Connected-Task Cities Win

A new Journal of Regional Science paper (ungated here) has a fascinating thesis: what makes US cities big and growing lately is not computers, education, creativity, or socializing. Instead it is task connectivity.

Authors Kok and Ter Weel have data on 140K workers in the 168 biggest US cities. Each worker has one of 326 jobs, and each job has weights for 41 different kinds of tasks (listed in table 2). From this they create a measure of what fraction of time workers of each city spend on each task.

They then look at correlations between tasks of these city times. Two tasks that are highly correlated across cities, so that when a city does one task more it usually also does the other task more, are said to be “connected.” It is presumably useful to co-locate connected tasks. If, for a focal task, one adds up all the correlations between that focal task and all the other tasks, one gets a “task connectivity” for that focal task. “Info input” and “work output” type tasks are less connected, and have declined over time, while “mental process” and “interact with others” type tasks are more connected and have increased.

Averaging the connectivity of tasks done in a city, one gets the task connectivity of that city. Kok and Ter Weel find:

Cities with a relatively highly connected task structure seem to be larger, less specialized, and more skilled than cities with lower levels task connectivity. These cities also seem to employ workers for which social skills are relatively more important.

The correlation with city size is pretty strong:

TaskConnect

Looking at employment growth of cities from 1990 to 2009, Kok and Ter Weel find that cities with less task connectivity grew less. Other bad signs for city growth are being big, having high rent, being specialized (like Hollywood and silicon valley), being in the Midwest and not in the West, and being cold in July. After controlling for these features, however, these other features were not growth signs: worker education, computer use, use of social skills, doing routine tasks, and local workers well matched to local jobs.

This paints a plausible picture, but one quite different than we usually see. If you want to be a big growing city, forget all that stuff you usually hear about recruiting educated “creative” workers, getting into computers and automation, promoting social interactions, or specializing in a particular industry. Instead have a nice climate, try to attract industries and jobs that do connected tasks, and get your rents down by increasing your building supply.

This also implies that which cities will win is pretty predictable. If the real estate market hasn’t yet recognized this, then do the calc, and invest in the good cities, and drop the bad ones.

Added noon: A similar result is found at the national level. HT Michael Hendrix.

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SciCast Pays HUGE

I’ve posted twice before when SciCast paid out big. The first time we just paid for activity. The second time, we paid for accuracy, but weakly, as it was measured only a few weeks after each trade. Now we are paying HUGE, for longer-term accuracy. We’ll pay out $86,000 to the most accurate participants, as measured from November 7 to March 6:

SciCast is running a new special! The most accurate forecasters during the special will receive Amazon gift cards:

• The top 15 participants will win $2250 to spend at Amazon.com

• The other 135 of the top 150 participants will win $225 to spend at Amazon.com

Participants will be ranked according to their total expected and realized points from their forecasts during the special. Be sure to use SciCast from November 7 through March 6! (more)

Added: At any one time about half the questions will be eligible for this contest. We of course hope to compare accuracy between eligible and ineligible questions.

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This Time Isn’t Different

~1983 I read two articles that inspired me to change my career. One was by Ted Nelson on hypertext publishing, and the other by Doug Lenat on artificial intelligence. So I quit my U. of Chicago physics Ph.D. program and headed to Silicon Valley, for a job doing AI at Lockheed, and a hobby doing hypertext with Nelson’s Xanadu group.

A few years later, ~1986, I penned the following parable on AI research:

COMPLETE FICTION by Robin Hanson

Once upon a time, in a kingdom nothing like our own, gold was very scarce, forcing jewelers to try and sell little tiny gold rings and bracelets. Then one day a PROSPECTOR came into the capitol sporting a large gold nugget he found in a hill to the west. As the word went out that there was “gold in them thar hills”, the king decided to take an active management role. He appointed a “gold task force” which one year later told the king “you must spend lots of money to find gold, lest your enemies get richer than you.”

So a “gold center” was formed, staffed with many spiffy looking Ph.D types who had recently published papers on gold (remarkably similar to their earlier papers on silver). Experienced prospectors had been interviewed, but they smelled and did not have a good grasp of gold theory.

The center bought a large number of state of the art bulldozers and took them to a large field they had found that was both easy to drive on and freeway accessible. After a week of sore rumps, getting dirty, and not finding anything, they decided they could best help the gold cause by researching better tools.

So they set up some demo sand hills in clear view of the king’s castle and stuffed them with nicely polished gold bars. Then they split into various research projects, such as “bigger diggers”, for handling gold boulders if they found any, and “timber-gold alloys’, for making houses from the stuff when gold eventually became plentiful.

