How To Pick A Quack: Theory

A few weeks ago I posted on the 25 most common types of clues mentioned in “How to pick an X” web guides for these 18 types of experts (sorted by ave # clue types per guide):

Lawyer, Private Eye, Therapist, Accountant, Chiropractor, Auto Repair, Doctor, Music Teacher, College, Dentist, Financial Advisor, Interior Decorator, Astrologer, Hedge Fund, Pastor, Charity, Health Plan, Fortune Teller

I said:

Guides do not often mention outcome-related clues, presumably as few customers attend to them. In general, we can’t tell if a type of expert X is a “quack”, where “better” versions don’t help customers much more with outcomes, by the kind of clues people use to pick X. Maybe most people can’t tell the difference.

Let me elaborate here, and describe what sort of expert incentives are produced by customers using each type of clue. (Next to each clue is the % of guides mentioning that clue.) A key question to keep in mind is how, if at all, use of that clue plays out differently if this type of expert is a “quack” who provides no more customer value than reassuring customers that they cared for, are high status, and are doing what most people think is what you are supposed to do for their problem. Continue reading "How To Pick A Quack: Theory" »

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The World Forager Elite

My last post was on Where’s My Flying Car?, which argues that changing US attitudes created a tsunami of reluctance and regulation that killed nuclear power, planes, and ate the future that could have been. This explanation, however, has a problem: if there are many dozens of nations, how can regulation in one nation kill a tech? Why would regulatory choices be so strongly correlated across nations? If nations compete, won’t one nation forgoing a tech advantage make others all the more eager to try it?

Now as nuclear power tech is close to nuclear weapon tech, maybe major powers exerted strong pressures re how others pursued nuclear power. Also, those techs are high and require large scales, limiting how many nations could feasibly do them differently.

But we also see high global correlation for many other kinds of regulation. For example, as Hazlett explains, the US started out with a reasonable property approach to spectrum, but then Hoover broke that on purpose, to create a problem he could solve via nationalization, thereby gaining political power that helped him become U.S. president. Pretty much all other nations then copied this bad US approach, instead of the better prior property approach, and kept doing so for many decades.

The world has mostly copied bad US approaches to over-regulating planes as well. We also see regulatory convergence in topics like human cloning; many had speculated that China would be defy the consensus elsewhere against it, but that turned out not to be true. Public prediction markets on interesting topics seems to be blocked by regulations almost everywhere, and insider trading laws are most everywhere an obstacle to internal corporate markets.

Back in February we saw a dramatic example of world regulatory coordination. Around the world public health authorities were talking about treating this virus like they had treated all the others in the last few decades. But then world elites talked a lot, and suddenly they all agreed that this virus must be treated differently, such as with lockdowns and masks. Most public health authorities quickly caved, and then most of the world adopted the same policies. Contrarian alternatives like variolation, challenge trials, and cheap fast lower-reliability tests have also been rejected everywhere; small experiments have not even been allowed.

One possible explanation for all this convergence is that regulators are just following what is obviously the best policy. But if you dig into the details you will quickly see that the usual policies are not at all obviously right. Often, they seem obviously wrong. And having all the regulatory bodies suddenly change at once, even when no new strong evidence appeared, seems especially telling.

It seems to me that we instead have a strong world culture of regulators, driven by a stronger world culture of elites. Elites all over the world talk, and then form a consensus, and then authorities everywhere are pressured into following that consensus. Regulators most everywhere are quite reluctant to deviate from what most other regulators are doing; they’ll be blamed far more for failures if they deviate. If elites talk some more, and change their consensus, then authorities must then change their polices. On topic X, the usual experts on X are part of that conversation, but often elites overrule them, or choose contrarians from among them, and insist on something other than what most X experts recommend.

This looks a lot like the ancient forager system of conflict resolution within bands. Forager bands would gossip about a problem, come to a consensus about what to do, and then everyone would just do that. Because each one would lose status if they didn’t. In this system, there were no formal rules, and on the surface everyone had an equal say, though in fact some people had a lot more prestige and thus a lot more influence.

This world system also looks new – I doubt this description applied as well to the world centuries or millennia ago, even within smaller regions. So this looks like another way in which our world has become more forager-like over the last few centuries, as we’ve felt more rich and safe. Big world wars probably cut into this feeling, so there was probably a big jump in the few decades after WWII, helping to explain the big change in attitudes ~1970.

