Yes, I’ve been away for a while. Yes, this is 50K words. It’s worth it. Read on.

HANDLE:

Chapter 1, Work and Wages in iWorld:

1. Cowen lays out some facts:

1.A. Real wages for young people are down, and unemployment (or, more precisely, underemployment and the labor-force dropout rate) is up.

1.B. The situation is international

1.C. Meanwhile the very top earners are earning much more.

1.D. The Great Divergence (Or Great Bifurcation, or Great Skewing) is widespread and expresses itself in many dimensions, good things are correlated with other good things, likewise bad with bad.

2. This is because:

2.A. Automation and increasing productivity of industrial robots and intelligence machines (intelligence used in a weak sense, mostly any IT).

2.B. Globalization

2.C. Split of the economic into very stagnant (Baumol services, old tech) and very dynamic (new tech) sectors.

The key questions will be: Are you good at working with intelligent machines or not? Are your skills a complement to the skills of the computer, or is the computer doing better without you? Worst of all, are you competing against the computer? Are computers helping people in China and India compete against you? … Ever more people are starting to fall on one side or the divide or the other. That’s why average is over.

There is now a joke that “a modern textile mill employs only a man and a dog – the man to feed the dog, and the dog the keep the man away from the machines.”

Not each and every one of these innovations will pay off. But let’s ask a few questions. First, in which major areas do we see ongoing technological advances exceeding expectations from just a few years ago? Second, in which areas do we see a lot of new and promising technological works in progress? Third, in which areas can we expect the general forces propelling innovation … to remain powerful? Finally, can we see evidence that these areas are already influencing economic statistics measuring our nation’s well-being? I’ll get into more detail on all of these questions, but for now the point is that the areas of the economy identified in the answers to these questions all overlap on one technology: mechanized intelligence.

3. Human beings are more predictable, readable, and ‘exploitable’ by sensors and algorithms than many people would comfortably accept or want used against them. But these statements are true, they are exploitable, and so they are going to be exploited, and the world is going to change a lot because of it. This will creep people out at first, precisely because they will be so effective.

This [Franchise, by Asmiov] may sound outrageous to many. It seems to cross a precious line of liberty and freedom. But perhaps we are not as free as might think in the first place. … the future of technology is likely to illuminate the unsettling implications of how predictable we are and indeed in 2012 political campaigns invested heavily in predicting where to find supporters and important swing districts.

Whether we like it nor not, our sparring partners will use mechanized intelligence during our business contests

Wasn’t that in the movie of Crichton’s Rising Sun (1993)? We’re going to get a world where people actually are under the social equivalent of the Eye of God, where everyone has plausible access to all your embarrassing past sins even more than today, so it really does make people God-fearing and suppresses transgression. Of course, that includes political and ideological transgression.

Eventually it will be commonly understood that such analyses are going on in real time. Negotiators will be trained to fool or otherwise throw off the voice-tracking programs. In turn the programs will be improved to keep up with these tactics, setting up a never-ending “arms race” between technologies of deception and detection. And a new kind of sophisticated social interaction will develop. That is bigger news than any new gadget. … We may tend to think of mechanized intelligent analysis as primarily useful in judging other people, but it will also have the potential to promote self-knowledge. During a date, a woman might consult a pocket device in the ladies’ room that tells her how much she really likes the guy. The machine could register her pulse, breathing, tone of voice, the level of detail in her narrative, or whichever biological features prove to have predictive power.

The sorry truth is that if we knew all or even some of the bad things about our prospective partners, we might be so cautious that we never take a romantic leap. As it stands, the world is set up to overreact to negative information, as even a whiff of scandal causes us to lose trust in other individuals. We will need some significant cultural changes to make do with an increase in the “warts and all” coverage mechanized intelligent analysis will soon be delivering about virtually all notable public figures and many private individuals too. … The positive illusions (all out children are above average) that help us get through everyday life could so easily wither in the face of sangfroid machine critiques. Not this year or next year, but most likely within our lifetimes

4. Tech is improving faster than other sectors because more lightly regulated.

It is no accident that we are seeing so many signs of significant progress in mechanized intelligent analysis, albeit in varying stated of maturity. First, Moore’s law about ongoing advances in processing speed has continued to pay off, with no immediate end in sight. Second, the machine intelligence sector is largely unregulated. If you compare it to health care as a world-altering, stagnation-ending breakthrough industry, regulatory obstacles are a far greater problem for pharmaceutical companies and for hospitals than for the like of Google, Amazon, and Apple. Health care, with its physician licensing, Byzantine hospital regulations, and FDA approval process, also makes most of its changes quite slowly, for better or worse.

…

Still, very often entrepreneurs and scientists can do the work behind smarter machines, and develop usable products, without need much special permission from the powers that be. … Technological progress slows down when there are too many people who have the right to say no, but software in general gets around a lot of the traditional veto power point.

HANDLE:

Part I, Chapter 2: The Big Earners and the Big Losers

…here is what is scarce: 1. Quality land and natural resources

2. Intellectual property, or good ideas about what should be produced

3. Quality labor with unique skills Here is what is not scarce these days: 1. Unskilled labor, as more countries join the global economy

2. Money in the bank or held in government securities, which you can think of as simple capital, not attached to any special ownership rights (we know there is a lot of it because it has been earning zero or negative real rates of return)

I. Marketing. Echoing Sailer:

Despite all the talk about STEM fields, I see marketing as the seminal sector for our future economy.

Here’s a recent and related article of his at Bloomberg.

II. Personal Services, people are going to treat the rich like royalty all the time, and constantly try to get access to them:

We can expect a lot of job growth in personal services, even if those jobs do not rely very directly on computing power. The more that the high earners pull in, the more people will compete to serve them, sometimes for high wages and sometimes for low wages. This will mean maids, chauffeurs, and gardeners for the high earners, but a lot of the service jobs won’t fall under the service category as traditionally constructed. They can be tough of as “creating the customer experience.” … All of those people are working to make you feel better. They are working at marketing. It sounds a little silly, but making high earners feel better in just about every part of their lives will be a major source of job growth in the future.

He uses the metaphor of Calcutta beggars all climbing over each other to try to get the favorable attention of a passing billionaire. That’s a lot of people in the future. Billionaires will both be flattered by that, but also quite annoyed and one can expect them to become a bit calloused to it all and put up, for lack of a better term, their ‘bitch shields’ to the army of pitchmen, or force them to go through gatekeepers. That reminds me of this classic Roosh post.

For high earners, life will feel better than ever before, but at the same time, life will feel more harried and more overloaded with information than ever before.

III. Who is earning? Mostly managers, finance, and law.

If we look at the increase in the share of income going to the top tenth of a percent from 1979 to 2005, executives, managers, supervisors, and financial professionals captured 70 percent of those gains.

IV. Scrutiny and Hyper-Meritocracy.

We are going to be scrutinized at work much more closely, continuously, and much more accurately, than most people are used to, or will be comfortable with. There is a joke. “In the past, they said that in the future people would only work 3 hours per day. They were right. What they didn’t know is that, in the present, we still sit in front of our desks for the other 5 hours, pretending to work.” That joke won’t be funny soon, and it may not ever make sense to future workers.

Another development is this: The better the world is at measuring value, the more demanding a lot of career paths will become. This is why I say “welcome to the hyper-meritocracy” with a touch of irony. Firms and employers and monitors will be able to measure economic value with a sometimes oppressive precision.

V. Scarce and Surplus types of labor:

In any case, the slacker twenty-two-year-old with a BA in English, even from a good school, no longer has such a clear path to an upper-middle-class lifestyle. At the same time, Facebook, Google, and Zynga are so desperate for talent that they will buy out other companies, not for their products, but rather to keep their employees. It’s easier and cheaper to buy the companies than to try to replicate their recruiting or lure away their best employees. Often the purchased product lines are abandoned. … The technology blogs call this being “acqhired,” …

Heh – business opportunity. Out-compete the big boys recruiting talented and undervalued engineers out of college to work at a ‘start up’ and paying them little or just in promises of options or something. Have them work on some project which gets attention but otherwise doesn’t have to make any genuine business sense. Maybe do a culling down to the best half. Then tell Google you’ve got an engineer-plantation they can buy, slaves and all. Google doesn’t care about the land or the cotton, it has its own groves, but it likes the slaves, because you are known to have good taste in slaves. Profit.

VI. Good managers are the scarce input in many operations, which is why their salaries have gone up so much.

He says that top folks simply don’t have enough time to invest in managing more people. He gets offers from good people to work as an additional research assistant for him for free, and he still turns them down, because he has no more time. He would have to get a middle manager to take on more people, even free people, and he can’t do that cheaply.

To hire a risky and iffy worker, without a competent overseer, simply isn’t worth it, no matter how low the wage. And so a lot of workers have a hard time being picked up and integrated into productive teams. It is precisely that process that managers are paid to make work more efficiently. It is a process that is continuing its long, long trend towards increasing importance. And finally it is why managers are being paid more.

VII. Workers need to be more conscientious and happily obedient to be valuable these days.

Team production makes the quality of “conscientiousness a more important quality in laborers. Managers need workers who are reliable. If you have a team of give, one unreliable worker is wrecking the work of four others. … It’s not just that the bad workers are lazy or maybe destructive. It’s that low quality workers spread bad moral to many others …

VIII. Conscientiousness not equal between sexes

The growing value of conscientiousness in the workplace helps women do better than men at work and in colleges and universities. At my daughter’s recent college graduation ceremony the awards for the top achievers in all the school’s programs and departments went almost entirely to women, including awards in science and mathematics.

I’ve seen this during my education too. They didn’t necessarily get the choicest jobs, but boy did they rack up the good grades awards.

