Monthly Archives: November 2014

The Puzzle Of Persistent Praise

We often praise and criticize people for the things they do. And while we have many kinds of praise, one very common type (which I focus on in this post) seems to send the message “what you did was good, and it would be good if more of that sort of thing were done.” (Substitute “bad” for “good” to get the matching critical message.)

Now if it would be good to have more of some act, then that act is a good candidate for something to subsidize more. And if most people agreed that this sort of act deserved more subsidy, then politicians should be tempted to run for office on the platform that they will increase the actual subsidy given to that kind of act. After all, if we want more of some kind of acts, why don’t we try to better reward those acts? And so good acts shouldn’t long remain with an insufficient subsidy. Or bad acts without an insufficient tax.

But in fact we seem to have big categories of acts which we consistently praise for being good, and where this situation persists for decades or centuries. Think charity, innovation, or artistic or sport achievement. Our political systems do not generate much political pressure to increase the subsidies for such things. Subsidy-increasing proposals are not even common issues in elections. Similarly, large categories of acts are consistently criticized, yet few politicians run on platforms proposing to increase taxes on such acts.

My best interpretation of this situation is that while our words of praise give the impression that we think that most people would agree that the acts we praise are good, and should be more common, we don’t really believe this. Either we think that the acts signal impressive or praise-worthy features, but shouldn’t be more common, or we think such acts should be more common, but we also see large opposing political coalitions who disagree with our assessment.

That is, my best guess is that when we look like we are praising acts for promoting a commonly accepted good, we are usually really praising impressiveness, or we are joining in a partisan battle on what should be seen as good.

Because my explanation is cynical, many people count it as “extraordinary”, and think powerful extraordinary evidence must be mustered before one can reasonably suggest that it is plausible. In contrast, the usual self-serving idealistic explanations people give for their behavior are ordinary, and therefore can be accepted on face value without much evidence at all being offered in their defense. People get mad at me for even suggesting cynical theories in short blog posts, where large masses of extraordinary evidences have not been mustered. I greatly disagree with this common stacking of the deck against cynical theories.

Even so, let us consider some seven other possible explanations of this puzzle of persistent praise (and criticism). And in the process make what could have been a short blog post considerably longer. Continue reading "The Puzzle Of Persistent Praise" »

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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:


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

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

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:


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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