Tag Archives: Innovation

Why Not For-Profit Government?

Centuries ago, most commerce and production was organized around individuals and small groups. Since then, we’ve seen enormous innovation in business forms and structures. Today, such forms are much larger and more complex, and they do most everything of importance in our world.

We have apparently learned a great deal about how to best run such orgs, and we expect to learn much more in the coming centuries. And one of the biggest things we’ve apparently learned is that businesses are usually most efficiently structured as for-profits, wherein owners can replace management, and get a share of net profits in trade for prior investments. (Also using many other modern business methods.) For example, in the US today only 14% of workers are employed by governments, and 10% by non-profits; for-profits employ the rest.

(Note standard econ theory explains why the winning form tends to be best for everyone, overall, not just best for investors.)

Over those last few centuries, we’ve also seen innovation in the organization of governments. But that evolution has been slower, and it hasn’t yet converged nearly as much on one main winning strategy; there are still many of different forms of government around. For example, only about half of nations today are considered democracies. And although many have wanted governments to displace for-profits in many areas of life, most such attempts tend to go badly; governments still do much less in our world than do for-profits.

In this context, you might think an obvious idea to try would be to organize governments as for-profit enterprises. That is, let investors choose managers and share profits from an organization that holds a monopoly of force over a geographic region. If for-profits are the best way to organize most smaller orgs, and if we aren’t sure how best to organize governments, why not try that most successful business form for them? On its face, this seems completely plausible. Yet we hardly ever hear of governments of this. Why not?

Well first note that a lot of people really hate the idea. In fact, rivals often accuse others sorts of governments of actually being for-profits behind the scenes, secretly run by investors who pull the strings. As if that would be such a terrible thing.

Second, note that this concept has long been a trope of dystopian science fiction:

A Mega-Corp is often a large, shadowy organization with a power base and structure that rivals even The Government. When you take it one step further, with the Mega Corp actually being the government during their Day of the Jackboot, you get … a “corporate state.” … A corporate state is a government run and organized like a business. … At the top is typically a board of executives (more likely than not corrupt…) which makes all the decisions; for the common people, the terms “citizen” and “customer” (or perhaps “employee” is more accurate) are more or less interchangeable. … It’s not uncommon for corpocracies in fiction to wield military power too … may employ Law Enforcement, Inc., or even own them outright as a subsidiary. (more)

Third, note that a for-profit government was actually tried at a pretty large scale, and quite early on, in the form of the British East India Company from 1600 to 1873, This seems to have successfully achieved the task it was assigned, of extracting wealth from distant colonies, and did this on average better than would have other forms of government of the time. It was ended due to a combination of discomfort with its assigned ask, and distaste for the very idea of for-profit government:

In response to the threat that the [British East India Company] posed to the state’s monopoly on governance, public opinion turned negative, and politicians argued that the East India Company had become a danger. In 1773, Parliament … curtailed Company shareholders’ influence and gave the government greater authority … In The Wealth of Nations, published in 1776, Adam Smith noted the “strange absurdity” of the Company having both “the character of the sovereign” and that of the “merchant.” Edmund Burke, a member of Parliament at the time, similarly called the Company a “state in the disguise of a merchant” in 1788. The 1784 East India Act further attempted to constrain the Company, … Parliament … nationalized the business in 1858. (more)

Fourth, note that the energy to start the United States came to a large extent from investors who stood to gain from it.

And fifth, let me admit that Curtis Yarvin, whom I once debated, seems to advocate something like this:

In Yarvin’s view, democratic governments are inefficient and wasteful and should be replaced with sovereign joint-stock corporations whose “shareholders” (large owners) elect an executive with total power, but who must serve at their pleasure. The executive, unencumbered by liberal-democratic procedures, could rule efficiently much like a CEO-monarch. (more)

Now you might think it obvious that citizens wouldn’t be sufficiently “protected” from being hurt by a for-profit government. But that doesn’t seem at all obvious to me, as I tried to explain in my last post. Most employees today are protected from employers much less by their government than by employers needing to offer attractive reputations in the face of competition. Also, most governments threaten citizens in many other ways. In addition, citizens could be part owners of a for-profit government, such as via owning direct shares and/or transferable citizenship.