Elites like to talk about this system as if it were “democratic”, so that any faction that opposes it “undermines democracy”. And it is true that this system isn’t run by a central command structure. But it is also far from egalitarian. It embodies a huge inequality of influence, even if individuals within it claim that they are mainly driven by trying to help the world, or “the little guy”.

This system seems a big obstacle for my hopes to create better policy institutions driven by expert understanding of institutions, and to get trials to test and develop such things. Because as soon as any policy choice seems important, such by triggering moral feelings, world elite culture feels free to gossip and then pressure authorities to adopt whatever solution their gossip prefers. Experts can only influence policy via their prestige. Very prestigious types of experts, such as in physics, can win, especially on topics about which world elites care little. But otherwise, elite gossip wins, whenever it bothers to generate an opinion.

That is, the global Overton window isn’t much wider than are local Overton windows, and often excludes a lot of valuable options.

Notice that in this kind of world, policy has varied far more across time than across space. Context and fashion change with time, and then elites sometimes change their minds. So perhaps my hopes for policy experiments must wait for the long run. Or for a fall of forager values, such as seems likely in an Age of Em. Alas neither I nor my allies have sufficient prestige to push elites to favor our proposals.

Added 11p: It seems to me that the actual degree of experimentation and variance in policy is far below optimum in this conformist sort of policy world. We are greatly failing to try out as many alternatives as fast as we should to find out what works best. And we are failing to listen enough to our best experts, and instead too often going with the opinions of well-educated but amateur world elites.

Added4p: As John Nye reminds me, in the early years of a new tech, only a few nations in the world may be able to pursue it. They then set the initial standards of regulation. Later, more nations may be able to participate, but risk-averse regulators may feel shy about defying widely adopted initial standards.

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Elois Ate Your Flying Car

J Storrs Hall’s book Where Is My Flying Car?: A Memoir of Future Past, told me new things I didn’t know about flying cars. The book is long, and says many things about tech and the future, including some with which I disagree. But his main thesis is a contrarian one that I’ve heard many times from engineers over my lifetime. Which is good, because by putting it all in one place, I can now tell you about it, and tell you that I agree:

We have had a very long-term trend in history going back at least to the Newcomen and Savery engines of 300 years ago, a steady trend of about 7% per year growth in usable energy available to our civilization. …

One invariant in futurism before roughly 1980 was that predictions of social change overestimated, and of technological change underestimated, what actually happened. Now this invariant itself has been broken. With the notable exception of information technology, technological change has slowed and social change has mounted its crazy horse. …

In the 1970s, the centuries-long growth trend in energy (the “Henry Adams curve”) flatlined. Most of the techno-predictions from 50s and 60s SF had assumed, at least implicitly, that it would continue. The failed predictions strongly correlate to dependence on plentiful energy. American investment and innovation in transportation languished; no new developments of comparable impact have succeeded highways and airliners. …

The war on cars was handed off from beatniks to bureaucrats in the 70s. Supersonic flight was banned. Bridge building had peaked in the 1960s. … The nuclear industry found its costs jacked up by an order of magnitude and was essentially frozen in place. Interest and research in nuclear physics languished. … Green fundamentalism has become the unofficial state church of the US (and to an even greater extent Western Europe). …

In technological terms, bottom line is simple: we could very easily have flying cars today. Indeed we could have had them in 1950, but for the Depression and WWII. The proximate reason we don’t have them now is the Henry Adams curve flatline; the reasons for the flatline have taken a whole book to explore. We have let complacent nay-sayers metamorphose from pundits uttering “It can’t be done” predictions a century ago, into bureaucrats uttering “It won’t be done” prescriptions today. …

Nanotech would enable cheap home isotopic separation. Short of that, it would enable the productivity of the entire US military-industrial complex in an area the size of, say, Singapore. It’s available to anyone who has the sense to follow Feynman’s pathway and work in productive machinery instead of ivory-tower tiddley-winks. The amount of capital needed for a decent start is probably similar to a well-equipped dentist’s office.