It is well known from personality psychology, and confirmed by experience, that women are on average more conscientious than men. They are more likely to follow instructions and orders with exactness and without resentment. You can think of men as the “higher variance” performers at work. That means some men are more likely to be the very highest earners and also the exhibit extreme dedication to the task … Other men, in greater number, will be more irresponsible, more likely to show up drunk, more likely to end up in prison, and more likely to become irreparably unemployable.

Didn’t Larry Summers get fired from his Harvard Presidency for saying something similar?

Here is another, more general way to think about the shifting gender balance of power in education and parts of the workplace. The wealthier we become, the grater a cushion we have against total failure, starvation, and other completely unacceptable outcomes. In such a world, both women and men will indulge some propensities that otherwise might be stifled or kept under wraps or that would not have been affordable fifty or one hundred years ago. For some men, these propensities are quire destructive and this turns them into labor market failures.

And some of the women turn into sexual market failures. Anyway, it’s all about minimizing management and supervision costs, especially through early screening and filtering.

The premium is on conscientiousness, namely whether the worker can follow some straightforward requests with extreme reliability and basic competence. … If you’re a young male hothead who just can’t follow orders and you have your own ideas about how everything should be done, you’re probably going to have an ever-tougher time in the labor markets of the future. [Smoky! -H]

Let’s draw up a simple list of some important characteristics … 1. Exactness of execution becomes more important relative to accumulated mass of brute force

2. Consistent coordination over time is a significant advantage

3. Moral is extremely important to motivate production and cooperation

IX. Bad workers are potentially huge liabilities in lots of ways, including of course disastrous legal consequences. Exclusion of even slight risks is key.

Workers represent a firm to the broader outside work, and the firm faces a higher risk of lawsuits. … It is easier to destroy than to create, and the more valuable and the more precision-based that firms become, the more they will worry about destruction of value coming from workers. Any time there is a discussion of management strategies, you probably will hear a lot of works like teamwork, morale, and integrity. That’s all well and fine, but what if we substitute exclusion for all those nice warm phrases. They would be the same management strategies merely explained from a different point of view, namely of those who are kept away. There is no high morale without exclusions, no integrity without exclusion, and no corporate culture without exclusion. If the management styles at today’s quality companies seem so nice, so friendly, and sometimes so downright heartwarming, it is possible only because those cultures are so very picky, snobbish, and elitist at the same time. There is no open door.

X. Careers – the brightest, best-compensated, non-STEM people are going into finance, law, and consulting.

X.A. Law: Today, laws are more numerous and more complicated increasing demand for lawyers, at least at the top end.

X.B. Consulting: A global economy means longer supply chains, and consultants can help businesses track and evaluate those complex operations (he goes on to say that many business managers never step back and do real intellectual ‘fresh look / big picture’ analysis of their operations.

X.C. Finance: Growing in part because the promise of bailouts encourages banks to become larger and take on more risk (your mileage may vary with this one)

Working to exercise and demonstrate their general intelligence is in fact the main thing they are good for, and moving beyond this can take quite a few years. … We tend to glamorize these well-paying jobs. If we can set aside the glamour and perhaps our envy, me might notice that our society does not know what else to do with these people, who are otherwise not always very productive.

VLADIMIR:

I’ve been very busy with work and social obligations in the last few days, so I won’t be able to make a detailed comment for another day or two. However, for now, I’d just like to point out that this stuff appears to be optimized for injecting a dose of reality into the mainstream in a way that tries to avoid treading on any crimethink mines while minimizing the inevitable distortion of truth that follows from that. I think Cowen should be given much credit for doing it so successfully.

Heh – business opportunity. Out-compete the big boys recruiting talented and undervalued engineers out of college to work at a ‘start up’ and paying them little or just in promises of options or something. Have them work on some project which gets attention but otherwise doesn’t have to make any genuine business sense. Maybe do a culling down to the best half. Then tell Google you’ve got an engineer-plantation they can buy, slaves and all. Google doesn’t care about the land or the cotton, it has its own groves, but it likes the slaves, because you are known to have good taste in slaves. Profit.

This might actually be a part of the explanation for all these startups that appear to be wildly overvalued relative to the merits of their ostensible business plan.

HANDLE:

I know a Tesla mechanic and he really likes his job. He used to work for BMW, and said it had a truly toxic culture (not one that sounded very traditionally German) and the rats (i.e. other mechanics) were fleeing from a sinking ship. A former BMW maintenance manager was poached by Tesla, and he knew who the good guys were at BMW, and so was given them task of poaching them too.

Which really make you think.

One thing Tesla has is that anyone who can create a new car company from scratch will maintain a permanent advantage over all established car companies, in that it won’t be saddled with all those tremendous pension liabilities to former workers, and established super-powerful unions. Musk certainly has an incentive to get as far ahead on the automation curve as possible to avoid ever having to deal with those problems at anything like the magnitude of burden all the other companies must carry.

That makes it very hard for any established company to eat his lunch by copying simple and widely available tech, while also making it hard for any other new company to overcome the barrier to entry, especially if future subsidies are likely to be less generous than what Musk got to help him get started. That means there is a special, one-time opportunity to pick up this particular $100 bill off the sidewalk. He picked it up.

I admit I didn’t give this particular advantage enough consideration before, and now it seems to help account for Tesla’s unique ability to capitalize on electric cars with big batteries, which, after all, anyone can make. But his timing means that he’s the only one that can make them both with the most generous subsidies and before amassing manufacturing-era labor liabilities and before sclerosis infects his company.

It’s not necessarily regulatory arbitrage as it is also a kind of legacy sclerosis arbitrage. Indeed, this was and remains a considerably portion of the competitive advantage of East Asian automakers in the US market. All else being equal, the Big Three had to make an extra few thousand dollars per vehicle to pay for their liabilities. Tesla gets to start from scratch with a clean slate. That just having a clean slate is such a huge advantage these days is revealing in itself. Combined with ludicrously generous crony subsidies, it makes a strong case for his special, inimitable position.

Furthermore, in addition to not being saddled with the unions and all those pension liabilities to former workers, he’s got another advantage which accrues to any new company in an established sector, indeed one the big Silicon Valley companies have conspired among themselves to avoid by means of forming a labor-market demand-side cartel.

I’m guessing a lot of your work environments are a lot like mine, where compensation is fairly flat and compressed and bears little relation to ones marginal productivity in the short term, despite everyone knowing informally who is really pulling the weight. In the long term high performers are rewarded with promotions, but this suffers from Peter Principle problems, and anyway only works in tall hierarchies. There is a new employee where I work who is getting paid nearly as much as I am, but who is doing 20% of the work, because he is a moron, but he beats everybody in seniority, which is, alas, how the system works. He won’t get promoted, but in a way that’s almost worse, since the good performers will leave the job and people like him will stick around, lowering average productivity.

Everybody I know has lots stories like these.

So that creates another kind of obvious arbitrage opportunity. Maybe “Productivity Correlation Arbitrage.” If one could only pick one good manager in a unit or office, tell him he must fire 60% of people, and that he has unlimited authority to fire anyone he wants, and those he retains will get paid double so long as all the work gets done, then I have no doubt that the company and everyone left will be much better off.

Some seasonal companies actually do something like via over-hiring, automatic attrition, and selective rehiring. I had an uncle-in law who worked a job like this on the Alaskan oil fields and called it something like an “underbrush fire” that left all the big timbers standing.

But most mature organizations, especially those saddled with strong unions, can’t legally or practically manage anything remotely approaching this kind of ruthless culling.

But if a new company can poach a few good managers with the special inside knowledge needed to be future poachers of more good people, then your new company can start off with much better people producing much more value and for only a little more money. Is Tesla doing this too? That’s pretty smart, and it seems to borrow from some insights that may have been gained from Silicon Valley experiences.

Hmm… something to think about.

HANDLE:

Part I, Chapter 3: Why Are So Many People Out of Work?

I. The labor force participation rate has been going down for some time.

Cowen includes this picture (though timed to lack the little upward spike at the end)

Well, 67 to 63 in 15 years seems pretty dramatic.

But let’s constrain to prime working age adults:

Not as dramatic. Let me abuse faulty human visual pattern finding here (something between pareidolia and apophenia). What I want is to do a Fourier transform and get out the seasonal signal, but also the business cycle oscillation with a bust in the early 90’s, boom in the late 90’s, bust in the early 00’s, boom in the mid to late 00’s, and then the GFC big bust until, well, now. What I want to say is that the smooth hump that would be left would have peaked around 1996 just above 84%, and in 20 years has declined to over 81%. The questions are whether that 3% is gone for good, and where things go from here.

A big part of the difference is more higher education. Not all the difference, but a lot. A lot of people criticize Cowen, saying he is purposefully ignoring that and so exaggerating the problem. IIRC, he has responded that it still means that our economic structure makes more bodies unavailable for production at any moment, but the issue here is supposed to be ‘involuntary unemployment’.

II. Here is how the step by step evolution of machine intelligence worked in Chess (an obsessive analogy with Cowen, for obvious reasons, for much of the book.)

This step-by-step evolution is how intelligent technology will change a lot of industries. At first the machine hardly adds anything and it’s really just an investment in building a better machine. At the second step, experts – the in field of the program’s operation – will be required to work with the machines, to fill the gaps in what the machines can do. As the programs improve, the next and third step is that the humans understand the programs very well, with a minimum of expertise – but expertise nonetheless – in the relevant industry. These workers will essentially be information processors, albeit with an understanding of context. The fourth and final step is that the human isn’t needed much at all because the program on its own is so strong.