After a while the town barons complained loud enough and also got some gold research money. The lion’s share was allocated to the most politically powerful barons, who assigned it to looking for gold in places where it would be very convenient to find it, such as in rich jewelers’ backyards. A few bulldozers, bought from smiling bulldozer salespeople wearing “Gold is the Future” buttons, were time shared across the land. Searchers who, in their alloted three days per month of bulldozer time, could just not find anything in the backyards of “gold committed” jewelers were admonished to search harder next month.

The smart money understood that bulldozers were the best digging tool, even though they were expensive and hard to use. Some backward prospector types, however, persisted in panning for gold in secluded streams. Though they did have some success, gold theorists knew that this was due to dumb luck and the incorporation of advanced bulldozer research ideas in later pan designs.

After many years of little success, the king got fed up and cut off all gold funding. The center people quickly unearthed their papers which had said so all along. The end.

P.S. There really was gold in them thar hills. Still is.

As you can see, I had become disillusioned on academic research, but still suffered youthful over-optimism on near-term A.I. prospects.

I’ve since learned that we’ve seen “booms” like the one I was caught up in then every few decades for centuries. In each boom many loudly declare high expectations and concern regarding rapid near-term progress in automation. “The machines are finally going to soon put everyone out of work!” Which of course they don’t. We’ve instead seen a pretty slow & steady rate of humans displaced by machines on jobs.

Today we are in another such boom. For example, David Brooks recently parroted Kevin Kelley saying this time is different because now we have cheaper hardware, better algorithms, and more data. But those facts were also true in most of the previous booms; nothing has fundamentally changed! In truth, we remain a very long way from being able to automate all jobs, and we should expect the slow steady rate of job displacement to long continue.

One way to understand this is in terms of the distribution over human jobs of how good machines need to be to displace humans. If this parameter is distributed somewhat evenly over many orders of magnitude, then continued steady exponential progress in machine abilities should continue to translate into only slow incremental displacement of human jobs. Yes machines are vastly better than they were before, but they must get far more vastly better to displace most human workers.

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Open Thread

This is our monthly place to discuss relevant topics that have not appeared in recent posts.

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Philosophy Between The Lines

Seven years ago I raved about a Journal of Politics article by Arthur Melzer that persuaded me that ancient thinkers often wrote “esoterically,” e.g., praising their local religions and rulers on the surface, while expressing their true atheism, rebellion, etc. between the lines. Melzer has just come out with a very well written and persuasive book Philosophy Between The Lines, that greatly elaborates this thesis.

Melzer’s book emphasizes the puzzle that while ancient thinkers were quite open about esotericism, modern thinkers have mostly forgotten it ever existed, and are typically indignantly dismissive when the idea is suggested. Below the fold I give an extended quote on a fascinating transition period in the late 1700s when European intellectuals openly debated how esoteric to be.

While Melzer’s last chapter is on implications of esotericism, he really only talks about how it can somewhat undercut cultural relativism, if we can see intellectuals from different times and places as actually agreeing more on God, politics, etc. Yet he doesn’t mention the most obvious implication, at least to an economist: since esotericism raises the price of reading the ancients, we will likely want to buy less of this product, and pay less attention to what the ancients said. Melzer also doesn’t mention the implications that the rise of direct speech might be in important enabler of the industrial revolution, or that seeing more past esotericism should lead us to expect to find more of it around us today, even if we now officially disapprove of it.

Melzer says that the main point of his book is just to convince us that esotericism actually happened, not that it was good or bad, nor any particular claim about what any particular ancient really meant. But this stance is undermined by the fact that the main bulk of the book focuses on elaborating four good reasons why the ancients might have been esoteric. In contrast, when Melzer talks about why we moderns dislike esotericism, and why esotericism is the usual practice around the world today in non-Western cultures, he mentions many illicit reasons why writers might be esoteric. For example, Melzer quotes An Anthropology of Indirect Communication giving these reasons for such talk:

To avoid giving offence, or, on the contrary, to give offence but with relative impunity; to mitigate embarrassment and save face; to entertain through the manipulation of disguise; for aesthetic pleasure; to maintain harmonious and social relations; to establish relative social status; to exclude from a discourse those not familiar with the conventions of its usage and thereby to strengthen the solidarity of those who are.

But when Melzer talks about why the famous long-revered ancient thinkers might have been esoteric, he gives only reasons that such ancients would have seen as noble: protecting thinkers from society, protecting society from thinkers, teaching students, and promoting social reform.