If our pre-1970 energy use trend had continued, we’d now use ~30 times as much energy per person, mostly via nuclear power. Which is enough energy for cheap small flying cars. The raw fuel cost of nuclear power is crazy cheap; almost all the cost today is for reactors to convert power, a cost that has been made and kept high via crazy regulation and liability. Like the crazy restrictive regulations that now limit innovation in cars and planes, destroyed the small plane market, and prevented the arrival of flying cars.

Anything that goes into a certificated airplane costs ten times what the thing would otherwise. (As a pilot and airplane owner, I have personal experience of this.) It’s a lot like the high cost of human medical drugs compared with the very same drugs for veterinary use.… Building of airports remains so regulated (not just by the FAA) that only one major new one (KDEN) has been built [since 1990]. …

It seems virtually certain that if we had had [recent] cultural and regulatory environment … from, say, 1910, the development of universal private automobiles would have been suppressed. … By the end of the 70s there was virtually nothing about a car that was not dictated by regulation.

With nuclear power, we’d have had far more space activity by now. Without it, most innovation in energy intensive things has gone into energy efficiency, and into smaller ecological footprints. Which has cut growth and prevented many things. The crazy regulation that killed nuclear energy is quite unjustified, not only because according to standard estimates nuclear causes far fewer deaths, but also because standard estimates are greatly inflated via wide use of a “linear no threshold model”, regarding which there are great doubts:

Several places are known in Iran, India and Europe [with high] natural background radiation … However, there is no evidence of increased cancers or other health problems arising from these high natural levels. The millions of nuclear workers that have been monitored closely for 50 years have no higher cancer mortality than the general population but have had up to ten times the average dose. People living in Colorado and Wyoming have twice the annual dose as those in Los Angeles, but have lower cancer rates. Misasa hot springs in western Honshu, a Japan Heritage site, attracts people due to having high levels of radium, with health effects long claimed, and in a 1992 study the local residents’ cancer death rate was half the Japan average.

To explain this dramatic change of regulation and litigation, Hall says culture changed:

Western culture had essentially succeeded in supplying the needs of the physical layers of [Maslow’s] hierarchy, including the security of a well-run society; and that the shift to the Eloi [of the Well’s Time Machine story] could be thought of as people beginning to take those things—the Leave It To Beaver suburban life—for granted, and beginning to spend the bulk of their energy, efforts, and concerns on the love, esteem, and self-actualization levels. … “Make Love, Not War” slogan of the 60s … neatly sums up the Eloi shift from bravery to sensuality. …

The nuclear umbrella meant that economic, political, and moral strength of the society was no longer at a premium.

I’ll say more about explaining this cultural change in another post.

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Honest Putdowns

What do people want? Surely one thing they want is to not be insulted or put down by others. Yet if you ask people what personal features they most aspire to improve, the features they pick have no correlation with the features used in the putdowns around them! Yet putdown features have strong correlations with the features used in praise, and with the features people say they care most what others think of them. In this post, I’ll describe and interpret these new results, concluding that putdowns are our best guide to what really counts for status; aspirations are deluded, and not to be trusted.

So let’s start. Recently on Twitter I asked people for the damning descriptors that most lower folks’ status in their world. I then collect 32 such putdowns, identified the key human feature to which each referred, and then taxed the patience of my Twitter followers by posting five sets of 24 polls, with each poll comparing four of these 32 features. (Each feature appears in exactly 3 polls in each set.)

Each poll set corresponds to a different choice criteria. For the first criteria, Putdowns, I asked which feature was most often used for putdowns in their world. The next three criteria are: Praise asks which feature is most used for praise or admiration, Aspirations asks which you most aspire to improve in yourself, and ImageSeek asks for which you care most what others think of you. The last criteria TryLookBad asks for which feature (e.g., fart rate) it is most plausibly has an issue of it looking bad to try hard.

For each criteria, I fit (via min squared error) poll % responses to a simple model wherein some % of responses are random, and the rest are in proportion to the relative (positive) “priority” of each feature. The following table shows, for each criteria, the average number of responses per poll (Ave Poll N), the average root mean square error (RMSE in %) of its model in estimating poll % responses, and correlations between its prioritizes and priorities of other criteria. The correlations in red have t-stats of over 4.