This whole topic came to my mind because I recently visited Prospera, which is in many ways close to being a for-profit government. It sits within the nation of Hondoras, whose government has agreed to let it take over many local functions of government for a long duration. Prospera seems to be successfully achieving those functions at a substantially lower cost than does ordinary Honduran government, a fact that substantially lowers the cost of doing business there. (I may have helped convince them to use liability insurance in deal with law risk.)

As a result, Prospera seems to be doing well, and I expect it will prosper. And I urge you to consider doing business there. Except, the Honduran government has been making noises about maybe reneging on their promise. And Prospera keeps getting nasty unfair world press, due to so many really hating the idea of for-profit government. And yes, enough hate might take it down.

My best guess is that this hate has something to do with disliking profane money connecting to sacred governance. Which is another reason to try to study the sacred more. To see if there is any way around this problem.

Added: In interesting intermediate form would be if management consulting firms ran for office in democratic elections, based on their worldwide track record of performance in such roles. Alas many would probably also hate this as a profane-sacred violation.

Added 10a: Many are saying that what we really need is more competition between governments. Which would of course help, yes. But that seems to me a separate issue from what I’m discussing here. Also note that by allowing hostile takeovers, for-profit forms would introduce a new form of competition over governments.

Added 12Nov: Curtis Yarvin and I had a brief email exchange:

Yarvin: You should note that the subject population of a for-profit government is its capital base, giving it an aligned incentive to preserve and promote the health of the people—the traditional motto of government, salus populi supreme lex.

Revenue or even profit are not the purpose of a company, but only growth of capital—profit including appreciation/depreciation. So the incentives are aligned (not perfectly aligned, as in the case of ZMP people, but well aligned.)

Me: Yes, I’d guess most for-profit govts today would have incentives sufficiently well aligned. The main problem seems to be public hostility to the concept.

Yarvin: But public opinion is downstream from power. Power can persuade everyone to believe in anything. It can fool almost everyone almost all the time. Look around you!

Therefore, if such a regime can establish itself, it can maintain itself. Not only can it inculcate its doctrines in the whole population—this is especially easy if those doctrines are true.
As for bootstrapping, the people of today are frivolous and ironic and fanciful. The best way to do anything with them is to get them to do it for fun. They will do anything for fun—ergo, the revolution will have to be fun.
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What Will Be Fifth Meta-Innovation?

We owe pretty much everything that we are and have to innovation. That is, to our ancestors’ efforts (intentional or not) to improve their behaviors. But the rate of innovation has not been remotely constant over time. And we can credit increases in the rate of innovation to: meta-innovation. That is, to innovation in the processes by which we try new things, and distribute better versions to wider practice.

On the largest scales, innovation is quite smooth, being mostly made of many small-grain relatively-independent lumps, which is why the rate of overall innovation usually looks pretty steady. The rare bigger lumps only move the overall curve by small amounts; you have to focus in on much smaller scales to see individual innovations making much of a difference. Which is why I’m pretty skeptical about scenarios based on expecting very lumpy innovations in any particular future tech.

However, overall meta-innovation seems to be very lumpy. Through almost all history, innovation has happened at pretty steady rates, implying negligible net meta-innovation at most times. But we have so far seen (at least) four particular events when a huge quantity of meta-innovation dropped all at once. Each such event was so short that it was probably caused by one final key meta-innovation, though that final step may have awaited several other required precursor steps.

First natural selection arose, increasing the rate of innovation from basically zero to a positive rate. For example, over the last half billion years, max brain size on Earth has doubled roughly every 30 million years. Then proto-humans introduced culture, which allowed their economy (tracked by population) to double roughly every quarter million years. (Maybe other meta-innovations arose between life and culture; data is sparse.) Then ten thousand years ago, farming culture allowed the economy (tracked by population) to double roughly every thousand years. Then a few hundred years ago, industrial culture allowed the economy (no longer tracked by population) to double every fifteen years.

So these four meta-innovation lumps caused roughly these four factors of innovation growth rate change: 60,120, 240, infinity. Each era of steady growth between these changes encompassed roughly seven to twenty doublings, and each of these transitions took substantially less than a previous doubling time. Thus while a random grain of innovation so far has almost surely been part of a rather small lump of innovation, a random grain of meta-innovation so far has almost surely part of one of these four huge lumps of meta-innovation.