Now whether the ancients were esoteric for good or bad reasons isn’t very relevant to the empirical claim that they were in fact esoteric, which Melzer says is his main focus. So then why does Melzer focus on if the ancients were esoteric for good reasons? One possible answer is that Melzer actually wants us to like and respect esotericism, not just believe that it existed. Another possible answer is that Melzer sees his readers as biased to see ancient thinkers as good people. If many folks have invested so much in identifying with famous ancient thinkers that they will not accept a claim about those ancients that suggests they were bad people, then to convince such folks of his claim Melzer needs to show that that his claim is quite compatible with those ancients being good people.

Either way, however, Melzer does quite successfully show that the ancients were often and openly esoteric. That promised quote on late 1700s European intellectuals:

Continue reading "Philosophy Between The Lines" »

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“I Robot, You Unemployed”

Tomorrow (Wednesday) at 7pm EST I’ll do a Learn Liberty Live! web presentation on “I, Robot. You, Unemployed” here. After a short ten minute presentation, I’ll lead ninety minutes of discussion. I expect to focus on em econ.

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The Rosy View Bias

How much does merit contribute to success? A rosy view is that success is mostly due to merit, while a dark view is that success is mostly not due to merit, but instead due to what we see as illicit factors, such as luck, looks, wit, wealth, race, gender, politics, etc.

Over a lifetime people gain data on the relation between success and merit. And one data point stands out most in their minds: the relation between their own success and merit. Since most people see themselves as being pretty meritorious, the sign of this data point depends mostly on their personal success. Successful people see a rosy view, that success and merit are strongly related. Unsuccessful people see a dark view, that success and merit are only weakly related.

In addition, successful people tend to know other successful people, and people tend to think their associates are also meritorious. So the other data points around people tend to confirm their own data point. The net result is that older people tend to have more data on the relation between merit and success, with successful people seeing a rosy view, and unsuccessful people seeing a darker view.

Since the distribution of success is quite skewed, most older people see a darker view. However, that dark majority doesn’t get heard much. Most of the people who are heard, such as reporters, authors, artists, professors, managers, etc., see rosy views, as they tend to be both older and successful.

Also, most people prefer to look successful, and so they prefer to look like they’ve seen a rosy view. Even if they haven’t, at least not yet. And a good way to look like you believe something is to actually believe it, even if your evidence doesn’t support it so much.

In sum, we expect the people we hear to be biased toward saying and believing a rosy view of the relation between success and merit. Of course that might be good for the world, if a realistic view would lead to too much envy and conflict. But it would still be a biased view.

Added 11p: Of course if they can find a way to rationalize it, we expect everyone to be inclined to favor a view where merit is a big cause of people reaching up to the success level where they are, but non-merit is a relatively bigger cause of people reaching the higher levels above them. When there are many success ladders we expect people to see merit as a big cause of success on their ladder (up to their point), but as less a cause of success on other ladders.

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Thrown’s Kit’s Self-Deception

Back in July 2010 Kerry Howley published a nice New York Times Magazine article on the tensions between my wife and I resulting from my choice to do cryonics. The very next month, August 2010, is the date when, in Howley’s new and already-celebrated book Thrown, her alter-ego Kit first falls in love with MMA fighting:

Not until my ride home, as I began to settle back into my bones and feel the limiting contours of perception close back in like the nursery curtains that stifled the views of my youth, did it occur to me that I had, for the first time in my life, found a way out of this, my own skin. … From that moment onward, the only phenomenological project that could possibly hold interest to me was as follows: capture and describe that particular state of being to which one Sean Huffman had taken me.

I’ve read the book, and also several dozen reviews. Some reviews discuss how Kit is a semi-fictional character, and a few mention Kit’s pretentiousness and arrogance. Some disagree on if Kit has communicated the ecstasy she feels, or if those feelings are worthy of her obsession. But all the reviewers seem to take Kit at her word when she says her primary goal is to understand the ecstasy she felt in that first encounter.

Yet right after the above quote is this:

And so naturally I began to show up places where Sean might show up— the gym where he trained, the bar where he bounced, the rented basement where he lived, the restaurants where he consumed foods perhaps not entirely aligned with the professed goals of an aspiring fighter. I hope it doesn’t sound immodest to say that Sean found this attention entirely agreeable.

Kit does the same to another fighter named Eric, and later she gets despondent when Erik won’t return her calls. She tracks him down to a fight, hugs him in front of the crowd, and is delighted get his acceptance:

My moment of embarrassment had already transformed into a glow of pride. The entire room saw that I was his, and he mine.

