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.
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
2 Portability
3 Indestructibility
4 Homogeneity
5 Divisibility
6 Stability of value
7 Cognizability.”
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.
Auerswald writes:
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
You mean you can just make more of these things? Mugabe is brilliant!
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.
From Ray Kurzweil
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 start the third (and final) part of Auerswald’s The Code Economy: The Human Advantage.
Chapter 10: Complementarity discuses bifurcation, a concept Auerswald mentions frequently throughout the book. He has a graph of the process of bifurcation, whereby the development of new code (ie, technology), leads to the creation of a new “platform” on the one hand, and new human work on the other. With each bifurcation, we move away from the corner of the graph marked “simplicity” and “autonomy,” and toward the corner marked “complexity” and “interdependence.” It looks remarkably like a graph I made about energy inputs vs outputs at different complexity levels, based on a memory of a graph I saw in a textbook some years ago.
There are some crucial differences between our two graphs, but I think they nonetheless related–and possibly trying to express the same thing.
Auerswald argues that as code becomes platform, it doesn’t steal jobs, but becomes the new base upon which people work. The Industrial Revolution eliminated the majority of farm laborers via automation, but simultaneously provided new jobs for them, in factories. Today, the internet is the “platform” where jobs are being created, not in building the internet, but via businesses like Uber that couldn’t exist without the internet.
Auerswald’s graph (not mine) is one of the few places in the book where he comes close to examining the problem of intelligence. It is difficult to see what unintelligent people are going to do in a world that is rapidly becoming more complicated.
On the other hand people who didn’t have access to all sorts of resources now do, due to internet-based platforms–people in the third world, for example, who never bought land-line telephones because their country couldn’t afford to build the infrastructure to support them, are snapping up mobile and smartphones at an extraordinary rate:
And overwhelming majorities in almost every nation surveyed report owning some form of mobile device, even if they are not considered “smartphones.”
And just like Auerswald’s learning curves from the last chapter, technological spread is speeding up. It took the landline telephone 64 years to go from 0% to 40% of the US market. Mobile phones took only 20 years to accomplish the same feat, and smartphones did it in about 10. (source.)
There are now more mobile phones in the developing world than the first world, and people aren’t just buying just buying these phones to chat. People who can’t afford to open bank accounts now use their smarphones as “mobile wallets”:
According to the GSMA, an industry group for the mobile communications business, there are now 79 mobile money systems globally, mostly in Africa and Asia. Two-thirds of them have been launched since 2009.
To date, the most successful example is M-Pesa, which Vodafone launched in Kenya in 2007. A little over three years later, the service has 13.5 million users, who are expected to send 20 percent of the country’s GDP through the system this year. “We proved at Vodafone that if you get the proposition right, the scale-up is massive,” says Nick Hughes, M-Pesa’s inventor.
But let’s get back to Auerswald. Chapter 10 contains a very interesting description of the development of the development of the Swiss Watch industry. Of course, today, most people don’t go out of their way to buy watches, since their smartphones have clocks built into them. Have smartphones put the Swiss out of business? Not quite, says Auerswald:
Switzerland… today produces fewer than 5 percent of the timepieces manufactured for export globally. In 2014, Switzerland exported 29 million watches, as compaed to China’ 669 million… But what of value? … Swiss watch exports were worth 24.3 billion in 2014, nearly five times as much as all Chinese watches combined.
Aside from the previously mentioned bifurcation of human and machine labor, Auerswald suggests that automation bifurcates products into cheap and expensive ones. He claims that movies, visual art services (ie, copying and digitization of art vs. fine art,) and music have also undergone bifurcation, not extinction, due to new technology.
In each instance, disruptive advances in code followed a consistent and predictable pattern: the creation of a new high-volume, low-price option creates a new market for the low-volume, high-price option. Every time this happens, the new value created through improved code forces a bifurcation of markets, and of work.
Detroit
He then discusses a watch-making startup located in Detroit, which I feel completely and totally misses the point of whatever economic lessons we can draw from Detroit.
Detroit is, at least currently, a lesson in how people fail to deal with increasing complexity, much less bifurcation.