What caused these four huge lumps? Oddly, we understand the oldest lumps best, and recent lumps worse. But all four seems to be due to better ways to diffuse, as opposed to create, innovations. Lump 1 was clearly the introduction of natural selection, where biological reproduction spreads innovations. Lump 2 seems somewhat clearly cultural evolution, wherein we learned enough how to copy the better observed behaviors of others. Lump 3 seem plausibly, though hardly surely, due to a rise in population density and location stability inducing a change from a disconnected to a fully-connected network of long-distance travel, trade, and conquest. And while the cause of lump 4 seems the least certain, my bet is the rise of “science” in Europe, i.e., long distance networks of experts sharing techniques via math and Latin, enhanced by fashion tastes and noble aspirations.

Innovation continues today, but at a pretty steady rate, suggesting that there has been little net meta-innovation recently. Even so, our long-term history suggests a dramatic prediction: we will see at least one more huge lump, within roughly another ten doublings, or ~150 years, after which the economy will double in roughly a few weeks to a few months. And if the cause of the next lump is like the last four, it will be due to some new faster way to diffuse and spread innovations.

Having seen a lot of innovation diffusion up close, I’m quite confident that we are now no where near fundamental limits on innovation diffusion rates. That is, we could do a lot better. Another factor of sixty doesn’t seem crazy. Even so, it boggles the mind to try to imagine what such a new meta-innovation might be. Some new kind of language? Direct brain state transfer? Better econ incentives for diffusion? New forms of social organization?

I just don’t know. But the point of this post is: we have good reason to think such a thing is coming. And so it is worth looking out for. Within the next few centuries, a single key change will appear, and then within a decade overall econ growth would increase by a factor of sixty or more. Plausibly this will be due to a better way to diffuse innovations. And while the last step enabling this would be singular, it may require several precursors that appear at different times over the prior period.

My book Age of Em describes another possible process by which econ growth could suddenly speed up, to doubling in weeks or months. I still think this is plausible, but my main doubt is that the main reason I had predicted much faster growth there was not due to betters way to diffuse innovations in this scenario. Making this scenario a substantial deviation from prior trends. But maybe I’m wrong there.

Anyway, I’m writing here to say that I’m just not sure. Let’s keep an open mind, and keep on the lookout for some radical new way to better diffuse innovation.

Added 6a: Note that many things that look like plausible big meta-innovations did not actually seem to change the growth rate at the time. This includes sex, language, writing, and electronic computing and communication. Plausibly these are important enabling factors, but not sufficient on their own.

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Moral Progress Is Not Like STEM Progress

In this post I want to return to the question of moral progress. But before addressing that directly, I first want to set up two reference cases for comparison.

My first comparison case is statistics. Statistics is useful, and credit for the value that statistics adds to our discussions goes to several sources: to the statisticians who develop stat tests and estimates, to the teachers who transmit those tools to others, and to the problem specialists who find useful places to apply stats.

We can tell that statisticians deserve credit because we can usually identify the particular tests and estimates being used (e.g., “chi test”) in each case, and can trace those back to the teachers who taught them, and the researchers who developed them. New innovations are novel combinations of stat details whose effectiveness depends greatly on those details. We can see the first use cases of each such structure, and then see how a habit of its use spread.

Similar stories apply to many STEM areas, where we can distinguish particular design elements and analysis tools, and trace them back to their teachers and innovators. We can thus credit those innovators with their contributions, and verify that we have in fact seen substantial progress in these areas. We can see many cases where new tools let us improve on the best we could do with old tools.

My second comparison case is the topic area of home arrangement: what things to put in what drawers and rooms in our homes, and what activities to do in what parts of what rooms at what times of the day or week. Our practices in these areas result from copying the choices of our parents, friends, TV shows, and retailers, and also from experimenting with personal variations to see what we like. Over our lifetimes, we each tend to get more satisfied with our choices.

It is less clear, however, how much humanity as a whole improves in this area over time. Oh, we prefer our homes to homes of centuries ago. But this is most clearly because we have bigger nicer homes, that we fill with more nicer things than our ancestors had or could afford.