While Kit only feels ecstasy during an actual fight, she spends all her time as a “groupie” to two fighters, Sean and Erik. (She says she is a “space-taker”, not “groupie”, but I couldn’t see the difference.) Kit mainly only goes to fights when these men fight, even when such fights are months apart. Kit’s ego comes to depend heavily on getting personal attention from these fighters, and her interest in them rises and falls with their fighting success. The book ends with her latching on to a new fighter, after Sean and Erik have fallen.

It seems to me that if Kit had wanted mainly to study her feeling of ecstasy while watching fights, she would have gone to lots of fights, and tried to break her feelings down into parts, or looked at how they changed with context. She could have also talked to and studied other fighter fans, to break down their feelings or see how those change with context. But Kit instead sought to hang with successful fighters between fights, when neither she nor they felt this ecstasy she said was her focus. She didn’t even talk with fighters much about their ecstasy feelings. What mattered most to Kit apparently was that fighters associated with her, and that they won fights.

Kit quits her philosophy program:

I knew what they would turn my project into, these small scholastics with their ceaseless referencing of better men would, if they even allowed my explorations as a subject of dissertation, demand a dull tome with the tiniest flicker of insight buried underneath 800 pages of exegeses of other men’s work. Instead of being celebrated as a pioneer of modern phenomenology, I would merely be a footnote in the future study of Schopenhauer, whom, without my prodding, no one would study in the future.

It seems to me that Kit is self-deceived. She thinks she wants to study ecstasy, but in fact she is simply star-struck. The “ecstasy” feeling that hit her so hard was her subconscious being very impressed with these fighters, and wanting badly to associate with them. And she felt very good when she succeeded at that. By associating with their superiority, she could also feel feel superior to the rest of the world:

I would write my fighterly thesis, but I would not fraternize with the healthy-minded; better to leave them to their prenatal yoga, their gluten-free diets, their dull if long lives of quietest self-preserving conformism.

Of course Kerry Howley, the author, does not equal Kit, the voice Kerry chooses to narrate her book. Kerry may well be very aware of Kit’s self-deception, but still found Kit a good vehicle for painting an intimate portrait of the lives of some fighters. But if so, I find it odd that none of the other dozens of reviews I’ve read of Thrown mention this.

Added 21Oct: Possible theories:

  1. Most reviewers read the book carefully, but are too stupid to notice.
  2. Most reviewers are lazy & only skimmed the book.
  3. Reviewers hate to give negative reviews, & this sounds negative.
  4. Readers crave idealistic narrators, and reviewers pander to readers.
  5. My reading is all wrong.

Added 27Oct: Note that at the end of the book Kit articulates no insight on the nature of ecstasy. You might think that if understanding ecstasy had been her goal, she might have a least reflected on what she had discovered.

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Em Software Results

After requesting your help, I should tell you what it added up to. The following is an excerpt from my book draft, illustrated by this diagram:

SoftwareIntensity

In our world, the cost of computing hardware has been falling rapidly for decades. This fall has forced most computer projects to be short term, so that products can be used before they are made obsolete. The increasing quantity of software purchased has also led to larger software projects, which involve more engineers. This has shifted the emphasis toward more communication and negotiation, and also more modularity and standardization in software styles.

The cost of hiring human software engineers has not fallen much in decades. The increasing divergence between the cost of engineers and the cost of hardware has also lead to a decreased emphasis on raw performance, and increased emphasis on tools and habits that can quickly generate correct if inefficient performance. This has led to an increased emphasis on modularity, abstraction, and on high-level operating systems and languages. High level tools insulate engineers more from the details of hardware, and from distracting tasks like type checking and garbage collection. As a result, software is less efficient and well-adapted to context, but more valuable overall. An increasing focus on niche products has also increased the emphasis on modularity and abstraction.

Em software engineers would be selected for very high productivity, and use the tools and styles preferred by the highest productivity engineers. There would be little interest in tools and methods specialized to be useful “for dummies.” Since em computers would tend to be more reversible and error-prone, em software would be more focused on those cases as well. Because the em economy would be larger, its software industry would be larger as well, supporting more specialization.

The transition to an em economy would greatly lower wages, thus inducing a big one-time shift back toward an emphasis on raw context-dependent performance, relative to abstraction and easier modifiability. The move away from niche products would add to this tendency, as would the ability to save copies of the engineer who just wrote the software, to help later with modifying it. On the other hand, a move toward larger software projects could favor more abstraction and modularity.

After the em transition, the cost of em hardware would fall at about the same speed as the cost of other computer hardware. Because of this, the tradeoff between performance and other considerations would change much less as the cost of hardware fell. This should greatly extend the useful lifetime of programming languages, tools, and habits matched to particular performance tradeoff choices.