Computer programs do especially well in chess because it is a totally regularized environment where the right answer can be ascertained, at least in principle, by pure calculation. … In poker, the very best players are still humans, because the computers don’t know how to psych out the opponent, bluff, or real the “tells” from the guy sitting across the table. The more that an endeavor requires inferences about the mind-states of others, the more than intelligent machines will require human aid. We humans do have out talents.

III. Back to labor force participation

Those numbers on labor force participation are telling us that, for whatever reason, over 40 percent of adult, non-senior Americans don’t consider it worthwhile to have a job. They can’t find a deal that suits then. … Adult males are seceding from the workforce – or being kicked out – in frightening numbers. Few of these individuals are wealthy playboys. Is it no surprise that popular culture today has this image of the male slacker, a young man who lives at home, plays video games, is indifferent to holding down a job, and maybe doesn’t run after young women so hard. … People are getting accustomed to an existence where they cannot find satisfying work at a wage they are happy with. … Ten years ago, 5 million Americans collected federal disability benefit; now the number is up to 8.2 million, at a direct dollar cost of $115 billion a year, over $1,500 for every American household. Yet the American workplace, as measure by deaths and accidents, has never been safer.

The number actually peaked near 9 million in Sept, 2014, and has been mostly flat ever since, with a small but steady decline afterwards. The number of applications and awards (the first derivative) peaked in late 2011 and has declined over 25% of average since then. The Disability Trust Fund was set to go bankrupt in late 2016, and was expected to lose $30 Billion a year indefinitely.

The ‘fix’ was in the “Bipartisan Budget Act of 2015”, which shifted 0.57% of the 12.4% in total payroll taxes from the old age program to the disability program, but only for three years (so, spending 5.4% of ‘pension contributions’ to bail out a formally financially segregated insurance program). DI tax receipts, which had been mostly flat for 8 years, suddenly jumped 35% in 2016, by over $40 Billion extra dollars, pushing off the insolvency of one trust fund, at the cost of accelerating the insolvency of the other trust fund, but in the end only delaying the inevitable DITF bust for about 5 or 6 years, unless there’s another bailout, which everyone knows there will be. This was one of the most under-reported story of that year, and not just because of the election and Trump.

These problems with labor have gone beyond the general problems with our economy, so something has gone wrong with work itself. … But for men, from 1969 to 2009, as measured, it appears that wages for the typical or median male earner have fallen by about 28 percent.

He admits some people dispute that number, but even some rosy assumption come up with a pretty disappointing and surprising number.

Imagine yourself as an economist back in 1969, being asked to predict the course of American male wages over the next forty years or so. You are told that no major asteroid will strike the earth and that there will be no nuclear war. The riots of the 1960s will die out rather than consuming out country in flames. Communism would go away as a major threat and most of the world would reject socialism. Who would have thought that wages for the typical guy were going to fall?

Ctrl-F for ‘immig’ comes up nothing, and maybe those numbers should have been stipulated as well to our hypothetical 1969 economist. And there were some people on the nationalist, anti-globalist right who thought wages for the typical guy would be hurt by a more open economy with more open borders. But there were not dominant voices, to put it mildly.

IV. The Great Recession. During the boom, firms weren’t paying much attention to granular productivity. After the bust came the microscopes and the firings.

Firms … took some discrete steps to figure out which workers were adding the most value, and once they identified the less productive workers, they let them go. … Those laid-off workers have been absorbed into new jobs at a rate much slower than is usual following a recession. They can’t get their old jobs back, even though the worst of the crisis is over and corporate profits are back up. Most importantly, the new jobs being created are more likely low wage than mid-wage. … most labor market polarization is transmitted through the immediate mechanism of recessions, which is when those middle class jobs are disappearing. After the recession is over, the lost middle class jobs do not come back.

V. Putting aside problems with short-term nominal stickiness, lower wages still can’t fix the problems with contemporary low-productivity labor, as in the past, because today’s suspect workers just aren’t worth the trouble.

“Seeking only workman’s wages I come looking for a job, but I get no offers. Except the come-ons from the whores on Seventh Avenue …”

It doesn’t matter how flexible the wage is in the more complex, less brute force jobs. A manual worker who just shows up at your door is probably not someone you want to hire unless it is already part of a preexisting business plan with broad buy-in from your enterprise and your creditors. The worker might say, “I’ll lower my wage demands by thirty percent!” or, “I’ll work for nothing!” It usually won’t matter. The sad reality is that many of these workers you don’t want at all, even if the business plan involves additional labor. Some workers simply aren’t worth the trouble unless the demand for extra labor is truly pressing. I believe these “zero marginal product” workers account for a small but growing percentage of out workforce.

During the surge and temporary force-builds, the Army and Marines had to lower standards and accept less impressive applicants in order to meet accession quotas for enlistedmen. Usually that involved relaxing each of the many standards each by a little bit. Actually, the system pretends the standards aren’t being changed at all, but that individuals are being granted discretionary ‘waivers’ of a typical standard on a one by one basis by commanders, which is the system ordinarily used rarely in exceptional cases for people with extreme talent or value in some area, but maybe just under the threshold for one of the standards. Well, suddenly these waivers were routine. Still, there is value to keeping the standards ‘in the book’ the same, since everybody still knows what they are supposed to do, and the waivers will eventually go away when the pressure is off.

But eventually you are going to be cutting into muscle and bone and not able to relax some standards any more. And someone is going to discover where you are going to get the most bang for your buck in terms of the greatest numbers resulting from a policy change in the other standards. That turned out to be in background check department, which gave rise to the whole ‘moral waivers’ problem. A lot of these guys were good soldiers, fit enough and smart enough to fit in, go fighting downrange, and get the job done well, but, inevitably, a huge number of them got into serious disciplinary trouble at some point. They were good workers who would get in trouble, which is a very different problem from the obedient and law-abiding ones that just aren’t up to snuff.

In times when men were desperately needed, when those men got in trouble, they’d get slapped on the wrist with minor penalties, or even just a good old-fashioned “smoke the shit out of him” extended painful-exertion session with an NCO. But as soon as Congress announced the numbers had to go down – by a lot, and quickly – then a very different message went out to commanders. Suddenly every little thing was a dischargeable offense, and it was, predictably, disproportionately the moral-waiver guys who were getting kicked out.

VI. Productivity. The labor-productivity statistics following the GFC bust bear this story out.

Cowen says that in a typical recession, especially under simple Keynesian Aggregate Demand models, one might expect to see job losses occur across the whole economy and in each sector in a fairly proportionate way. Everything should just shrink, and average productivity should remain the same. But that’s not what happened. In many firms, hours fell faster than output and productivity increased a lot. That means that this time firms went to effort to identify the gold and the dross, and then disproportionately got rid of dross. What’s worse is that while some of those people weren’t dross, everybody knows that people who lost their jobs and became unemployed were disproportionately dross, and so other firms were reluctant to hire them back, based on this statistical generalization. And that means re-employing those people was a different problem from the one of past recessions.

Some of the JOLTS data bears this out too. Firms were hiring, but disproportionately from pools of college graduates on the one hand and people who already had jobs and were just switching companies on the other. They weren’t hiring in anything like a similar proportional rate from the giant reserve army of unemployed.

One quibble with this data is that something like that might still show up as an artifact in some large firms that are just indiscriminately firing line workers, but which can’t cut overhead in headquarters, where people are paid the most. Still, I’m guessing that’s probably a minor issue.

VII. There are plenty of new, if lower paying jobs. However:

There are plenty of lower-paying jobs in the world, more than ever before, but here are the rather significant catches: 1. A lot of those jobs are being created overseas. If the job does not require high and complex capital investment, the advantage to keeping that job in the United States is lower. 2. A lot of Americans are not ready to take such job, either financially or psychologically. They have been conditioned to expect “jobs in the middle,” precisely the area that is falling away. 3. Through law and regulation, the United States is increasing the cost of hiring, whether it be mandated health benefits, risk of lawsuits, or higher minimum wages. It is hard to escape the conclusion that unemployed young workers will only slowly be reemployed. And the jobs they get will often have considerably lower wages.

VIII. Freelancers and self-employed and food trucks:

Rono Economou … is a typical story. She was laid off [from] her well-paying job at a large Manhattan law firm … After some soul-searching, she responded by opening Boubouki, a small Greek food stall … she wakes up at 5:30am, lift a lot of heavy bags, and can’t afford to miss a day of work. It’s not clear her project will succeed financially, much less bring her riches, and it also doesn’t seem that her life is freer. A lot of future jobs will look like this – that is, they will look more like the jobs we already see in great numbers in developing countries.

Over time we can expect these categories to blur, and freelancing jobs will become increasingly respectable and indeed normal, if only because they offer a bit of pay and a bit of personal freedom too. More workers will think of themselves as free agents, and more employers will be keener to make hires without traditional benefits packages being attached to the job offers.

If the law lets them anyway, and allows independent contractor relationships without benefits, overtime, or guaranteed regular hours.

However, I have a friend who despite being quite gifted was just a bad fit, personality-wise, for the boring daily grind of a middle-management bureaucrat. Too restless and athletic and temperamental. Not the family man type at all; just a little too much wild blood in there.

He ended up quitting his civil servant job and driving Uber / Lyft full time. He was making good money for a while, and now he makes ok money, enough to get by in an expensive city, though of course with no pension and only the minimum health care plan.

It’s a lot less than he was making in his regular job, and his net per ride is decreasing as the situation gets more competitive and the market-makers turn the screws. Also there is volatility and seasonality and he has had to bust his ass to avoid hitting the wall a few times. Also, if he ever does decide to try and settle down and rejoin the workforce, then he’s got a whole lot of Uber on his resume, and no references.