As new items become available, our plans for which things go where, and what we do with them when, have adapted over time. But it isn’t clear that humanity learns much after an early period of adaptation to each new item. Yes, for each choice we make, we can usually offer an argument for why that choice is better, and sometimes we can remember where we heard that argument. But the general set of arguments used in this area doesn’t seem to expand or improve much over time.

It is possible and even plausible that, even so, we are slowly getting better in general at knowing where to put things and what to do when in homes. Even if we don’t learn new general principles, we may be slowly getting better at reducing our case specific errors relative to our constant general principles.

But if so, the value of this progress seems to be modest, compared to our other related sources of progress, such as bigger houses, better items, and more free time to spend on them. And it seems pretty clear that little of the progress that we have seen here is to be credited to researchers specializing in home arrangement or personal activity scheduling. We don’t share much general abstract knowledge about this area, and haven’t added much lately to whatever of that we once had.

We see similar situations in many other areas where there is widespread practice, but few research specialists or teachers of newly researched tools. There might be progress in reducing errors where practice deviates from widely accepted stable principles, but if so that progress seems modest relative to progress due to other factors, such as better technology, increased wealth, and larger populations.

With these two reference cases in mind, STEM tools and home arrangement, let us now consider moral progress. The world seems to many to be getting more moral over time. But that could be because we have been getting richer and safer, which makes morality more affordable to us. Or it could be due to random correlated drift in our practices and standards, combined with our habit of judging past practices by current standards.

However, it also seems possible, at least at first glance, that our world is getting more apparently moral because of improved moral abilities, holding constant our wealth and knowledge about non-moral topics. For example, moral researchers might be acquiring more objective genera knowledge about morality, knowledge which morality teachers then spread to the rest of us, who then apply those improved moral tools to particular cases.

In support of this theory, many people point to particular moral arguments when they defend the morality of particular behaviors, and they often point to particular human sources for those arguments. Furthermore, many of those sources are new and canonical, so that a great many people in each era point to the same few sources, sources that are different from those to which prior generations pointed. Does this show progress?

If you look carefully at the specific moral arguments that people cite to support their behavior, it turns out that those arguments look pretty similar to arguments that were known long before. While each new generation’s canonical sources have some unique examples, styles, and argument details, those differences don’t seem to matter much to the practices of the ordinary people who cite them.

This situation seems in sharp contrast to the case of progress in statistics, for example, where the details of each new statistical test or estimate show up clearly and matter greatly to applications of those stats. It seems more consistent with moral arguments being used to justify behavior that would have happened anyway, rather than having moral arguments cause changes in behavior.

Yes, some old moral arguments may well have been forgotten for a time, and thus need to be reinvented by newer sources. For example, while ancient sources plausibly expressed thoughtful critiques of slavery and gender inequality, recent critics of such things may well have not read such ancient sources.

Even so, progress in morality looks to me much more like progress in home arrangement, and much less like progress in STEM. Even though locally new home arrangement choices continually appear, they don’t appear to add up to much overall progress relative to other sources of progress. Similarly, while it is possible that there is some moral progress due to slowly learning to have lower local error rates relative to constant general principles, I think we can pretty clearly reject the STEM-analogue hypothesis that morality researchers invent new detailed morality structures which then diffuse via teachers to greatly change typical practice.

Thus an examination of the details of moral change suggests that little of it can be credited to moral researchers, and only modest amounts to practioners slowly learning to cut errors relative to stable principles. Thus most apparent progress is plausibly due to our getting richer and safer, or to drift combined with a habit of judging past practices by current standards.

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Branding Report Professions

What is the difference between a job and a profession? It seems that, relative to professional jobs, ordinary jobs tend to be less well-paid, prestigious, autonomous, long-lasting, formally trained, regulated, organized, or associated with idealism and identity. But why are these features correlated, and why do we tend to see profession vs. not as a binary distinction?

One standard story is that professions exist when it is hard for customers to judge worker output quality. In response, customers push workers to rate each other’s quality, and this requires that workers organize. No doubt work does vary in this parameter. But workers are often lousy at judging each other’s output, once rated there’s no need to ban low quality workers, output quality seems hard to judge in a great many non-professions, and employers find many useful ways to judge worker quality without organized professions. So I doubt this explains that much of why professions exist.