Even that word–bifurcation–contains a problem: what happens to the middle? A huge mass of people at the bottom, making and consuming cheap products, and a small class at the top, making and consuming expensive products–well I will honor the demonstrated preferences of everyone involved for stuff, of whatever price, but what about the middle?
Is this how the middle class dies?
But if the poor become rich enough… does it matter?
Because work is fundamentally algorithmic, it is capable of almost limitless diversification though both combinatorial and incremental change. The algorithms of work become, fairly literally, the DNA of the economy. …
As Geoff Moore puts it, “Digital innovation is reengineering our manufacturing-based product-centric economy to improve quality, reduce cost, expand markets, … It is doing so, however, largely at the expense of traditional middle class jobs. This class of work is bifurcating into elite professions that are highly compensated but outside the skillset of the target population and commoditizing workloads for which the wages fall well below the target level.”
It is easy to take the long view and say, “Hey, the agricultural revolution didn’t result in massive unemployment among hunter-gatherers; the bronze and iron ages didn’t result in unemployed flint-knappers starving in the streets, so we’ll probably survive the singularity, too,” and equally easy to take the short view and say, “screw the singularity, I need a job that pays the bills now.”
Auerswald then discusses the possibilities for using big data and mobile/wearable computers to bring down healthcare costs. I am also in the middle of a Big Data reading binge, and my general impression of health care is that there is a ton of data out there (and more being collected every day,) but it is unwieldy and disorganized and doctors are too busy to use most of it and patients don’t have access to it. and if someone can amass, organize, and sort that data in useful ways, some very useful discoveries could be made.
Then we get to the graph that I didn’t understand,”Trends in Nonroutine Task Input, 1960 to 1998,” which is a bad sign for my future employment options in this new economy.
My main question is what is meant by “nonroutine manual” tasks, and since these were the occupations with the biggest effect shown on the graph, why aren’t they mentioned in the abstract?:
We contend that computer capital (1) substitutes for a limited and well-defined set of human activities, those involving routine (repetitive) cognitive and manual tasks; and (2) complements activities involving non-routine problem solving and interactive tasks. …Computerization is associated with declining relative industry demand for routine manual and cognitive tasks and increased relative demand for non-routine cognitive tasks.
Yes, but what about the non-routine manual? What is that, and why did it disappear first? And does this graph account for increased offshoring of manufacturing jobs to China?
If you ask me, it looks like there are three different events recorded in the graph, not just one. First, from 1960 onward, “non-routine manual” jobs plummet. Second, from 1960 through 1970, “routine cognitive” and “routine manual” jobs increase faster than “non-routine analytic” and “non-routine interactive.” Third, from 1980 onward, the routine jobs head downward while the analytic and interactive jobs become more common.
*Downloads the PDF and begins to read* Here’s the explanation of non-routine manual:
Both optical recognition of objects in a visual field and bipedal locomotion across an uneven surface appear to require enormously sophisticated algorithms, the one in optics and the other in mechanics, which are currently poorly understood by cognitive science (Pinker, 1997). These same problems explain the earlier mentioned inability of computers to perform the tasks of long haul truckers.
In this paper we refer to such tasks requiring visual and manual skills as ‘non-routine manual activities.’
This does not resolve the question.
Discussion from the paper:
Trends in routine task input, both cognitive and manual, also follow a striking pattern. During the 1960s, both forms of input increased due to a combination of between- and within-industry shifts. In the 1970s, however, within-industry input of both tasks declined, with the rate of decline accelerating.
As distinct from the other four task measures, we observe steady within- and between-industry shifts against non-routine manual tasks for the entire four decades of our sample. Since our conceptual framework indicates that non-routine manual tasks are largely orthogonal to computerization, we view
this pattern as neither supportive nor at odds with our model.
Now, it’s 4 am and the world is swimming a bit, but I think “we aren’t predicting any particular effect on non-routine manual tasks” should have been stated up front in the thesis portion. Sticking it in here feels like ad-hoc explaining away of a discrepancy. “Well, all of the other non-routine tasks went up, but this one didn’t, so, well, it doesn’t count because they’re hard to computerize.”