Note that TryBadLook seems to have just failed as a poll question, with large errors and weak correlations; many just misunderstood it. Praise and ImageSeek are quite strongly correlated with each other and are similarly correlated with Putdowns, though only Praise is weakly correlated (t-stat 1.37) with Aspirations.

The most striking result, shown in bold, is that priorities for Putdowns and Aspirations are uncorrelated! You might think that since people don’t like to be insulted, they’d aspire more to look better on vulnerable features. But no. To help explore this puzzle, here are the best fit relative priorities for all 32 features and five questions, sorted by the difference between Aspirations and Putdowns priorities. (The % priorities for each criteria add to 100%.)

The pro-Aspiration top of the list has features like wealth, creative, brave, and articulate, that impress observers even if observers don’t value them as much. And it has features like productive and effort, which we’d like to convince others are a high priority for us. I do not at all believe that these two features are actually most people’s highest priority for improvement.

At the pro-Putdowns bottom of the list are features like menacing, biased, sanity, pleasant, and honest, which people see as important in others but not worth of improving in themselves. Plausibly, people convince others that they are not the type of folks at risk of ranking poorly on such features, so there is little need to work at them. Or, admitting that they are working on them would admit they have problems with them. It seems that people are also reluctant to admit they might have a problem with insufficient smarts.

In the middle of the list are features, like breadth, curious, professional, and generous, that most people pretend to care more about than they actually do. They are neither damning enough to be worth more putdowns, nor valued enough to be worth more aspiration. Note that features like liked and attractive plausibly matter less in putdowns because audiences for putdowns don’t like to admit that they care about them as much as they do. Not also that in another poll, respondents said 3-to-1 that criticism influences reputations more than does praise, with a majority saying it does so far more.

As these interpretations of the PutdownsAspirations differences mostly blame Aspirations for being less than honest, I conclude that the priorities of Putdowns are a more accurate measure of the true determinants of status than are the other measures above. Praise and ImageSeek are closer than Aspirations, but they are also polluted, Praise by the tendency to flatter people on the features on they want to be praised, and ImageSeek by our delusions regarding what failures are plausible for us.

Putdowns show what features really determine status, and aspirations can’t be trusted, as we care a lot more about status than we care to admit.

Yes of course it would be nice to check that these results hold for larger poll pools, and to see how they might vary with different subcultures.

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Specialized Innovation Is Easier

Consider a few things we know about task specialization and innovation: Larger cities and larger firms both have both more specialization and more (i.e., faster) innovation. More global industries also have both more specialization and innovation. And across the great eras of human history (animal, forager, farmer, industry), each era has brought more specialization, and also faster rates of innovation.

Here’s a simple explanation for (part of) this widely observed correlation: It is easier to create tools and procedures to improve tasks the more detail you know about them, and the less that task context varies across the task category. (It is also easier to fully automate such tasks; human level generality is very hard.)

For example, it seems harder to find a way to make a 1% improvement in a generic truck, designed to take any type or size of stuff any distance over any type of road, in any type of weather, relative to a very specific type of truck, such as for carrying animals, oil, cars, ice cream, etc. It gets even easier if you specialize to particular distances, roads, weather, etc. Partly this is because most ways to improve the generic truck will also apply to specialized trucks, but the reverse isn’t true.

This might sound obvious, but note that this is not our usual explanation for these correlations in each context. We usually say that cities are more innovative because they allow more chance interactions that generate ideas, not because they are more specialized. We say larger firms are more innovative because they have larger market shares, and so internalize more of the gains from innovation. We say more global industries are more capital intensive, and capital innovates faster. And we say that it is just a coincidence that over time we have both specialized more and invented better ways to innovate.

My simpler more unified explanation suggests that, more often than we have previously realized, specialization is the key to innovation. So we should look more to finding better ways to specialize to promote future innovation. Such as less product variety and more remote work.