Another simple story that may work better is that the key is simply organization. When workers organize, they can better promote their cause, especially via regulation. Stronger identity and idealism helps induce organization, and organization leads to more prestige and money. Plausible, but is it the whole story?

A third story is that the key is prestige. When the wider world is more willing to admire and idealize some kind of work, then that world becomes more willing to let these workers be autonomous, and to together control how they are educated and limit who can do that work. Which induces those workers to organize more, to gain these benefits, and to police prestige-related member behavior. That is, when a big part of what they are selling is prestige, workers who look similar can gain by preventing any of them from degrading their shared image via un-prestigious features or behavior.

In this post I want to explore a fourth complementary story, intended to apply to the subset of professions I call “report professions”, wherein workers produce reports intended to persuade audiences. For example, investors rely on accountant audits to judge the financial health of firms. Engineers and planners creates designs and plans suggesting to potential backers that things can be done or built with particular methods, and within given time and cost budgets. Doctor diagnoses convince insurers to cover treatments for claimed illnesses. And the grades teachers assign students may convince employers to hire them. Furthermore, such reports aren’t incidental; inducing them is often the main reason customers hire such professionals.

In all of these case, the ideal is audiences who are persuaded by reports they believe to have been produced according to professional norms, with workers resisting pressures from bosses or customers to give more favorable reports. That is, audiences need to believe that the accountant won’t obey a boss who instructs them to say that their broke firm is flush with cash, and to believe that a teacher won’t give an A+ to a failing but financially-generous student. But this is actually a non-trivial situation to produce. Why exactly wouldn’t an accountant obey a boss seeking to make his firm look as good as possible? And why wouldn’t a teacher give a student as high a grade as he or she can afford?

To keep such accountants, teachers, etc. in line, it helps if they identify with the high ideals of their profession. But it can help even more to show a credible threat that some other professional of their type might quickly review their report and declare it to violate professional norms, leading to their expulsion from the profession. And to make this scenario believable it helps if this profession coordinates to train its similar-ability members similarly, to use relatively standard, stable, mechanical, and context-insensitive report-generating procedures. Without such procedures, its hard to see how a quick review could show violations. And it furthermore helps if there are ways to induce independent professionals to check suspicious reports, and then to punish violators found.

That is, report professionals need to coordinate to create and enforce a distinctive report-supporting “brand”. Without such coordination, their product is worthless. Which is why they push to organize and gain regulatory powers, and why the rest of us grant their requests.

This story doesn’t explain professions that don’t issue reports to persuade audiences. But for professionals who do issue reports, this story plausibly explains why such workers have more autonomy, training, regulation, organization, and idealism. No one will value their reports if members cannot credibly commit to following professional norms instead of the desires of their bosses and customers. And this story predicts that these factors will distort their methods away from being innovative and usefully adapted toward local conditions, and instead toward methods that are standard, stable, mechanical, and context-insensitive.

Ideally, branded report professions would be organized and compete like franchises, such as McDonalds or Burger King. While McDonalds can regulate its franchisees to ensure that they follow McDonald’s procedures, to protect the McDonald’s brand, it can’t regulate Burger King franchises. Similarly, ideally each profession would regulate the behavior of its members, to keep to its standard methods, but new professions could enter with new methods and compete for the same set of customers.

Alas, we instead typically allow prestigious professions to not only say who can be a member and what methods members must use, we also allow them to regulate this FOR ALL similar workers! For example, the official professional organizations that regulate legal, medical, accounting, teaching, and engineering are given control over any workers who do any similar sorts of jobs, not just those under their brand. It is as if we gave McDonalds the ability to regulate all burger places, all fast food places, or even all restaurants. That would not be good for innovation and adaptation in restaurants.

These overly broad regulatory powers seem to do great damage. They let lazy report professions lock in poor methods that don’t adapt much to local conditions, and that don’t innovate much over time. And as our world of once-completing nations is merging into a single world mob, with strong global coordination on regulations, competition between nations no longer functions to discipline such lazy nation-specific professional associations. To promote innovation and adaptation then, we instead need to allow the entry of new distinctly-branded report professions that compete with old ones for the same customers, perhaps even backed by profit-seeking investors. New professions in medical, legal, accounting, engineering, etc. Don’t let McDonalds tell Burger King how to cook.