After an initial period of large rapid gains, the software and hardware designs for implementing brain emulations would probably reach diminishing returns, after which there would only be minor improvements. In contrast, non-em software will probably improve about as fast as computer hardware improves, since algorithm gains in many areas of computer science have for many decades typically remained close to hardware gains. Thus after ems appear, em software engineering and other computer-based work would slowly get more tool-intensive, with a larger fraction of value added by tools. However, for non-computer-based tools (e.g., bulldozers) their intensity of use and the fraction of value added by such tools would probably fall, since those tools probably improve less quickly than would em hardware.

For over a decade now, the speed of fast computer processors has increased at a much lower rate than the cost of computer hardware has fallen. We expect this trend to continue long into the future. In contrast, the em hardware cost will fall with the cost of computer hardware overall, because the emulation of brains is a very parallel task. Thus ems would see an increasing sluggishness of software that has a large serial component, i.e., which requires many steps to be taken one after the other, relative to more parallel software. This sluggishness would directly reduce the value of such software, and also make such software harder to write.

Thus over time serial software will become less valuable, relative to ems and parallel software. Em software engineers would come to rely less on software tools with a big serial component, and would instead emphasize parallel software, and tools that support that emphasis. Tools like automated type checking and garbage collection would tend to be done in parallel, or not at all. And if it ends up being too hard to write parallel software, then the value of software more generally may be reduced relative to the value of having ems do tasks without software assistance.

For tasks where parallel software and tools suffice, and where the software doesn’t need to interact with slower physical systems, em software engineers could be productive even when sped up to the top cheap speed. This would often make it feasible to avoid the costs of coordinating across engineers, by having a single engineer spend an entire subjective career creating a large software system. For an example, an engineer that spent a subjective century at one million times human speed would be done in less than one objective hour. When such a short delay is acceptable, parallel software could be written by a single engineer taking a subjective lifetime.

When software can be written quickly via very fast software engineers, product development could happen quickly, even when very large sums were spent. While today investors may spend most of their time tracking current software development projects, those who invest in em software projects of this sort might spend most of their time deciding when is the right time to initiate such a project. A software development race, with more than one team trying to get to market first, would only happen if the same sharp event triggered more than one development effort.

A single software engineer working for a lifetime on a project could still have troubles remembering software that he or she wrote decades before. Because of this, shorter-term copies of this engineer might help him or her to be more productive. For example, short-term em copies might search for and repair bugs, and end or retire once they have explained their work to the main copy. Short-term copies could also search among many possible designs for a module, and end or retire after reporting on their best design choice, to be re-implemented by the main copy. In addition, longer-term copies could be created to specialize in whole subsystems, and younger copies could be revived to continue the project when older copies reached the end of their productive lifetime. These approaches should allow single em software engineers to create far larger and more coherent software systems within a subjective lifetime.

Fast software engineers who focus on taking a lifetime to build a large software project, perhaps with the help of copies of themselves, would likely develop more personal and elaborate software styles and tools, and rely less on tools and approaches that help them to coordinate with other engineers with differing styles and uncertain quality. Such lone fast engineers would require local caches of relevant software libraries. When in distantly separated locations, such caches could get out of synch. Local copies of library software authors, available to update their contributions, might help reduce this problem. Out of synch libraries would increase the tendency toward divergent personal software styles.

When different parts of a project require different skills, a lone software engineer might have different young copies trained with different skills. Similarly, young copies could be trained in the subject areas where some software is to be applied, so that they can better understand what variations will have value there.

However, when a project requires different skills and expertise that is best matched to different temperaments and minds, then it may be worth paying extra costs of communication to allow different ems to work together on a project. In this case, such engineers would likely promote communication via more abstraction, modularity, and higher level languages and module interfaces. Such approaches also become more attractive when outsiders must test and validate software, to certify its appropriateness to customers. Enormous software systems could be created with modest sized teams working at the top cheap speed, with the assistance of many spurs. There may not be much need for even larger software teams.

The competition for higher status among ems would tend to encourage faster speeds than would otherwise be efficient. This tendency of fast ems to be high status would tend to raise the status of software engineers.

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History Vs. Future

I’ve long puzzled over differing interest in history and the future, in both fiction and non-fiction. And I’ve finally collected some numbers.

Amazon.com says it has 37 million books on offer. Here are the fraction of those books it says are in these named categories:

AmazonCategories

Note that Amazon has no “future studies” category, so I listed the two future-themed categories I found. Here are the fraction of books associated with related keyword phrases:

AmazonKeyword

Why the far larger interest in real history, relative to all the other combinations of future/history and real/fictional? It can’t just be a simple history vs. future effect, nor a real vs fiction effect – it is some sort of combination effect.

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