Anyway, the paper is 62 pages long, including the tables and charts, and I’m not reading it all or second-guessing their math at this hour, but I feel like there is something circular in all of this–“We already know that jobs involving routine labor like manufacturing are down, so we made a models saying they decreased as a percent of jobs because of computers and automation, looked through jobs data, and low and behold, found that they had decreased. Confusingly, though, we also found that non-routine manual jobs decreased during this time period, even though they don’t lend themselves to automation and computerization.”
I also searched in the document and could find no instance of the words “offshor-” “China” “export” or “outsource.”
Also, the graph Auerswald uses and the corresponding graph in the paper have some significant differences, especially the “routine cognitive” line. Maybe the authors updated their graph with more data, or Auerswald was trying to make the graph clearer. I don’t know.
Whatever is up with this paper, I think we may provisionally accept its data–fewer factory workers, more lawyers–without necessarily accepting its model.
The day after I wrote this, I happened to be reading Davidowitz’s Everybody Lies: Big Data, New Data, and What the Internet Can Tell us about who we Really Are, which has a discussion of the best places to raise children.
Talking about Chetty’s data, Davidowitz writes:
The question asked: what is the chance that a person with parents in the bottom 20 percent of the income distribution reaches the top 20 percent of the income distribution? …
So what is it about part of the United States where there is high income mobility? What makes some places better at equaling the playing field, of allowing a poor kid to have a pretty good life? Areas that spend more on education provide a better chance to poor kids. Places with more religious people and lower crime do better. Places with more black people do worse. Interestingly, this has an effect on not just the black kids but on the white kids living there as well.
Here is Chetty’s map of upward mobility (or the lack thereof) by county. Given how closely it matches a map of “African Americans” + “Native Americans” I have my reservations about the value of Chetty’s research on the bottom end (is anyone really shocked to discover that black kids enjoy little upward mobility?) but it still has some comparative value.
Davidowitz then discusses Chetty’s analysis of where people live the longest:
Interestingly, for the wealthiest Americans, life expectancy is hardly affected by where you live. …
For the poorest Americans, life expectancy varies tremendously…. living in the right place can add five years to a poor person’s life expectancy. …
religion, environment, and health insurance–do not correlate with longer life spans for the poor. The variable that does matter, according to Chetty and the others who worked on this study? How many rich people live in a city. More rich people in a city means the poor there live longer. Poor people in New York City, for example, live longer than poor people in Detroit.
Davidowitz suggests that maybe this happens because the poor learn better habits from the rich. I suspect the answer is simpler–here are a few possibilities:
1. The rich are effectively stopping the poor from doing self-destructive things, whether positively, eg, funding cultural that poor people go to rather than turn to drugs or crime out of boredom, or negatively, eg, funding police forces that discourage life-shortening crime.
2. The rich fund/support projects that improve general health, like cleaner water systems or better hospitals.
3. The effect is basically just a measurement error that doesn’t account for rich people driving up land prices. The “poor” of New York would be wealthier if they had Detroit rents.
(In general, I think Davidowitz is stronger when looking for correlations in the data than when suggesting explanations for it.)
Now contrast this with Davidowitz’s own study on where top achievers grow up:
I was curious where the most successful Americans come from, so one day I decided to download Wikipedia. …
[After some narrowing for practical reasons] Roughly 2,058 American-born baby boomers were deemed notable enough to warrant a Wikipedia entry. About 30 percent made it through achievements in art or entertainment, 29 percent through sports, 9 percent via politics, and 3 percent in academia or science.
And this is why we are doomed.
The first striking fact I noticed in the data was the enormous geographic variation in the likelihood of becoming a big success …
Roughly one in 1,209 baby boomers born in California reached Wikipedia. Only one in 4,496 baby boomers born in West Virginia did. … Roughly one in 748 baby boomers born in Suffolk County, MA, here Boston is located, made it to Wikipedia. In some counties, the success rate was twenty times lower. …
I closely examined the top counties. It turns out that nearly all of them fit into one of two categories.