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Prediction Markets Need Trial & Error

We economists have a pretty strong consensus on a few key points: 1) innovation is the main cause of long-term economic growth, 2) social institutions are a key changeable determinant of social outcomes, and 3) inducing the collection and aggregation of info is one of the key functions of social institutions. In addition, better institutional-methods for collecting and aggregating info (ICAI) could help with the key meta-problems of making all other important choices, including the choice of our other institutions, especially institutions to promote innovation. Together all these points suggest that one of the best ways that we today could help the future is to innovate better ICAI.

After decades pondering the topic, I’ve concluded that prediction markets (and closely related techs) are our most promising candidate for a better ICAI; they are relatively simple and robust with a huge range of potential high-value applications. But, alas, they still need more tests and development before wider audiences can be convinced to adopt them.

The usual (good) advice to innovators is to develop a new tech first in the application areas where it can attract the highest total customer revenue, and also where customer value can pay for the highest unit costs. As the main direct value of ICAI is to advise decisions, we should thus seek the body of customers most willing to pay money for better decisions, and then focus, when possible, on their highest-value versions.

Compared to charities, governments, and individuals, for-profit firms are more used to paying money for things that they value, including decision advice. And the decisions of such firms encompass a large fraction, perhaps most, of the decision value in our society. This suggests that we should seek to develop and test prediction markets first in the context of typical decisions of ordinary business, slanted when possible toward their highest value decisions.

The customer who would plausibly pay the most here is the decision maker seeing related info, not those who want to lobby for particular decisions, nor those who want to brag about how accurate is their info. And they will usually prefer ways to elicit advice from their associates, instead of from distant curated panels of advisors.

We have so far seen dozens of efforts to use prediction markets to advise decisions inside ordinary firms. Typically, users are satisfied and feel included, costs are modest, and market estimates are similarly or substantially more accurate than other available estimates. Even so, experiments typically end within a few years, often due to political disruption. For example, market estimates can undermine manager excuses (e.g., “we missed the deadline due to a rare unexpected last-minute problem”), and managers dislike seeing their public estimates beaten by market estimates.

Here’s how to understand this: “Innovation matches elegant ideas to messy details.” While general thinkers can identify and hone the elegant ideas, the messy details must usually come from context-dependent trial and error. So for prediction markets, we must search in the space of detailed context-dependent ways to structure and deploy them, to find variations that cut their disruptions. First find variations that work in smaller contexts, then move up to larger trials. This seems feasible, as we’ve already done so for other potentially-politically-disruptive ICAI, such as cost-accounting, AB-tests, and focus groups.

Note that, being atheoretical and context-dependent, this needed experimentation poorly supports academic publications, making academics less interested. Nor can these experiments be enabled merely with money; they crucially need one or more organizations willing to be disrupted by many often-disruptive trials.

Ideally those who oversee this process would be flexible, willing and able as needed to change timescales, topics, participants, incentives, and who-can-see-what structures. An d such trials should be done where those in the org feel sufficiently free to express their aversion to political disruption, to allow the search process to learn to avoid it. Alas, I have so far failed to persuade any organizations to host or fund such experimentation.

This is my best guess for the most socially valuable way to spend ~<$1M. Prediction markets offer enormous promise to realize vast social value, but it seems that promise will remain only potential until someone undertakes the small-scale experiments needed to find the messy details to match its elegant ideas. Will that be you?

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Innovation Liability Nightmare

When I try to imagine how our civilization might rot and decline over the coming millennia, my thoughts first go to innovation, as that has long been our main engine of growth. And while over the years I’ve often struggled to think of ways to raise the rate of innovation, it seems much easier to find ways to cut it; in general, it is easier to break things than improve them.

For example, we might press on one of our legal system’s key flaws. Today, law does far more to discourage A from harming B than to encourage A to help B. B can often sue A for compensation when A harms B, but A can rarely sue B for compensation when A helped B. Law. Today is mostly a system of brakes, not of engines or accelerators.