Still, if he’s got enough money saved up, and he gets an invitation or sudden opportunity or just a feeling and wants to take two weeks off on a whim to go climbing or biking or to pursue some love affair, or just go on a bender, he doesn’t have to ask anybody for permission. He just goes on the spur of the moment. He waits for those last-minute crazy international deals out of his airport and if the price is right he just goes. If he runs low on cash or wants to make some indulgent purchase, he can increase his hours whenever he wants. If he gets insomnia or some chick flakes on him or something, boom, he can just turn on the app and start making money then and there. He’s actually met a girl this way once. He says, “I have no supervisors or clients and no one is responsible for me but me. I am a free man. I love it.”

To him, this kind of life is satisfying, if hard, low paying, and low status, but it feels like it is still full of dignity because of his independence and freedom from having to submit to anybody or any schedule other than his own. I am almost of opposite temperament to him in many ways, but even I’ll admit that there are times when that grass looks a little greener and I slightly envy his flexibility. But I’m fairly certain his standard of living simply must descend, eventually, to that of his most hungry competitor. Which is to say, next to nothing.

IX. Threshold earners, and their culture:

Today, many of these young earners are threshold earners, meaning earners who are content just to get by and who do not push ambitiously for a higher wage or stronger credentials at every step. Williamsburg, Brooklyn is full of young threshold earners, although rising rents are starting to push them out into the other parts of the city …

… it is commonly recognized that a lot of the young denizens simply aren’t striving after very much, at least not in terms of commercial job opportunities.

X. Summary

Overall, these job market trends are bringing: 1. Higher pay for bosses

2. More focus on morale in the workplace

3. Greater demands for conscientious and obedient workers

4. Greater inequality at the top

5. Big gains for the cognitive elite

6. A lot of freelancing in the services sector

7. Some tough scrambles for workers without a lot of skills. Those are essential characteristic of the coming American labor markets, the new world of work.

HANDLE:

Part II: What Games Are Teaching Us

Chapter 4: New Work, Old Game

I. Gaming is a huge part of the economy now, bigger than Hollywood, which is an under-reported and under-recognized fact. They also emphasize rapid processing of large amounts of information, which is what will be especially valuable in the future.

II. Watching how intelligent machines play and eventually beat the top masters in Chess (and recently, Go) provides a rich source of data about the tendencies, strengths and weakness of human decision making.

In the past, without the ability to ‘verify’ whether a decision was really the ‘correct’ one through exhaustive computational analysis, only other top human experts could evaluate a decision by another top human expert. And they would have to use their fallible heuristic intuitions to do so. What’s more, if there is something common about brain architecture that makes these intuitive engines fail in common ways, the expert consensus will evaluate another expert’s ‘wrong’ move as right.

But now we can really scrutinize these things with the help of machines, and as a result we can learn about the power and limits of our own brains, or, at least, the best our species has at the moment before the Chinese pay Stephen Hsu to use CRISPR to create biological super-intelligent super-alpha-Han or something.

III. Computers make a lot of ‘ugly’ moves, that feel wrong or weird to the pattern-recognition heuristic intuitions of most human players, and which full of complexity and mystery.

Chess grandmasters have coined a phrase – “That’s a computer move” – to describe those ugly, counterintuitive decisions made by computers, the moves that surely appear wrong. Yet the machines that produce those ugly moves beat the grandmasters virtually every time. … The moves of the machines show, regularly, how puny and unreliable our intuitions are, even if we spend our decades studying chess. It makes you wonder if the same is true about the rest of our lives.

IV. Partnering with machine-intelligent advisors and playing to win in broader human contexts.

It may get riskier yet, as the computers are programmed to play an active, tactical game. The computer is programmed to play for a win, not a draw. We can imagine competing intelligent-machine companies offering programs that seek out an active advantage in a typical human situation. No one rises to the top of the business world by breaking even on a lot of deals, and no one successfully woos a lot of women, or marries the right one, by acting “just okay” or neutral. People know that they need to take chances in complex situations, and they will buy tactical computer programs that help them do this. We’re going to generate a lot of hairy, very complicated personal interactions, driven by real-time data analysis and computer intelligence. Average is over. Some real-world interactions will become a lot simpler and call for conservatism and simple rule-following behavior, while others will become far more complicated and extreme. The case for keeping it simple is plain: Just do what the machine tells you. Avoid mistakes, hang on to your job, your relationship, your portfolio, or whatever it is you are trying to preserve. Defer to the authority of the beast with the intellectual brute force.

HANDLE:

Part II, Chapter 5: Our Freestyle Future:

I. A lot more chess analogy stuff. Freestyle (or ‘advanced chess’ or ‘cyborg’ or ‘centaur’) is human+machine teams playing other human+machine teams. If H is human and M is machine, you can imagine the possible competitions as:

H v H (traditional)

H v M (human playing computer, will always lose now)

M v M (software playing itself or another piece of software)

H+M v H (Freestyler versus human, human alone should always lose)

H+M v M (Freestyler versus some software, maybe different software. If it’s the same software, and the human is expert, then the Freestyler should probably win.)

H+M v H+M (Freestyle competition)

I am leaving out the complication of there being multiple humans, or multiple machines, or both, with maybe a team of people trying to figure out how to decide between multiple options when different chess engines give different answers.

And that’s the example Cowen uses with the champion British diversity-poster-children team of Anson Williams (Afro-Caribbean), Nelson Hernandez (looks Spanish to me), and Yingheng Chen (Anson’s girlfriend).

Anson, when playing, is in perpetual motion, rushing back and forth from one machine to another, as Freestyle chess is, according to team member Nelson, “all about processing as much computer information as rapidly as possible.”

Freestyle teams study the opening moves their machine opponents have made in previous games because, as Kasparov has observed, an initial advantage in Freestyle chess usually means an eventual victory. The players also know the weaknesses of particular engines and how one engine can at times offset the weaknesses of another.

Well, I suppose what we have here are kind of a meta level of intuitive heuristics, bolstered by ‘intense probing’ of each engine’s offered move when there is a ‘evaluation flip’ opportunity. How do the humans know about these strengths and weaknesses and make decisions? Why aren’t those intuitions susceptible to the same problems as in standard chess? My point is, why can’t that be automated in a similar fashion by a kind of meta-chess engine. Of course, if you have meta-chess engine software, combined with all the other engines, you have a super-engine. And maybe then we need to go one level higher in the hierarchy of engines and meta-engines, and meta-meta-engines …

II. More chess …

III. Having good and quick memory is important in these games, and in the real world too

Indeed, in plenty of real-world situations the immediate command over factual or analytical material brings a big edge. Discussions in meetings, strategies and reactions during sales calls, lawyers arguing in front of a jury, and managers in volatile, voices-raised personnel situations all try to draw upon preprocessed information at a moment’s notice. In all those cases, it matters more and more what workers have learned from the computer, or not, and how well they remember computer-derived information and advice.

IV. Another real-world ‘freestyle’ combo example. Medical diagnosis.

For over 20 years there have been automated imaging systems for histology and pathology and cell screening, such as in pap smears, blood draws, and biopsy samples. It’s also true in radiology. There’s also been automated EKG analyzing software to help cardiologists for longer than that. (In fact, little Handle once worked in the cold room at a local university hospital where the ‘supercomputer’ did exactly that, reading the data off old cassette tapes and making preliminary diagnosis, often very well, sometimes comically wrong.)

These systems are under the ‘supervision’ of an expert human and try to divide things up by ‘comparative advantage’, but really there is some feedback and complementary cross-checking to compensate for the other party’s weaknesses.

For pap smears, there can be lots and lots of cells, and the computer never misses a potentially abnormal one, but it has a false positive a lot of the time. It sends only these images to the doctor who studies them more closely. But that processing is done over thousands of cells and completed in a flash, which saves the docs lots of valuable time. The ‘team’ works like this:

Machine: Many, Easy. Filters down to:

Human: Few, ‘Hard’

Of course, what is ‘easy’ or ‘hard’ is a matter of the latest software and how smart it is which, in the short term, is probably also a matter of expense. But as machines get smarter and faster, the margin will keep shifting until eventually the human doctor isn’t ‘needed’ at all. That is, he adds zero value to making the results statistically more reliable.

V. On the other things, it will be hard to replace human agents for some diagnostic tasks, because of GIGO problems and that whole poker-like ‘reading the state of a human mind’ problem.

One medical innovation would run a patient’s reported symptoms through a Watson-like software program and see what might be wrong, drawing upon extensive databases. But can the computer ask follow-up questions to the patient properly or guess where the patient might be lying or exaggerating in the description of symptoms? … Not anytime soon, and so we are back to collaboration.

Actually, I think the computers might be pretty good at figuring out if someone is lying or exaggerating, or helping tip off the doctor or nurse.

VI. Implications for credentialed guild professionals:

It is clear that for the collaboration to work, we need to have a very smart machine. But, if the machine is already in place and plugged in, how expert does the human have to be? When the worker has to be a highly paid physician, a collaborative team can be costly, even if it improved health outcomes. The world – not the mention the American Medical Association – is pretty far from accepting this fact, but the person working with the computer doesn’t have to be a doctor or even a medical expert. She has to be good at understanding and correcting the computer’s mistakes, which is a very different skill.

VII. We are already running a kind of experiment of automated diagnosis given symptom descriptions with Google, which never had to ask anyone permission to do it, and which, apparently, isn’t liable for this going wrong. Which it must have done plenty of times. Again, ‘the tech exception’. But lots of people are typing in their inquiries and getting, it turns out, ok diagnoses.