First, and this surprised me, many of these counties contained a sizable college town. …
I don’t know why that would surprise anyone. But this was interesting:
Of fewer than 13,000 boomers born in Macon County, Alabama, fifteen made it to Wikipedia–or one in 852. Every single one of them is black. Fourteen of them were from the town of Tuskegee, home of Tuskegee University, a historically black college founded by Booker . Washington. The list included judges, writers, and scientists. In fact, a black child born in Tuskegee had the same probability of becoming a notable in a field outside of ports as a white child born in some of the highest-scoring, majority-white college towns.
The other factor that correlates with the production of notables?
A big city.
Being born in born in San Francisco County, Los Angeles County, or New York City all offered among the highest probabilities of making it to Wikipedia. …
Suburban counties, unless they contained major college towns, performed far worse than their urban counterparts.
A third factor that correlates with success is the proportion of immigrants in a county, though I am skeptical of this finding because I’ve never gotten the impression that the southern border of Texas produces a lot of famous people.
Migrant farm laborers aside, though, America’s immigrant population tends to be pretty well selected overall and thus produces lots of high-achievers. (Steve Jobs, for example, was the son of a Syrian immigrant; Thomas Edison was the son of a Canadian refugee.)
The variable that didn’t predict notability:
One I found more than a little surprising was how much money a state spends on education. In states with similar percentages of its residents living in urban areas, education spending did not correlate with rates of producing notable writers, artists, or business leaders.
Of course, this is probably because 1. districts increase spending when students do poorly in school, and 2. because rich people in urban send their kids to private schools.
BUT:
It is interesting to compare my Wikipedia study to one of Chetty’s team’s studies discussed earlier. Recall that Chetty’s team was trying to figure out what areas are good at allowing people to reach the upper middle class. My study was trying to figure out what areas are good at allowing people to reach fame. The results are strikingly different.
Spending a lot on education help kids reach the upper middle class. It does little to help them become a notable writer, artist, or business leader. Many of these huge successes hated school. Some dropped out.
Some, like Mark Zuckerberg, went to private school.
New York City, Chetty’s team found, is not a particularly good place to raise a child if you want to ensure he reaches the upper middle class. it is a great place, my study found, if you want to give him a chance at fame.
A couple of methodological notes:
Note that Chetty’s data not only looked at where people were born, but also at mobility–poor people who moved from the Deep South to the Midwest were also more likely to become upper middle class, and poor people who moved from the Midwest to NYC were also more likely to stay poor.
Davidowitz’s data only looks at where people were born; he does not answer whether moving to NYC makes you more likely to become famous. He also doesn’t discuss who is becoming notable–are cities engines to make the children of already successful people becoming even more successful, or are they places where even the poor have a shot at being famous?
I reject Davidowitz’s conclusions (which impute causation where there is only correlation) and substitute my own:
Cities are acceleration platforms for code. Code creates bifurcation. Bifurcation creates winners and losers while obliterating the middle.
This is not necessarily a problem if your alternatives are worse–if your choice is between poverty in NYC or poverty in Detroit, you may be better off in NYC. If your choice is between poverty in Mexico and poverty in California, you may choose California.
But if your choice is between a good chance of being middle class in Salt Lake City verses a high chance of being poor and an extremely small chance of being rich in NYC, you are probably a lot better off packing your bags and heading to Utah.
But if cities are important drivers of innovation (especially in science, to which we owe thanks for things like electricity and refrigerated food shipping,) then Auerswald has already provided us with a potential solution to their runaway effects on the poor: Henry George’s land value tax. As George accounts, one day, while overlooking San Francisco:
I asked a passing teamster, for want of something better to say, what land was worth there. He pointed to some cows grazing so far off that they looked like mice, and said, “I don’t know exactly, but there is a man over there who will sell some land for a thousand dollars an acre.” Like a flash it came over me that there was the reason of advancing poverty with advancing wealth. With the growth of population, land grows in value, and the men who work it must pay more for the privilege.[28]
Alternatively, higher taxes on fortunes like Zuckerberg’s and Bezos’s might accomplish the same thing.
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,
Coolness overcomes,
Ninkasi, you are the one who spreads the cooked mash on large reed mats,
Coolness overcomes,
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.
Sumerian tablet recording the allocation of beer
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.
Gobekli Tepe, Turkey
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.