Added 25Sep: A relevant quote:

As Frank Knight once expressed it, the fundamental point about the division of labour is that it is also a system for increasing the efficiency of learning and thus the growth of knowledge

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Time Reversal In Tenet

The mew movie Tenet shows plenty of eye-candy action and charismatic characters, though it didn’t really make me care much emotionally about its characters or their problems. The movie did, however, scratch my big physics idea itch. So I’m going to talk a bit about the movie’s key physics premise. I’ll give spoilers; you are warned. Continue reading "Time Reversal In Tenet" »

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Our Brave New Merged World

AGI isn’t coming in the next thirty years. Neither are Moon or Mars colonies, or starships. Or immortality. Or nano-assemblers or ems. Cities won’t be flooded due to CO2, a nuclear war won’t devastate civilization, aliens won’t arrive in the skies, and a religious jihad won’t remake culture. The rates of change in the economy, lifespans, fertility, automation, and non-carbon energy will stay about the same. Quantum computing, 3D printing, and crypto-commerce will grow but remain small. There won’t even be that many flying or self-driving cars. So if you are looking for science-fiction-level excitement re dramatic changes over this period, due to a big change we can foresee today, you’ll be disappointed.

Unless maybe you look at remote work. (Yeah, its not the best name; “work from home” may be better. But I’ll stick with the US standard name here.)

I’ve written about how remote work can, for many industries, allow the merging of many local markets into fewer much larger, often global, markets. If this potential is realized, big changes will result. In this post, I’ll outline many of them.

First and foremost, this is a more productive and richer world that would otherwise exist. It has some mix of more stuff and experiences, and a wider variety of such things. Workers are more specialized, and so take longer to train. Firms are also more specialized, and are larger on average, with wider geographic scope.

This is also a more standardized homogenous world. Products and services are more the same around the world, as are workers and their tools, culture, and training (including school). Regulation of work and product quality is also more standard, as are currency and accounting units. Variety becomes more across professions and industries, and less across places.

This is also a more equal world. Places that resist integration and standardization will suffer, and that is likely to especially include the richest and poorest places. But for the rest, market integration will ensure more equal wages and prices for products and services. As usual, this will help those whom the prior fragmentation hurt, and hurt those whom the prior fragmentation helped. And everyone will gain from new economies of scale and scope. Of course real persistent productivity differences, such as across regions or demographic groups, should continue to induce real wage differences.

There will be a boom in key enabling tech and infrastructure, such as avatars, avatar tools, home controllers, home offices, and low-latency bandwidth. And also a boom in personal habits and skills better suited to this world, such as introversion, self-discipline, self-motivation, and writing. (Ph.D.s look better.) And there will be a relative bust in tech and infrastructure that supported prior work habits, such as offices, high rises, work suits, parking garages, freeways, and cars. And also old-style work habits and skills. New generations who learn the new ways early will also gain.

As jobs will less force people to move, people will move areas less often, and the areas where people live will be less set by jobs. As life at work will be less social, people will have to get more of their socializing from elsewhere. Some of this will come from remote socializing, but much will still probably come from in-person socializing. So people will choose where they live more based on family, friends, leisure activities, and non-work social connections. Churches, clubs, and shared interest socializing will increase in importance. People will also pick where to live more based on climate, price, and views. Beach towns will boom, and the largest cities will lose.

Because people will move areas less often, the social connections they make in school will last them longer into life. Yet today school is widely talked about as a preparation for work. So schools will be torn between wanting to be in-person to promote local social connections, and remote to promote work skills. Perhaps schools will split, with core work-related classes being remote but electives and “after school activities” being in-person. Work hours will be less rigid, and it will be easier to do non-work tasks during usual work times.

Because of their stronger social solidarity, local areas dominated by family and informal social ties seem better suited to provide social insurance, such as medical, retirement, and unemployment benefits. But if so they should arrange for global reinsurance, to deal with risks that could hurt whole areas together. And firms might resist, having long offered social insurance to induce worker loyalties.

The firms in this new world are larger, full of foreigners, create weaker personal bonds via less in-person contact, and sit in more competitive markets for workers, customers, and investors. Local and national governments also find them harder to regulate, and the world will probably fail to coordinate to create strong global governance. These global firms may thus find it harder to get workers and customers to trust them. And so such firms may try harder to create clearer track records and incentive contracts, as substitutes for regulation and loyalties based on area or personal connection.

Some smaller firms will advertise their connections to particular areas as ways to gain allegiance from local customers and workers. But larger firms will find that doing so puts off too many customers and workers from other places. Most such firms will thus seek an international brand, not tied to particular areas. As a result, they are likely to seek to create generic customer facing workers, not clearly associated with particular areas. They might even use simple machine learning to map local worker appearances, in image or sound, into more standard appearances. For example, they might remove distinctive local accents.