The study did not consider the possible costs of incorrect or misleading results, so we’re still far from evaluating this rather large-scale experiment in new medical institutions. If nothing else, it’s an argument for proceeding with more regularized and authorized forms of the collaborative approach in medicine. We already have computerized doctors, and that illustrates the power of information technology to spread rapidly; the next question is how good and how reliable our mechanical medical servants are going to be.

VIII. Broader Lessons

1. Human-computer teams are the best teams

2. The person working the smart machine doesn’t have to be expert in the task at hand

3. Below some critical level of skill, adding a man to the machine will make the team less effective than the machine working alone.

4. Knowing one’s own limits is more important than it used to be. We also can use the concept of man-machine collaboration to define the difference between a worthless or “zero marginal product” worker and a potentially valuable worker. The worthless worker is one whose cooperation with the machine makes the final outcome worse rather than better. A potentially valuable worker offers the promise of improving the machine, taken alone.

VLADIMIR:

Clearly, immigration is the elephant in the room that Cowen is doing his best to ignore. Another is that a policy of protectionism of domestic workers against having their wages crushed by overseas competition (except for narrow politically powerful special interests) is no longer really in the Overton window, and he doesn’t even stop to consider if this might change.

Moreover, he doesn’t get to the core of the problem of what’s really so bad about being poor in the New Economy. The only really important aspect of this problem he does mention is real estate. But clearly, the much worse issue is the social Coming Apart that’s taking place along with the economic Average is Over, i.e. the fact that one must be somewhere in the upper deciles of the wealth and status distribution to avoid being stuck in horrifying social chaos and decivilization. Alas, even with Cowen’s skills, it’s just not possible to talk about this meaningfully without going too far in obvious crimethink directions.

Despite all that, I think Cowen is pretty much correct with all his observations, and the fundamental trends he identifies would still be taking place even if their consequences weren’t further exacerbated by immigration and overseas labor competition. As libertarians like to point out when they argue in favor of free trade and open borders, imports and immigrants — ignoring the political and other externalities of the latter — are just like technology. The argument is supposed to work by appealing to the unquestionable goodness of the latter, which supposedly only crazy Luddites could dispute. But of course, properly understood, it actually demonstrates that economic changes brought by technology itself may at least in principle have bad consequences for a majority of the population. Even if that had not actually been the case for the last few centuries, things may be changing now.

SPANDRELL:

No Coming Apart in Japan. 3.5% unemployment. No immigration. No real estate boom. Increased centralization in Tokyo, yes, but nothing too dramatic. You can buy a decent house for 300k in a Tokyo suburb.

Wages have stagnated since the peak, but inequality hasn’t risen that much. No great increase in compensation for managers, CEOs and all that. Certainly no boom in financial services. Salaries for traditionally lucrative guilds such as law and doctors are if anything decreasing, especially law after the bar exam was made easier.

I see a lot of assertions about automation and human+machine teams which sound edgy and fun and make it sound like a sci-fi novel where governments are weak and engineers determine the future; but I’m not seeing much of a thorough argument.

I’ll make the obvious objection: Everything is Politics. Average is Over happened because of the particular political choices of USG. It could have happened differently. It could still change. Nothing inevitable about it.

VLADIMIR:

4. Tech is improving faster than other sectors because more lightly regulated.

It’s remarkable that the word “technology” in its popular shortened form (“tech”) has come to mean “things done in Silicon Valley.” There seems to be a popular assumption, not altogether inaccurate, that nothing else happens any more in the realm of technology that’s not totally stagnant and uninteresting.

As a fascinating look into the past as a foreign country, I recommend the story “The Chief Research Chemist of the Metaplast Corporation” from Richard Feynman’s autobiography.Outside of the computer industry, I don’t think it’s possible to imagine anything like this happening today: a smart guy given the freedom to tinker around and improve and invent things, unrestrained by an onerous and rigid bureaucratic process and superiors trembling at the prospect of regulatory and legal repercussions at every step. It’s a sobering thought when people mention all this economic deregulation and liberalization that’s supposedly taken place since the 1970s.

VLADIMIR:

Spandrell: Assuming your description of Japan is accurate, then according to you, what exactly were the political and other factors that have made Japan different?

I can see the following possibilities (not mutually exclusive) off the top of my head:

(1) Lack of mass immigration combined with demographic shrinkage has made the supply of low- and mid-skilled labor stagnant or falling just in time to compensate for the falling demand due to technology.

(2) As a highly cohesive and disciplined society, Japan implements protectionism and makework exceptionally well, ensuring continued prosperity even for those classes that would otherwise lose out badly in the AOE economy.

(3) Similar to Germans, the Japanese are so exceptionally productive that capital is still chasing them wherever they live, preventing the Great Centralization from playing out in a severe form. (What exactly do people in Japanese small towns do these days that’s competitive in the global economy, rather than being propped up by the state?)

(4) The Average is Over/Coming Apart trends are not just due to economic factors, but also because the old Malthusian farmer cultural and social capital is eroded further with each generation that lives in cushy prosperity, so there’s a bifurcation between the successful upper percentiles who continue with the old farmer ethic, being somehow unaffected by this degeneracy due to genes or lucky circumstances, and the lower classes who are reverting to a savage state. Maybe due to culture and/or HBD the Japanese just have the old farmer ethic ingrained much deeper.

(5) The Japanese have never done much except dull and uninspired (if still very successful) copying of American technology and institutions, and their Asiatic conformity prevents any exceptional individuals from sticking out. So there just isn’t any market for exceptional performers who would form the successful right peak of the bimodal AOE distribution.

(6) A variation on (6): maybe there are exceptional performers, but the Asiatic conformity dictates that they must not stick out in terms of compensation. (This isn’t so far-fetched: for all the frantic talk about rising inequality in the U.S., my experience is that income distribution in the corporate world is, if anything, quite communist, due to factors I don’t quite understand. People who are more productive and have greater responsibilities get paid more, but nowhere near in proportion to how much their contribution and responsibility is greater.)

Anything else?

SPANDRELL:

(1) There’s something to it. Also IT adoption is fairly low. Most Japanese offices haven’t changed that much and are fairly unproductive, but nobody seems interested in changing that. Candide has some insight on how fucked up the local IT industry is.

(2) Legally speaking Japan is hardly protectionist, but the industry is set up so that imports don’t sell well. Part of that is public policy part of that is the complex guild-like structures that dominate the economy and keep makework a thing.

(3) I live in a very, very small town, inside a very small province. Don’t get me wrong; young people are leaving, Centralization is a thing. But a small thing. 80% of young people stay, and they have plenty of jobs. Lots of industry, large and small. All Japanese industry is in the countryside, more or less. A large part of that is due to public policy and sheer pork; the Japanese politician class are mostly rural, to this day.

(4) I don’t think that’s it. Denmark has been prosperous for longer than Greece. Degeneracy is a matter of public policy too. Japanese don’t do drugs because they get life imprisonment. Single mothers don’t get welfare easily. Japanese law doesn’t subsidize degeneracy. I blame France for White savagery, in Asia it just doesn’t happen. Athrelon had a good article about how in Asia the lower class is well behaved.

(5) Conformity is certainly a thing. But I don’t see what those exceptional individuals in the US are doing that justifies coming apart in the whole West. As Sailer puts it most recent Silicon Valley innovations are just regulatory arbitrage (this is funny too). Asian regulators are not into arbitrage. They set up guilds decades ago and they enforce them.

(6) This is absolutely a thing. The Japanese are well aware that some workers are awesome and many are semi useless; yet everybody has to make a living. If anything very productive people are forced to work more than anyone else. They get paid more, but not vastly more. In the end the purpose of compensation is bragging rights. In Japan you get massive bragging rights with a $200k salary. Out of the chart bragging rights. You can look down on everybody. Do you really need a yacht?

So in summary:

1. The rent-seeking structure the state set up in the 1950s still remains in place. No regulatory arbitrage, no disruption. You can’t make rich by destroying a whole industry. They won’t let you.

2. Investor influence on management is nil. Management culture in Japan is collegial, they have their old culture of collective management and there are limits to executive pay. People are *very* sensitive to the difference between a $200k salary and a $190k salary.

3. The State is very invested in the rural areas and won’t let them fall. Money is running out, so they’re going to have to do something, but if Centralization is to happen they’ll take care it happens slowly.

4. No anarcho-tyranny. No drugs. No petty crime. Little immigration, and the few there is happens in Dubai-style semi-slavery terms. So the “flight to civilization” component of Centralization in the West doesn’t exist here.

Basically Japan (and I guess Germany) doesn’t like AIO. To the degree that the information economy makes AIO easy to happen, Japanese public policy buts effective barriers. You can’t stop it completely, but you can slow it fairly well. Seems to me that Anglosphere policy is actively accelerating AIO. And Russian or Chinese policy it’s pushing it even further.

Given the omnipresent status of the modern state, one can’t talk of the economy in a vacuum. Economic forces work inside the framework of state policy. Anglo state policy enables AIO, Asian state policy staves AIO, Third World state policy captures AIO. There’s nothing inevitable about it. There is no vacuum, there is always a choice.

DIVIDUALIST:

You can learn a lot about an economy if you look at business software. How much it values productivity, and do people really work efficiently or not. Humans will make mistakes and sometimes use the wrong post code, etc. Ideally business/ERP/accounting software – the order processing, invoicing module – would allow you to preview and even print an invoice in order to review and amend it before you finally post it to the accounting books or finalize it, where it become unchangeable. Or it would allow a simple, one-click crediting the invoice and making a new, editable invoice copying all the data if you realize the mistake too late. Now if you look at Denmark, where 90% of the businesses use Navision / Dynamics-NAV, it is best summarized as “LOL WUT I don’t even.” Zero opportunity for human scrutiny after processing. No preview, you have no friggin’ idea how an invoice will look like before you actually post and finalize it, and one-click crediting was just introduced in 2015, before that, for 20 years, they had nothing. Do Scandinavians even wörk?