This is less of a problem for auto accidents or pandemics, where the most important effects of the most important actions are indeed harms. But it is a much bigger problem in innovation, where the main problem is too little incentive to help. In general, society gains far more from innovations than do the people who push for them. So innovation needs engines, not brakes.

The problem is that even events whose effects are overall beneficial will still have some harmful effects. For example, if you invent a new better mousetrap, you may displace previous mousetrap makers. Or by introducing cars, you may hurt people who supplied or managed horses. So what if our legal system makes it easier to sue people for the harms caused by their innovations?

For example, many have complained lately of negative effects of social media, such as increasing anxiety, decreasing privacy, and passing on “fake” news. And just as legal liability has been a big weapon in recent campaigns against harms from tobacco and pain-killers, liability may well also become a big weapon against social media. Wielded especially strongly against those who have most innovated and developed social media.

Imagine that holding innovators liable for the negative effects of their innovations became more widespread. But without increasing the rewards we allow to innovators for the benefits that they bestow. Together with the trend to increased regulation, this might just become enough to kill the innovation goose that lays our golden egg of growth.

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Why Not Innovate More?

When investigating specific policy changes to test for evidence of whether stronger patents induce more R&D, a number of papers have failed to uncover such a relationship. (more)

Economists widely believe that failing to sufficiently promote innovation is one of humanity’s biggest, if not the biggest, social failure. While innovation is the main cause of growth in population, wealth, and satisfaction over time, the people who put in effort to create and diffuse innovations on average gain much less from their efforts than what everyone else gains. So they do too little.

Yes, we do have some laws and policies that we say promote innovation. Such as intellectual property, research tax credits, and government funded research. But our total spending on all of these is quite small as a fraction of the economy. Even given these efforts, we still have a huge underinvestment in innovation. Why?

One theory says that we still don’t sufficiently understand innovation. Yes, we know roughly what social process we have in mind, and we can roughly agree on which events and things around us represent more or less innovation. For example, we can hand out awards for unusually good innovation. But if we funded a government agency tasked with promoting innovation, or if your org funded a special office to do similarly, they wouldn’t actually know enough about what to do to justify a large budget. Which suggests that we don’t actually know enough yet about which are the more useful innovation efforts.

Another theory, however, says that we know plenty of other ways to promote innovation, but just aren’t willing to pay their costs. Our world would be more innovate with lower levels of regulation, especially re new products and services. There’d be more innovation with less variety in products, services, languages, and cultures, and with more emphasis on capital and engineering over labor and design. We’d also have more innovation diffusion if we weakened our “not invented here” biases, and other biases to celebrate invention more than diffusion. And if we celebrated innovation more, compared to other accomplishments, such as activism.

We know of many ways to make changes in these directions. But for all such changes we have sacred-like values that oppose them, and which we prioritize over innovation. The obvious but hard solution: change our priorities.

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Centrists Outside The Overton Window

Overton window – the spectrum of ideas on public policy and social issues considered acceptable by the general public at a given time.

The main thing that sets the size of the world economy today is the amount of innovation that has happened before today. In general we don’t induce enough innovation, as innovators tend to benefit society more than themselves. Thus better innovation institutions, which offer stronger incentives to innovate well, are probably the main way to make a better wealthier more prosperous tomorrow.

Innovation is also terribly important in politics and governance. Most of today’s official policies today were once only proposals, and before that quite unpopular proposals. That is, most current policies were once outside the Overton window of policies seen as acceptable to at least consider. 

The usual political and governance processes are about choosing among policies within the Overton window. Almost everything you see in government, politics, and the media is part of this process. As well as a great many things you don’t see. But just as important are the less visible processes that happen outside the Overton window, processes which decide which of the vast space of logically possible policies are moved closer to the edge of that window, where random fluctuations might tip them in. 

These processes by which outside proposals are honed and selected seem especially neglected today. The main groups who attend to them seem to be political extremists, whose ideal policies are far outside the current Overton window. Such extremists seek ways to reframe political alliances or issues to move the Overton window, and thus gain more favorable consideration for their extreme ideal points. 

Now given how important is innovation, and how neglected is the vast space of policies outside the tiny Overton window, it is good that at least some are thinking outside this “box”. But it also seems a shame that these are mainly extremists. 