Most work interactions will be recorded, making rule-violating discrimination, sexual harassment, and conspiracies harder at work. But those things will still be common in person among friends, family, clubs, etc. Automation will be promoted by the fact that tasks done remotely satisfy more prerequisites of automation. Large datasets of workers doing tasks would be created, and remote service workers could easily invoke an automation to do a small task, often without clients even noticing the difference. Even so, automation progress will continue to be slow.

Some firms may retain in-person work and interactions at their headquarters, with personnel drawn more from in-person social networks and schools in a nearby local area. Even if this puts such firms at a global disadvantage, local political games may make it hard for outsiders to get in. At the other extreme, some workers may be so unskilled and poor as to not make it worth the bother to have them work remotely. Remote works seems to most help folks in the middle: middle professions and middle nations.

While fictional depictions of remote service work often try to make it seem alien, harsh, and degrading, everyone is more likely to work to make it seem natural, friendly, and comfortable. For example, instead of people wearing goggles that completely obscure their view of their immediate world while they immerse themselves in distant work, workers are more likely to see both worlds in overlay. Somewhat like how through a window you might see both the world outside and a refection of your room. And avatars that deal directly with customers are likely to prominently show a human face and voice.

A brave new world of remote work awaits. It isn’t AGI or aliens, but it is a pretty big change, coming to your world in the next few decades.

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Remote Work Is Teleportation Lite

The division of labour is limited by the extent of the market. Adam Smith

Adam Smith gave the famous example of pins. He asserted that ten workers could produce 48,000 pins per day if each of eighteen specialized tasks was assigned to particular workers. Average productivity: 4,800 pins per worker per day. But absent the division of labor, a worker would be lucky to produce even one pin per day. (More)

Factories are dramatic examples of scale economies resulting from specialization. When you break up a large task into many small tasks, each to be repeated many times by a specialist, then you can do those tasks much faster and cheaper. Especially when the work product is very standardized, not varying much from case to case. Each worker can figure out just how to set up his/her task to do it best, with just the right angle, lighting, tools, and order of subtasks. And just the right people can be assigned to each task. Specialized tools can be designed for particular subtasks, and some subtasks can be automated. For example, car factories cars need only 14 hours of work to make a car, but if one person makes a car it takes them two months to two years.

Now consider car repair. Today, that process is mostly done via the same sort of general methods by which one person would make a car by themselves – much less efficient. Could we make more efficient car repair factories? Yes, if we had sufficient scale. If all the cars that were built the same (with same make & model) and had the same presenting problem could be lined up in front of a huge factory, then that repair task could be broken into many small tasks done by specialized workers. The problem is that it would be too expensive to ship all the cars of a particular model with a given problem to one location, and then there might not be enough of them at any one time to keep a big factory busy.

There is one car maintenance task that is needed often enough, and similar enough across car models, that we do actually make small factories: car washes. In large urban areas, there is enough demand to support small car wash factories near enough to each customer. And automated car washes are often substantially cheaper than hand car washes.

Now imagine that we had cheap teleportation; things disappear from one spot and immediately appear somewhere else on the globe. In this case we would be able to create larger repair and maintenance factories for each product, probably right next to the factory where that product is made. This would create stronger incentives to standardize products, to allow larger more specialized factories of this sort.

We could also make specialized factories for maintaining things that we don’t create. For example, with teleportation, there could be big facilities for grooming each particular type of pet. And veterinarians could specialize in dealing with each particular kind of animal and presenting problem. Making those services much cheaper.

All these gains would come because cheap teleportation would allow the entire world to become one big integrated market. Today larger cities are more efficient at most everything, and have more specialized services, such as more kinds of restaurants. With teleportation, the entire world becomes one big city, allowing vastly more specialization, which can be used either for more variety, as in the case of restaurants, or cheaper repair and maintenance, as in the case of specialized pet grooming and medical care.

Now consider remote work using advanced avatars. A robot can show up at your door, together with its large tool kit, or just sit in your closet. Larger more specialized toolkits and setups can sit ready a mile from your home. These all can be tele-operated by any human worker anywhere in the world. After years of practice, they can work the robots and tools remotely nearly as fluidly as they can work their own hands.