Amphitheater, Norte Chico, Peru
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 Great Bath of Mohenjo-daro
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.”
Society itself is a thermodynamic system for entropy dissipation. Energy goes in–in the form of food and, recently, fuels like oil–and children and buildings come out.
Government is simply the entire power structure of a region–from the President to your dad, from bandits to your boss. But when people say, “government,” they typically mean the official one written down in laws that lives in white buildings in Washington, DC.
London
When the “government” makes laws that try to change the natural flow of energy or information through society, society responds by routing around the law, just as water flows around a boulder that falls in a stream.
The ban on trade with Britain and France in the early 1800s, for example, did not actually stop people from trading with Britain and France–trade just became re-routed through smuggling operations. It took a great deal of energy–in the form of navies–to suppress piracy and smuggling in the Gulf and Caribbean–chiefly by executing pirates and imprisoning smugglers.
Beehive
When the government decided that companies couldn’t use IQ tests in hiring anymore (because IQ tests have a “disparate impact” on minorities because black people tend to score worse, on average, than whites,) in Griggs vs. Duke Power, they didn’t start hiring more black folks. They just started using college degrees as a proxy for intelligence, contributing to the soul-crushing debt and degree inflation young people know and love today.
Similarly, when the government tried to stop companies from asking about applicants’ criminal histories–again, because the results were disproportionately bad for minorities–companies didn’t start hiring more blacks. Since not hiring criminals is important to companies, HR departments turned to the next best metric: race. These laws ironically led to fewer blacks being hired, not more.
Where the government has tried to protect the poor by passing tenant’s rights laws, we actually see the opposite: poorer tenants are harmed. By making it harder to evict tenants, the government makes landlords reluctant to take on high-risk (ie, poor) tenants.
The passage of various anti-discrimination and subsidized housing laws (as well as the repeal of various discriminatory laws throughout the mid-20th century) lead to the growth of urban ghettos, which in turn triggered the crime wave of the 70s, 80s, and 90s.
Crime and urban decay have made inner cities–some of the most valuable real estate in the country–nigh unlivable, resulting in the “flight” of millions of residents and the collective loss of millions of dollars due to plummeting home values.
Work-arounds are not cheap. They are less efficient–and thus more expensive–than the previous, banned system.
Urban sprawl driven by white flight
Smuggled goods cost more than legally traded goods due to the personal risks smugglers must take. If companies can’t tell who is and isn’t a criminal, the cost of avoiding criminals becomes turning down good employees just because they happen to be black. If companies can’t directly test intelligence, the cost becomes a massive increase in the amount of money being spent on accreditation and devaluation of the signaling power of a degree.
We have dug up literally billions of dollars worth of concentrated sunlight in the form of fossil fuels in order to rebuild our nation’s infrastructure in order to work around the criminal blights in the centers of our cities, condemning workers to hour-long commutes and paying inflated prices for homes in neighborhoods with “good schools.”
Note: this is not an argument against laws. Some laws increase efficiency. Some laws make life better.
This is a reminder that everything is subject to thermodynamics. Nothing is free.
A great deal of fiction–possibly the majority–is dedicated to the fantasy of having some control over your life. Superman and Batman are strong enough that they can beat up (or otherwise stop) the bad guys, and don’t get sued or put in prison for their vigilante activities. Luke Skywalker gets in a little plane and shoots a laser beam into a hole and thereby brings down an entire Death Star. Voldemort gets pissed off at everyone for treating him shittily and so becomes the world’s most powerful wizard and sets out to make the world burn; Harry Potter uses his own magic power to defeat evil.
One of the most horrible villains in the Harry Potter series isn’t over-the top, sad-backstory Voldemort, but Dolores Umbridge–a plump Hogwartz teacher who dresses in pink, decorates with fluffy pink curtains and china plates with pictures of kittens on them, and makes Harry Potter write apologies in his own blood for, IIRC, having honestly states that Voldemort was back. She is the image of sweetness and propriety while torturing students and helping Voldemort, and there’s nothing Harry and his friends can do to stop her from using the official wizarding world bureaucracy to take over his school, at least until they lure her into the forest and trick her into getting abducted by centaurs.
In real life there are many Doloreses, but no centaurs.
