to get proper mashed potatoes, and maybe you just don’t feel like going that far. The neighborhood has lots its “character.” It withers.
Third, crime. The end of Jim Crow and Great Migration of millions of African Americans to northern cities was marked by a sharp uptick in crime. There were riots–in 1967, the Detroit riot killed 43 people and burned 2,000 buildings. In 1910, Detroit was 98.7% white and one of the world’s richest cities; today it is <10% white, 82.7% black, and a festering wound that anyone who can escape, has.
Where integration happened, it typically didn’t happen in upper-class neighborhoods, but in working class burgs, notably Irish, Italian, and Jewish ones. For example, Harlem, NY, was mostly Jewish and Italian in 1900. In 1910, it was 10% black. By 1930, it was 70%, due to the efforts of enterprising black realtors who saw an opportunity to move blacks into Harlem. Today it is mostly black and Puerto Rican; the Jews and the Italians fled the violence.
Crime soared; inner-city schools became warzones; white students were withdrawn and sent to private schools across town. As neighborhoods cratered families moved, losing the investments they’d poured into their houses. Now moms, dads, and kids all commuted far from their homes every day. (Moms have to pitch in and work, too, to afford the increased housing, school, and transportation costs–so now kids don’t even get to see them after school.) If you were lucky enough to make a friend, you probably weren’t lucky enough to live near enough to hang out.
Few of us today have ever lived in anything resembling a healthy, organic community. Those of us in the suburbs live in HOA-ruled fiefdoms where neighbors report each other for parking in the street or letting their dogs defecate in the back yard, while those in the city are taught to always be alert and never make eye-contact with anyone they pass.
So there are no more organic communities; people commute to work because jobs and “good schools” aren’t in the same place; people stay inside and watch TV instead of go outside and meet their neighbors, etc.
4. Then there’s the big change in employment, from self-employed farmers to employees of larger conglomerates. People used to have individual skills, products, etc. that they could individually trade with each other. Bob might know how to raise a barn; Sally how to milk a goat. Together, Bob and Sally are a pretty good team. Even 50 years ago, even though barns and goats were less important, there were more small businesses, fewer Walmarts.
Today you trade your skills less directly with other humans and more often with corporations. Bob is “skilled with IT systems delivery” and Sally is an “HR representative.” Together they accomplish… not much.
So social capital itself is less important than “selling yourself to the corporation” capital. Maybe we’ll call that corporate capital.
5. There are probably lots of other factors, too, like increasing atheism (the local church is a good place to meet your neighbors if you all attend and a convenient place for community events.) Even an atheist can agree that churches are a great forum for running community events; they have spaces where dinners and weddings can be held; they host ritual gatherings and reinforce moral and social norms. They do charity and host social gatherings.
Further, religious institutions promote a sense of belonging and duty to the group (and maybe there is some inherent utility to believing in a deity.)