These remote workers and their avatar robots allow many of the efficiency advantages that teleportation would bring. That is, remote workers are teleportation lite. Instead of the item to be worked on moving through a factory space filled with different workstations run by different specialists, the item can stay in one place next to the same avatar that is controlled by a sequence of specialists, each doing their particular task. Instead of physical objects teleporting around, it is the workers who in effect teleport to each new task. This arrangement may limit the number and kind of specialized tools that can be brought to bear on any one task, but there’s otherwise no limit on how specialized the workers and their methods can be.

If the idea of a service factory seems fanciful to you, consider that they already exist for services that can be done by phone:

Starting in the 1980s, airlines, insurers, health care providers, credit card companies, and other enterprises began outsourcing labor-intensive back-office work to shared-services providers—at first close to home and then increasingly offshore—to cut costs and improve efficiency. In most sectors, this workbench extension was about as far as the transformation got. The majority of large service enterprises still use traditional factory layouts that rely on large numbers of people following workflows that are hardwired into legacy IT systems. … [This] traditional service factory works well in terms of reducing costs and enabling service companies to scale up. (More)

Recently I said:

Remote work is my guess for the most important neglected trend over the next 30 years. (At least of trends we can foresee now.)

My reasoning: remote work should greatly increase the division of labor. While today we live in a globally interconnected world, most industries and professions are not actually global. Some are national, some are regional, some are city-wide, and some are even more local. The smaller the market, the less specialization, and the less productive is an industry. So as remote work spreads, the geographical scope of many kinds of markets will increase, and thus so will their variety and productivity.

Customers will have the choice to spend the new remote work possibilities on more specialized products and services, or on more efficient versions of a smaller number of more standardized products and services. But either way, remote work can be a very big deal. In the long run, about much more than just saving on the costs of commuting or renting office space.

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Common Econ Critiques

Consider this critique of physics:

Once upon a time the universe was full of magic, mystery, and majesty, wherein humans lived organically and intuitively with nature. But then physicists (and their engineering minions) pretended to know far more than humans can ever know in an irreducibly complex universe. And they pretended to far more objectivity and neutrality in their inquiries than is possible for humans. Using impressive math, physicists rose in status, while other less mathy but more fluid and organic ways of thinking fell in status. Physics concepts became used more widely, displacing other useful and more human but now neglected ways of thinking.

Physicists are reductionist, and focus overwhelmingly on the simplest physical parameters of the smallest physical parts. So they ignore more interesting parameters and large scale organization. They study particular phenomena via vastly-over-simplified models that neglect most of the rich complexity of the real world. Worse, regarding the items that they do consider in their simple models, most of their assumptions are just wrong.

For example, standard models of mechanical systems assume that they sit in a flat space-time. Most materials are uniform, isotropic, solid with sharp boundaries, and uncharged. Ground anchors do not rotate or accelerate. Perfect vacuum sits between most adjacent parts, and between the other pairs is either an absolute bond or frictionless relative motion. Yet real mechanical systems sit in rotating, accelerating environments full of corrosive fluids and cosmic rays, at temperatures and pressures that often melt materials, and amid vibrations that often break them. And estimates of all physical parameters in such models are known to be wrong, i.e., not exactly correct. Physicists claim that such deviations make for only small errors in their final analysis, but how can they know that if they don’t model the full complexity?

Engineers who use physics tend to create system designs that are more like typical physics models, with a small number of simple parts having a few simple relations to one another. These systems are quite different from the fluid, complex, highly-interdependent rich-relation biological systems that we are, and once lived among. These physics-model-derived systems are harsh, ugly, fragile, uninspiring, and alienating. These systems may do well by simple physics metrics, but that neglects a vast space of better if less formal ways to evaluate systems.

The dominance of physics in engineer training and related government policy has unfairly neglected intuitive, magical, arty, and literary approaches to engineering system design. Approaches that look bad by physics metrics, but not by intuitive organic human ways to evaluate. Today the fields of “design” use better approaches, and are displacing the fields of “engineering”. It’s about time.