In real life, it is quite illegal to get in a fight (of any kind) with anyone. Even cursing at someone can be “verbal assault.” The desire for revenge against those who’ve wronged you may be a basic human instinct (I am quite certain it is,) but revenge is illegal. Oh, yes, the state can take revenge–the state can lock people up or even put them to death–but ordinary citizens are not allowed to track down miscreants and beat the shit out of them. It is very, very illegal.
What do you do when someone wrongs you?
Here, fill out this form; talk to these people. If your case matches our criteria, something may be done–in months, or years. Here’s some more paperwork.
Nope, sorry, you don’t meet the criteria. There is nothing you can do.
The sheer amount of paperwork to keep track of in American society is overwhelming. I have friends who’ve lived in both America and China; the Chinese do not suffer under half the paperwork burden we do.
“Reducing overhead” remains one of my #1 political agenda items.
Paperwork, bureaucracy, and red tape are crushing our economy. They are probably worse than military spending, welfare, and everything else people hate that the government does combined. And they destroy people’s lives by forcing them to spend their time doing fucking paperwork instead of living.
And we do paperwork because we aren’t allowed to punch each other anymore.
If a mining company destroys a community by dumping poison waste into the local drinking water, the natural consequence is that the affected locals find the CEO, tie him to a chair, and drop him in the river. Today you file a class-action lawsuit and petition the local city officials to switch drinking water sources and groan in frustration as nothing happens for three decades straight.
Living in cities (as most of us do) means coming into constant contact with other people. Some of those people are nice, some are mean, and most are just irrelevant. You pass them on your way to work (or they pass you), ignore them at lunch and try not to make eye contact with them on the street.
Don’t make too much noise; the neighbors might hear you.
I was just talking to someone who was vociferously complaining that their neighbors “slam their car doors” at 2 am. And what will they do? Ask their neighbors to close their doors more softly? Or call the police to report a noise complaint? Probably the latter.
Everyone has to dial down their personalities, close up, avoid the people around themselves to avoid conflicts with the hundreds (or thousands) of people they pass by every day, otherwise lawsuits or police officers get involved.
Cities are intolerable.
There is no power in real life; no one (except maybe lawyers, police officers, and some politicians,) has any power.
For all my disagreement with them, I understand where the BLM crowd and their ilk are coming from: they feel powerless. The system is against them (it’s against everyone.)
Pretty much the only easy way to get power in modern society is to assemble a Twitter mob and attack someone. Maybe you can get them uninvited to a con, or kicked out of a university. Maybe you can just make them cry: power.
It’s the closest we come to bloodying a bully’s nose.
You might say the Twitter mob is the bully.
Yes, that’s the entire point. The bully is the one with the power.
A friend of mine was abused as a child. It’s powerless enough just being a child; everyone else is bigger than you. You must constantly obey others–teachers, parents, even older siblings and bigger kids on the playground. But to be beaten by your parents is another level entirely. And no one saved my friend. They grew up, broken, and devoted their life to becoming the biggest, baddest, meanest person around so they wouldn’t be hurt again.
Of course, then the police got them.
Even when something doesn’t involve conflict–just a simple change that would benefit everyone involved–it’s virtually impossible to get anything done. Take milk. Pediatricians overwhelmingly agree that children should drink regular–4%, full-fat, whole–milk, not low-fat or fat-free milk. The low fat milks are specialty diet products for people who are on a diet, and pediatricians don’t advise putting your kid on a diet unless they truly need to be on one, because calorie restriction can be really unhealthy when your body is supposed to be growing. Despite this, my kids’ school only serves low-fat and fat free milk, and since no one who has the actual power to make purchasing decisions gives a shit that this is actually unhealthy for kids, only an insane amount of protesting on my part (say, convincing a few hundred parents to sign a petition to change the milk) could get them to change the milk to the variety it is supposed to be.
And this is accompanied by the infuriating feeling that people are only pretending to listen, because they never actually change anything.
So the best we can do is put on a movie or pic up a book and read about someone else–the girl who wins the super handsome hunk, the hero who defeats the evil bad guy–who gets to be powerful and control their life.
evolutionistx 