Our usual business and tech innovation would be worse if most of this was done by extremists pushing unusual agendas regarding how we should all want to live. For example, imagine if cell phones were only likely to be developed by religious cults with a deep hatred of wires. It is good that instead most business and tech innovation is instead driven by profit incentives, as that drives them to explore a much wider space of possibilities. 

This common diagram describing the Overton window is seriously misleading, as it presents policy as a one-dimensional spectrum. In fact, the real space of policies is hugely multi-dimensional, with a “Overton hypercube” that takes up only tiny fraction of this vast space. It is thus perfectly rational to want to explore that vast space, even by those who are “centrists” with respect to the usually small number of ideological dimensions. There are surely a great many good policies out there in that vast space which are attractive to centrists, if only they can find and hone them.

Thus we want many such centrists to help explore this space, and not just a few extremists. Perhaps in future posts I’ll make some new more concrete proposals for how to better recruit centrists, and most everyone, to explore the vast space of possible policies that lie outside the tiny Overton hypercube. To help hone and select candidates, moving them closer to the edge of this Overton “window.” After which they may later drift inside that window, to become live possibilities for adoption. The better we explore the total space of policy options, the better will be the policies that we adopt. 

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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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Why Does Govt Do Stuff?

Looking across the many different activities and sectors of society, how well can we predict where governments get more vs. less involved?

Though this is an oft discussed topic, I can’t recall seeing an overall theory summary. So I thought I’d write one up. Here are some big relevant factors, and areas they may explain. Most are tentative; you may well convince me to move/change/add them.

Control – Whomever runs the government prefers to control areas that can be used to prevent and resist opposition and rivals.
Predicts more: religion, military, police, law, news, schools, disaster response, electricity, energy, banking.

Scale – If supplying a product or service has strong economies of scale, network, or coordination, it can be cheaper to use one integrated organization, who if private may demand excessive prices and thereby threaten control.
Predicts more: military, “roads” (including air, boat travel support), social media, money, language, electricity, telecom, water, sewer, trash, parks, fire, software, fashion, prestige
Predicts less: housing, food, medicine, art, entertainment, news, police, jail.

Innovation – As governments seem less able to encourage or accommodate effective innovation, governments tend to be less involved in rapidly evolving sectors.
Predicts more: roads, water, sewer, track, parks.
Predicts less: military hardware, vehicles, tech/computers, entertainment, social networks.

Variety – Governments tend to encourage and be better at relatively standardized products and services, done with fewer versions, more the same for everyone everywhere at all times.
Predicts more: war, medicine, schools, disaster response, roads.
Predicts less: housing, food, entertainment, romance, parenting, friendship, humor.

Norms – Norms are shared, and we like to enforce them together, officially.
Predicts more: religion, law, war, romance, parenting, medicine, drugs, gambling, slavery, language, manners, sports.

Show Unity – As we want to show that we are together, and care about each other, we like to do the things we to do to show such care together in a unified way.
Predicts more: religion, poverty/unemployment/health insurance, school, medicine, fire, parks, housing, food, disaster response, trash/sewer, coverage expansion subsidies.

Show Off – We want to impress outsiders with our tastes, abilities.
Predicts more: research, schools, high art, high sport, roads, parks, shared space architecture, trash/sewer.
Predicts less: low art/entertainment, low sport, gossip.

Hypocrisy – When we profess some motives, but others are stronger, the opacity and slack of government agencies, and better ability to suppress critiques, makes them better able to hide such differences.
Predicts more: medicine, drugs, gambling, schools, police, jail, courts, romance, zoning, building codes, war, banking.
Predicts less: water, sewers, electricity.

If we could collect even crude stats on how often or far govt is involved in each area, and crudely rate each area-factor combo for how strongly that factor applies to that area, we could do a more formal analysis of which of factors predict better where.

Note that scale is the strongest factor suggesting that govt does more when more govt helps more. Innovation and variety suggest that also when those factors are the cause of govt involvement, but much less so if those features are the result. While norms are on average valuable, it is much less clear when govt support improves them. Most signaling likely helps each society that does it, but is done too much for the good of the world overall.

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