This may look like a tiny thing, but the sensibility of the software is a signal of how local employers regard employee productivity which should normally be correlated with GDP because what else. This is how I don’t understand why Denmark is (comparatively) rich.

LESSER BULL:

I do not know if the Danes are actually that inefficient. But if they are, my thought would be that destruction is easier than construction. So it takes a lot of efficiency to make up for wrecking things through bad personal traits. If you don’t wreck things, you can be pretty prosperous without too much effort.

HENRY DAMPIER:

This is one reason why professional jobs in the 1950s and 60s were relatively calm. Women were happy working secretarial jobs, so the e-mail hell of today was handled by typists and secretaries handling memos. The memo volume was probably similar in terms of cognitive load to our current e-mail load, but now we expect everyone to handle an inane frenzy that keeps people paying attention to work after hours.

It’s also pretty common for professional women to either do nothing or to do worse than nothing by scheduling tons of expensive meetings. Then, the productive people have to work overtime uncompensated to make up for the slack in productivity.

We also have women being overhired in junior positions, overgroomed for promotion, who then tend to leave the workforce in enormous numbers at various biological breakpoint ages — which is the problem that “Lean In” was supposed to try to address. This makes the entire long term structure of the labor force seriously discombobulated.

We’ve also lost specialization in many office settings — now everyone is expected to type, whereas before, with even less efficient technology, we had specialized typists.

HANDLE:

Handle’s corollary to Parkinson’s Law: Behaviors that tend to lengthen the time needed to finish a project increase to the productivity available.

CANDIDE:

I’m about a third of the way through the book and I don’t like it much. Cowen is too glib to my taste (I have no gift for glibness, so this might just be envy on my part). Content-wise, I feel he got his bearings wrong through an unfortunate focus on one example of man-machine collaboration — namely chess — which is very untypical for having a clear-cut, objective goal (winning or losing) and ways of telling whether you reached it or not, as well as taking place in an extremely circumscribed environment with no unquantifiable factors and “unknown unknowns”. Once these two restrictions are removed, enhanced computation speed stops being nearly as useful (as, indeed, Cowen notes even about chess programs in the opening when the programs are operated without opening databases) and tends to function as a mirror for the operator’s prejudices, like a modern haruspex.

HANDLE:

To be fair, he says something very close to this in Part II, Chapter 6,

CANDIDE:

I don’t know where you read that. He did make remarks about the peculiarity of chess as a field of endeavor in Chapter 7, but his takeaway was nothing like what I wrote above, but rather that we’ll try to make the world more like chess — sliced and diced to be easy for software to work with. He makes a good observation that companies tend to offload some formerly clerical tasks on customers, lowering productivity. Also, I disagree about his take on humans “learning to override their intuition through use of/experience with computers”. This may partly stem from a conflation of “intuition” and “cognitive biases”, parallel and related to the conflation of “religion” and “superstition”. The chess players don’t “override their intuition”, they tune it in a different way. System II cognition is not really separable from System I cognition, the former is built out of and upon the latter.

ETA: Finished the book. The science chapter was rather bad, in my opinion, because Cowen’s philosophy is faulty. I have mentioned his misuse of “intuition” above. In the context of science, he uses “understanding” to mean something on the order of “corresponding to common sense derived from everyday experience”. In fact, it is quite possible and normal for one to pick up intuitions and quantum-mechanical common sense in the course of learning physics, and if this quantum-mechanical common sense does not jibe with everyday-experience common sense then so what*. One can still apply that common sense and those intuitions to solving problems, which is what counts. The same goes for more abstract mathematical structures. Cowen might have made a much better case for himself if he picked up on the problem of proof-by-computer (the map-coloring theorem is a well-known example) where a sizeable proportion of mathematicians feels doubtful whether this constitutes proof at all. If one takes away the “genius machines” and the pro-immigration stance, the last chapters are a passable, if relatively uncontroversial and not novel, argument in favor of the “Brazilification” version of American future.

* To pick up a mundane example, I think and believe that astronauts staying in space for long periods, e.g. at the ISS, quickly develop a separate set of common-sense intuitions related to their everyday experience, where water does not spill, objects don’t fall downwards etc. It would be interesting to know how quickly they adjust between these two sets.

HANDLE:

Here is a related paper of his from last year: Economic Development in an “Average is Over” World

SPANDRELL:

I don’t get it. What’s he saying, that the Third World doesn’t need to make stuff because Samsung sells cheap phones?

Well, cool, every single dude in Africa has a smartphone. He still doesn’t have a toilet, nor a proper house, nor a job. Isn’t that what development used to be about? Have they redefined that to mean having a facebook account?

ASDF:

Cowen has been pretty straightforward about this. He believes the world will be composed of a vast tannish underclass living in barrios with substandard infrastructure and shortages of anything that has some supply bottleneck. They will have access to cheap internet media and some trickle down mass produced goods, but won’t have great access to the things you mentioned. Above them will be the elite and perhaps 10-15% of people making up the professional/engineering class to run the robots and create high end goods.

He puts a positive spin on it, but he doesn’t lie about what he’s pushing for.

HENRY DAMPIER:

Why is deindustrialization seen as a point of success? Having a close loop — culturally, legally, physically, economically — between design and manufacturing is helpful to businesses rather than harmful.

Also even though the proportions of manufacturing employees go down, in absolute numbers, they are going up, even those employed effectively by the same company. “Radical insourcing” could only happen with more manufacturing expertise rather than less. If Boston Dynamics can’t even produce a useful robot, the imaginary academic manufacturing engineering community may not be as useful as the actual manufacturing community which is building expertise in China, Taiwan, etc. and not here.

The proposed radical automation stuff assumes that the useless prototypes routinely touted in magazines like Popular Science/Mechanics that never result in any usable products are going to suddenly become useful any day now.

He also misunderstands the nature of ‘Free’ software:

First, measured gdp won’t pick up free goods such as Facebook and Google.

Bzzzzzt. Neither of these things are free. Merchants pay for Facebook and Google. They’re the primary users: the other people are just along for the ride. And that activity is indeed picked up by GDP.

It also may not require much in the way of cultural changes or transformations, as most cultures in the developing world already are sympathetic to higher personal consumption. The almost obsessive pro-saving, pro-education ethics which evolved during the East Asian miracles need not be repeated for this growth path.

More clever-silliness. To consume, there must be savings.

Further, the digital economies are not actually all that digital. Amazon runs on UPS. UPS drives its trucks on publicly financed highways. The homes that it ships packages to benefit from advanced infrastructure and a network of blue collar labor that keeps those homes well maintained. Not even free apps can make all that much money from third world users because unlike in countries like China where there is some infrastructure, it’s awfully hard to ship packages to them. Extrapolating that economies can then be digital-first is profoundly confused.

The few countries that do manage something like this, like Estonia, have some unusual characteristics that aren’t shared by the Philippines or Central Asia. So even attempting to generalize a theory of economic development like this is almost a total waste of time, especially because individual political factors are going to be much more important than an attempt to understand which way the Hegelian groundhog is going to twitch his nose next year.

To his credit he makes some gestures towards this.

Most goods are still physical because humans live in the physical world. Even digital products tend to only be as valuable as the tangible objects that they describe.

So for example, even though Salesforce is a digital product, the services that it provides its customers are, at most at a few hops, about tracking the movements and ownership status of physical goods of some kind. Even if the Salesforce rep is selling software to the guy who sells software to the guy who is the regional manager for John Deere who ships John Deere tractors to the physical retailers, the original value down the chain came from the creation of that tractor. And the value of those digital transactions is effectively capped by the physical goods.

I guess people look at Nike and see that the manufacturing only cost $10 and the shipping costed $2.50 averaged out in bulk and that the pair of sneakers retails for $125, but that sorta ignores that there is no $125 sneaker without the physical sneakers, and you can’t build the cult of belief around the magic powers of those sneakers without the physical items themselves.

That’s a roundabout way of saying that even though non-manufacturing activities often make up most of the value of higher end products, that doesn’t mean that the manufacturing itself is of minor consequence. Especially for higher end products, total control over materials sourcing and manufacturing process is important. You can’t get that with enormous language and ocean barriers.

Shrugging your shoulders at the loss of American competitive advantage and saying “great, maybe the Kazakhs will do it for us, because reform here is unthinkable” is setting the country up to become another backwards polyglot republic at a time when it might have been possible to reverse direction from the 1970s.

The ‘why’ of deindustrialization also seems to handwave away the impacts of environmental regulation and other factors that actually motivate companies to offshore manufacturing. The reduction in industrial employment happens in high-regulation states, while the growth happens in low-regulation states. Rationalizing non-competitiveness as progress seems to be counterproductive.

CANDIDE:

It’s much more difficult to deliver toilets and proper houses than smartphones and second-hand clothes. The economic value-added of average people has been falling steadily towards negative territory, and as First-worlders aren’t allowed to get their kicks from mission civilisatrice and ruling anymore, there’s no incentive to deliver or maintain services in Third-world-like locations. And I’m not sure how much labor-related policies like Moldbug proposed in “Letter to France” and “Dire problem12</1>” would help, since demand by cognitive elite is limited.

DIVIDUALIST:

The smartphone production is done in the third world basically by having very capable management who rules workers with an iron fist, maintaining military discipline.