Here’s an obvious response:

For most products, few customers care much about how their systems are engineered, or the parameters by which they are described. So in a free competitive world, firms are free to offer products designed and evaluated via “intuitive, magical, arty, and literary approaches.” But few do. Yes, firms today also commonly use design as well as engineering, but mainly for a few relatively aesthetic choices close to the user experience. For at the vast majority of other choices, out of user sight, physics-based engineering dominates.

Physics winning this competition suggests that alternate approaches just aren’t as productive. Yes, there is often less free competition to woo government buyers, and physics-dominated regulations often demand that physics be used to prove that products are safe and effective. But consider that the world still has many competing nations, and engineering matters greatly in war, where simple physical parameters are quite meaningful. If a nation could build more effective weapons using other approaches to weapons design, they could win wars that way. The fact that few nations try is more evidence that physics-based approaches work better.

Yes, models greatly simplify. But for humans with some abstract understanding and greatly limited mental abilities of other sorts, approximation via simple modular models and designs is our main way to manage complexity. Nature faced different constraints, which is why her designs are different. Yes, simple modular designs can be harsh and alienating, but without them we could not create engineering designs nearly as capable. Humans just can’t do analysis without making a mass of simplifying, and thus wrong, assumptions. But the fact that our designs tend to work shows that our approximations tend to be appropriate. Yes of courses if we approximate badly, our models and designs will go badly. Which is why physicists and engineers pay so much attention to approximating well.

Now consider the many critiques of economics, which I’ve just spent many hours sampling. Most econ critiques are much like the above physics critique, making a similar response appropriate. But with one key difference, to be discussed at the end.

Before going into details, let’s review a few basics. Like physics, econ uses math to create a space of possible models. But instead of describing physical systems, econ models describe social systems. Economists have a standard set of assumptions that they see as most likely to be true, and other standard set of assumptions that seem easiest to analyze. Assumptions from the second set are often preferred, to allow entire models to be simple enough to analyze. Different economists explore different models, comparing them to each other and to data, and arguing about their relative accuracy as approximations. If you are arguing for different models in this topic area, but accepting that models are a reasonable way to think about social behavior, then you are doing econ. (And you might have a valid complaint re if your kind of econ gets a fair hearing.) Econ critics, in contrast, reject, or at lest minimize the value of, the whole econ approach to studying social behavior, and designing policy.

That said, let us now consider some common econ criticisms. Continue reading "Common Econ Critiques" »

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How To Pick A Quack: Data

How do we pick, or think we should pick, our experts? One clue comes from “How to pick an X” web guides. For 18 types of experts X, I searched for that phrase, and read the top 8 Google hits, noting all of the types of info clues mentioned in each guide. Here is the full table of results.

Here are the 25 most common clue types, sorted by the % of these guides in which each is mentioned:

Here are the 18 types of experts, sorted by the average number of clue types that their guides mention:

Looking at these tables, I hypothesized that guides might prefer to mention types of clues that we’d more want to use in making our choices, and that guides might mention more clues for kinds of experts where we worry more about choosing them well. So I’ve done a set of 16 Twitter polls to estimate these things for 16 types of experts and 16 type of clues.

Results to note:

  • Guides for 18 different types of experts vary by a factor of 3 in how many types of clues they mention.
  • The top 25 info clues vary by a factor of 12 in how often they are mentioned in guides.
  • While different clues are favored in guides for different types of experts, the overall pattern looks pretty random.
  • Only 7.8% of guides mention a top 25 clue directly sensitive to outcomes. (Ones marked in red above.)
  • The correlation between how many clues guides to X mention and how worried poll respondents are re pick X is strong: +0.40.
  • The correlation between how often guides mention a clue and how much poll respondents want to know it to pick is negative: -0.20. This is mainly because polls put the most weight on track records. My followers are probably less representative here, as that’s an issue I talk much about.

Guides do not often mention outcome-related clues, presumably as few customers attend to them. In general, we can’t tell if a type of expert X is a “quack”, where “better” versions don’t help customers much more with outcomes, by the kind of clues people use to pick X. Maybe most people can’t tell the difference.

So what explanations can you offer for any of the patterns you see?

Added: Here are the poll-based priorities each expert type and info clue: Continue reading "How To Pick A Quack: Data" »

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