SPANDRELL:

No, no, no. Smartphone production isn’t done in the third world. It’s done in China. The Chinese are serious and disciplined, genetically so; it doesn’t take that much to put a Chinese to work, let alone Chinese women, who are the majority at Foxconn production lines. Foxconn bosses aren’t military types. They just pay well, and enforce lots of small petty rules to break resistance. It’s like a primary school more than like a military institution.

There’s no way in hell that smartphones or anything at that level of complexity could be built in India, the Middle East or Africa.

VLADIMIR:

Content-wise, I feel he got his bearings wrong through an unfortunate focus on one example of man-machine collaboration — namely chess — which is very untypical for having a clear-cut, objective goal (winning or losing) and ways of telling whether you reached it or not, as well as taking place in an extremely circumscribed environment with no unquantifiable factors and “unknown unknowns”. Once these two restrictions are removed, enhanced computation speed stops being nearly as useful (as, indeed, Cowen notes even about chess programs in the opening when the programs are operated without opening databases) and tends to function as a mirror for the operator’s prejudices, like a modern haruspex.

That’s certain to happen if there is no reality feedback from competition. But if there is competition, then the haruspexes get outcompeted by those who are able to recognize when they should trust their judgment and when to defer to the machine. (The latter sort of ability is one of Cowen’s key points, which is in my view a very accurate insight.)

It’s similar to how, for example, statistical methods in the hands of academics usually become a tool for producing ideological propaganda and impressive-looking nonsense that’s good only for padding one’s publication resume. But this doesn’t mean that similar methods are worthless when used in industry, or that one could compete in these industries without using them.

HANDLE:

Part II, Chapter 6: Why Intuition Isn’t Helping You Get a Job

You could rename this chapter “[some] Intuition is Overrated,” or even, “Intuition is Over.”

However, as with most things, I think it would be wise to split intuitive instincts into what you might call core social instincts. On the one hand, there are human evaluation instincts such as “woman’s intuition” in sensing underlying discrepancy between her counterparty’s presented self and his or her actual perceived self. On the other hand, there are experienced-based, pattern-perceiving intuitive heuristics. An examples of the latter could be the representativeness heuristic, a potential failure mode of which is said to be the ‘clustering illusion’.

I. Romance

Cowen starts with romance – “we take a lot of wrong turns in our pursuit of a good partner” – but that is of course a very tricky subject fraught with landmines, and so he’s wise to just leave it at that.

And yes, I understand this makes the point more salient to many people in his audience who adhere to a deluded understanding of the mate-choosing process and about the underlying mechanisms of various relationship failure modes. One can’t even get close to subject of discussing human sexual nature without touching the most sensitive and hysteria-inducing triggers.

But to slightly more enlightened readers, blaming modern relationship failures on rationality failures akin to the deviations of chess grandmasters from the best computer programs is a strikingly … misleading … or at least importantly incomplete, analogy.

The issue is tricky, of course, because of the definition of the ‘good’ in this context. That is, the unarticulated end or purpose for which these partners would ‘good’. Of course modern ‘romance’ is a kind of a hybrid or intermittent oscillation between forager and farmer modes of courtship, mating, and family formation, with a serious ‘time inconsistent preferences’ problem, to put it mildly, and one about which it is impossible to have a profitably frank discussion in public these days.

My point is simply that romantic intuitions are not like chess intuitions in ways too important to make this ‘good partner screening’ analogy work for me. And going further, in chess, the obvious goal is to win, or at least not lose. But people lack full conscious awareness of their romantic goals, and have horrible understandings of the situations that will make them happy romantically (e.g. the hundred bullet-point list of partner essentials, ‘you never know what you need until you get it’, and so forth.) Thankfully, most people end up settling into a new romantic equilibrium and forgetting all the nonsense they once ‘believed’ about what was indispensable. That’s a good and important function of ‘Schelling Amnesia’ – socially useful memory retconning.

But if you are trying to ‘help’ people by forcing them to collapse this vague and deluded cloud of sentiments and (mis)conceptions into some concrete set of goals and preferences, and then have a computer produce ‘advice’ as if these goals were stable and permanent things and it were really essential that they be met, then the computer (and by its influence, the person using the computer) are going to take seriously what ought not to be taken seriously.

You risk locking a person into exactly the kind of bad ideas and bad strategies that cause women to hold out too long for Mr. Right, while at the same time not actually ensuring that the priority of effort is placed on maximizing their ‘market value’, so to speak. Now that would be unwelcome but possibly still helpful advice, “the most important thing for you to do right now if you want to land and keep a good husband is lose weight and stop being such a nag,” but if Apple has got to make Siri give officially PC answers, then who is going to be able to capitalize on a RealTalk app?

Part of the old matchmaker system was a more wise and enlightened understanding of the nature of romance and what it takes to make happy marriages (in a supportive, reinforcing, all-encompassing social environment), and of course, a very liberal dose of paternalism. But computer-assisted hyper-individualism is as likely to lead men and women astray as they might soon think ‘faulty intuition’ does in the unaided context.

But Cowen says the matching software could nudge people away from these bad ‘hold out’ strategies too, thus helping them avoid the pitfalls of too much nudge-free, self-reliant individualism. And speaking of injecting a palatable dose of reality into mainstream discussion without triggering crimethink instincts, check this out:

But it is easy to recognize the value of having a matchmaker nudge the indecisive into, well, growing up. You can’t keep shopping forever, though humans have long seemed to want to do so. It is common to continue sampling profiles and dreaming on about a perfect mate but not actually dating anyone. Surely this is not the best use of technology. Perhaps most importantly now, dating algorithm technology can help us realize the errors in some of personally generated choices.

Yeah, but dating algorithm technology like Tinder can keep attractive women in their fertile prime on the carousel until theirs 30’s, constantly exposed to occasional addictively thrilling tastes of five minutes of alpha and bombarded with the lusty attention of thousands of potential suitors, turning the social-media false-consciousness of being an “ultra-hot mini-celebrity” knob all the way to 11, thus ruining them for mellowed stable marriages with ordinary loyal providers but with a comparative paucity of drama and passion.

A final note on modern romance. Who discriminates?

Years later, when doing the research for this book, I read that the scientists at Match.com have discovered that a conservative is, on average, more willing to email a profile listed as politically liberal than vice versa.

Sounds plausible, though I’d like to see the methodology.

II. Some Behavioral Economics / Psychology / Nudge Thoughts.

Biases, such as toward the familiar, are something we have long tried to overcome. In the growing field of behavioral economics, researchers measure the biases behind individual choices as judged by some external standard. We’ve learned – or we think we’ve learned – that individuals overestimate their degree of influence over events, and anchor too much on one piece of information when making decisions, among many other human errors and biases. The last time I looked, the list of cognitive biases on Wikipedia had forty-eight entries. But even after all this work and all this evidence, nagging questions remain. When it comes to measuring a human bias, are we sure that the researcher is correct and that the individual choice is wrong? I have a lot of funny habits that I think serve me pretty well. … We see similar dilemmas in the more systematic literature. Remember that old rule of thumb “A bird in the hand is worth two in the bush”? Economists often consider it a “bias” that we value commodities we already own much more than commodities we might acquire (this bias is known as the “endowment effect”). But for all its apparent irrationality, maybe this is an inescapable part of anyone’s ability to be loyal to friend and family. Maybe part of true loyalty is that we can’t always apply it selectively on a moment’s notice. In that case, these endowment effects may be an essential part of the good life rather than a signal of our irrationality. I’m not saying we know this for sure, only that the models we economists device don’t really settle it.

This is very reminiscent of the whole infamous “Fat man and the runaway trolley” problem that we are also told is evidence of an irrational bias. But insurmountable reluctance and hesitation to commit personal and lethal violence against some random innocent stranger in cold blood – and a potentially dangerous large male at that! – and even in such a ‘lesser evil’ circumstances, is probably also an indispensable ingredient in the social recipe of ‘the good life’.

And of course, as we’ve discussed in the game theory threads, many behaviors that may seem irrational from a certain, narrow perspective are in fact perfectly rational when one grasps the role they play in the big picture of managing human disputes.

When economists investigate human rationality, they are often too dependent on arbitrary stipulations about what is rational and what is not, expressed in the form of models. An economist must write down some mathematical axioms and then find that human behavior falls short of these axioms. But how convincing were the axioms in the first place for complex and multidimensional human problem solving? A lot of the research in this tradition isn’t convincing, no matter how brilliant the investigator. Other economists rely on artificially constructed laboratory investigations to try to measure human rationality or lack thereof. They use inexperienced undergraduate subjects, who are not always taking the problem-solving exercises seriously, and the prizes for good performance are relatively small.

One should also keep Szabo’s point about fallacies mistaking short for long games in mind when assessing these studies.

The nice thing about computer chess is that we have clear-cut standards, albeit not perfect standards, for good chess moves and bad chess moves. So, putting human play through the lens of the engines, what do we learn about human intuition?

III. Testing, understanding – and maybe one day exploiting (to whose benefit and detriment?) – patterns of human intuition by data-mining archives of human calculation decisions and comparing them to the computer’s answers. Today in chess, tomorrow … everything?

Ken Regan was a chess prodigy (like Cowen) who became an Oxford-minted Mathematician working on the infamous P-NP problem, but has in recent years been conducting some of the most rigorous investigations in the manner described above.

Some of those observations:

III.A. Humans are ‘contempt averse’ and try to avoid (if I’m interpreting this right) “unfamiliar complications without a perception of plausible paths to victory – or even out of deadlock – within their the horizon of their experience or ability to calculate.”

III.B.

A player is least likely to make a major error when the game is tight, and if anything, players do their absolute best when they are faced with a slight disadvantage in their position.

III.C. There is not much evidence for a Nassim Taleb “Black Swan” model of cognitive failure.

Most games are decided on the basis of the accumulation of advantages, and the level of error is fairly well predicted by the relative skills of the players.

III.D. Players have consistent ‘styles’. For example, Kramnik is highly accurate ‘for a human’, but he doesn’t try to exploit psychological-based tactics against his opponents to throw them off their games by purposefully generating ‘unfamiliar complications’ where, in addition to the difficulty of the decision, their frustration, emotions, and ego will cause them to make more mistakes than usual. The polar opposite and currently most ‘nettlesome’ player is said to be prodigy Magnus Carlsen, of whom Cowen is something of a fan-boy (though in this hero worship of Carlsen he is hardly alone in the chess world, and this kind of hero-worship or real heroes is fine by me.)

III.E.

The striking fact about chess is how hard it is for humans to play it well. The output from the programs shows that we are making mistakes on a very large number of moves. Ken’s measures show that even top grandmasters, except at the very peaks of their performances, are fortunate to match Rybka’s recommendations 55 prcent of the time. … Rajlich [Rybka’s creator] stresses that humans blunder constantly, that it is hard to be objective, hard to keep concentrating, and hard to calculate a large number of variations with exactness. he is not talking here about the club patzer but rather the top grandmasters: “I am surprised how far they are from perfection.” In earlier times these grandmasters had a kind of aura about them among the chess-viewing public, but in the days of the programs the top grandmasters now command less respect.

III.F. Humans also seem to prefer thinking about things – especially competitions – in “drama narratives aligning with the story patterns which most humans instinctively experience as particularly salient”. Or maybe a Sapir-Whorf-style constructed conceptualization in terms of the way they categorize (and mischaracterize) what is really ‘going on’ in certain situations. But we’ll revisit that a little later when I talk about Go. But for now, it’s important to note that this may give humans a kind of ‘Schelling Point’ advantage over computers in certain human-to-human match-ups if one’s intuitions align well with the opponents’ intuitions, which allows one to make essential – and more accurate – inferences regarding mental states and motives. This is something the current approaches to machine learning has a very hard time accomplishing up to now.

It might be possible to get programs to learn to be better players of chess or Go by playing other machines – or copies of their own software – over and over. Theoretically, it could use those ‘lessons’ to get ever better and faster at totally crushing humans in man-machine play too. But if you had a program play itself in poker some other simulation of a human competition and it might not actually learn anything very useful about how to get better at beating humans in person. It’s easy to get a machine to play the odds based on the cards on the table, it’s hard to get them to play the humans, based on human factors. So far, anyway.

All this talk about improving on the intuitions of experienced experts reminds me a little of the ‘Moneyball’ revolution, at least in baseball. Or maybe I should say “the story of the Moneyball revolution”, since there is still plenty of debate and controversy about the matter, which is further complicated by the fact that when everybody adopts Moneyball tactics in the arms-race for victory, then Moneyball stops looking as effective as it did when it was newly innovated and used by only one or a few teams as a temporary way to exploit an opportunity in the marketplace.

The “story of Moneyball” is that hero amateur enthusiast Bill James practically single-handedly realized that coaches and managers were making hiring, pay, and fielding decisions based on little more than gut hunches and some crude, individualized statistics (hits, base runs, RBI’s, etc.) that may reflect individual glory but didn’t add up synergistically to achieve the mission of maximizing team wins and championships. So he took a look at all the numbers in all the databases and tried to discover new metrics like ‘win shares’ which were much more statistically reliable and could guide these decisions. When Brad Pitt started actually dumping intuition-based decision-making and using these guides in a dynamic and continuously-refined manner his team suddenly started winning a lot more. Yay, score one for statistics and computers and boo human ‘expert’ intuition.

But in addition to arms-race complications (and, ahem, the steroids era, and also the explosion in player salaries which attracts much better athletic talent on average) there is still a lot of debate about whether all this was really any good. After all, one cannot escape the problem of ‘Garbage In, Garbage Out’, and there are plenty of important human things (like having the right personality to be a good team player who enhances morale) which can be observed and perceived by coaches, managers, and scouts, but which are never measured or captured in the game statistics and so won’t show up in the regression analysis.

Another example could be noticing that a player is one of those athletes who peaks too early and then will wear out fast and get injured too severely, too many times. Should you play him into the ground now, chewing him up and spitting him out, or maybe pace him so he’s more of a long-term investment and maybe stays healthy long enough to really hit his stride later on? Or maybe the guy is just a thug deep down and, whatever the numbers say, is destined to get into serious trouble eventually and in a way that will really hurt the team at a critical time. Worth taking that chance? Will the computer know how to look for thugs? Would it be legal to teach it to, even if it could? I anticipate that the law of ‘discriminatory algorithms’ and what must and must not be done about them is going to be a very hot area in the years to come. Someone should write a legal note on it now (besides Orin Kerr this time).

Anyway, a lot of the articles critical of Moneyball these days have as a thesis something along the lines of, “On second thought, and taking another look, and now that we know the whole human story which wasn’t reported at the time or known outside a small circle of insiders, it turns out that maybe human intuition about a lot of these things wasn’t so bad after all. And is still really important and, at least at present, not easily replaceable with more ‘data science’.”

Remember the example of the doctor who knows how to tell when his patient is lying or exaggerating or omitting important facts, vs. the computer who merely records and processes everything inputted on the form as if they were genuinely presented symptoms of an underlying disease.

And, it seems to me, such confusion and controversy is likely to remain the case in these very human instances with a huge number of dynamic and subtly interacting variables, low signal-to-noise measurement problems, and a huge amount of ‘causal density’. The big trick according to Cowen is how to have the machine and the man be good complements, focusing on their respective comparative advantages and correcting the weaknesses of their ‘teammate’. But teasing out which teammate is better at which function in these complicated circumstances won’t always be straightforward.

However, I think the economic incentive is big and permanent enough that eventually this ‘division of labor’ will be optimized one way or the other – even through just a lot of dumb trial and error – and mostly what the humans will be doing is ‘assessing the human factor’ and “being a pleasant (and effective / manipulative / motivating / inspiring) human interactor when a particular experience of human interaction is desired (thus more useful and profitable).” That’s taking ‘customer service’ and ‘customer experience’ to a whole new level. We are all Geishas now. Geishas to other Geishas. And at this point Cowen might dryly quip, “Those new service sector jobs.” But that probably is the future of labor, and, of course, the qualities needed to do these things really well are both rare and very hard to teach.

IV. Circling around to the point about ‘Nudge’

So what? Haven’t thousands of articles from psychology and behavioral economics outlined major weaknesses in human perception and decision-making abilities? There are the works of Daniel Kahneman, Dan Ariely, and many others. Haven’t we all heard about “nudge,” the concept so eloquently outlined by Cass Sunstein and Richard Thaler? In that worldview, experts know the biases of other decision makers and design the choice architecture to manipulate better human choices, such as changing the default options for which pension plan you will enroll in. Yes, but the chess result differs. Computer chess is pointing out some imperfections in the world’s experts, or you might say it is pointing out imperfections in those who, in other contexts, might be nudgers themselves. … It is precisely our reasoned, considered judgments that we should be more suspicious of.

Well, ok, but then why not let Rybka nudge us? And maybe not ‘us’ all at once with some one-size-fits-all society-wide ‘policy’, but individually, with some app that takes our specific, current circumstances and particular preferences and goals into account, and gives everyone the unique and personally-tailored nudge? Isn’t the idea to have the machine in the man-machine team tell the man what it ‘thinks’ the man ought to do? If a human chess expert/scholar like Ken Regan tells us that, if we want to maximize our odds of winning, we should do whatever some computer program tells us, or even ‘nudges’ us into following the computer’s ‘provably superior’ advice, then what’s wrong with that?

So, this needs a big bit of context that Cowen leaves out, which is the whole (largle Libertarian) counter-attack against the Nudge™ intellectual enterprise. Which follows the Cato Clever™ strategy. When the progressives start barking up some novel, dangerous tree, it’s tempting to take the bait and challenge their assumptions and worldview head on. But that never works, because challenging these assumptions is sure to get one tarred as either a bigot, moron, or an eye-rollingly naive geek who is totally out of touch with the latest science of human reality.

So the Cato Clever™ strategy is to posture as if one accepts all the false things which much be publicly accepted – and even embrace, celebrate, and cheerlead for them – but then to find and relentlessly pursue some oblique line of attack.

In the case of Nudge, the strategy path seems to be what one might call ‘Counter-Mandarinism’, or applying correctives to the naive progressive faith in ‘scientific’ experts to rationally guide the populace and the economy towards some kind of optimal equilibrium. The same general road was later taken in the field of ‘Public Choice’ (and now ‘behavioral public choice‘) and Academics, Experts, and Mandarins put in charge of studying, designing, implementing, and enforcing the new Nudge requirements are sure to have less than impressive results – certainly less than advertised – because of the inherent limits to any system of empowered government scientific experts tasked with management of human decisions and behavior.

V. “Signs of Improvement”

An example of ‘bad intuition’ is that, for what are well-understood psychological reasons, male chess players noticeably increase their risk-taking and aggressiveness when paired with an attractive female opponent, “though not in a manner that increases his chances of winning the game.” If they are relying too much on intuition without some

“coldly-calculating disinterested supervision” (such as that provided by an intelligent machine), then they may not consciously realize they are thinking with the wrong head.

And it seems, in part due to the increased competitive pressures and deeper insights brought about by the introduction of chess computers to the scene, that chess players are learning to avoid some bad moves that many players used to intuitively perceive as g