Tag Archives: Innovation

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.

GD Star Rating
a WordPress rating system
Tagged as: ,

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.

GD Star Rating
a WordPress rating system
Tagged as: ,

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?

GD Star Rating
a WordPress rating system
Tagged as: , ,

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.

GD Star Rating
a WordPress rating system
Tagged as: ,

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.

GD Star Rating
a WordPress rating system
Tagged as: , ,

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. 

GD Star Rating
a WordPress rating system
Tagged as:

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

GD Star Rating
a WordPress rating system
Tagged as: ,

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.

GD Star Rating
a WordPress rating system
Tagged as: , , , ,

What Can Money Buy Directly?

Can money buy oranges? Well obviously, in an indirect sense. With money, you could travel to a place where you’ve heard oranges grow wild, search to find such a plant in the wild, dig it up and try to ship it home, see if it you can make it thrive there, and if it does, take some oranges as your reward. This might work, but success depends not just on the money you pay; it also depends much more on your effort, abilities, and other context. In principle, you might be able to execute this plan without any money, but typically more money will make such a plan a bit easier. So, yes, in this weak sense, you can “buy” oranges with money.

At an ordinary grocery store, however, you can buy oranges much more directly. You go to the produce section, look for the orange color, walk to the pile of oranges, take as many as you want, and pay the price per orange at the register. Or at a full service grocery, you might just say “six oranges please” and a grocer would go find and bag them for you. Online, you might just type in “orange”, enter “6” for quantity, and click “buy”.

These ways to buy oranges are usually pretty reliable even for an ordinary person who knows little about oranges. Using these methods, the number of oranges you get depends mainly on how much money you are willing to pay, and much less on other context. This is what I mean by buying something “directly.” And so regarding the oft-asked question “what can money buy?”, a more interesting version of this question is “What can money buy relatively directly.”

As more money makes most any plan a bit easier to achieve, the many long lists one can find of “things money can’t buy” are in one sense obviously wrong; money helps with most of them. And if they just mean that money can’t guarantee the max level of each thing, that’s obvious, but trivial, as pretty much nothing guarantees that. You can’t even guarantee you’ll get oranges if you order them from a grocery. And if that is the meaning, why pick on money, relative to anything else that might greatly but imperfectly help you get things?

Perhaps what people mean is that money isn’t the main factor that determines if you succeed with such things; money can be a distraction from more important issues. But if so, that seems to claim that you can’t buy such things directly. Which then raises the key question: for what kinds of things can the money you pay be a strong factor in determining how much of it you get? That is, what can money buy directly?

In my last post, I talked about how one can buy higher wages, via a job agent. I wasn’t saying that there are complex and subtle ways to spend money to help your career, ways that could work if only you were clever and skilled enough to understand and apply them. I was instead saying that there is a simple direct way to do this, one most anyone can understand: hire an agent (and anti-agent). That method doesn’t guarantee you any particular wage, but it does let you control how much you pay per wage increase.

In fact, I’ll go further now, and say that there seem to be ways to measure most anything, and as a result we can buy most any measured thing relatively simply and directly. That is, via a simple method that most anyone can come to understand, you can just point to what you want, put cash on the table, and then lose cash in proportion to how much you get of what you want. And the relation is substantially causal; paying more can cause you to get more, even when you have little relevant ability or understanding.

In the academic literature, this method is called an “incentive contract”. You find a way to measure the outcome you want, you offer to give someone access to levers by which they can plausibly influence this outcome, and you contract to pay them more cash the higher is this measure. You might also hold auctions or competitions to see who is best to put into this role.

We have a great many real examples today, and in history, of oft-used incentive contracts. Artists and athletes have agents paid a fraction of their earnings. Line workers are paid “piece rates” per how many items they assemble, or tomatoes they pick. Sales workers are paid commissions, per how many items they sell. Hedge fund managers are paid more if their fund makes higher returns. Lawyers on contingency fees are paid a fraction of court awarded damages. Firm managers are paid in stocks and options which rise in value when firm stock prices rise. Athletes are paid bonuses for individual and team success. Construction contractors are paid more if their work is completed by a deadline. Ships carrying convicts to Australia were paid on the number who arrived alive (which worked much better than the number who started out alive.)

Are the applications we’ve seen the only feasible ones, or could many more yet be developed? Consider beauty. Some say beauty can’t be measured, as it is “in the eye of the beholder”. But if you ask many people to rate someone’s beauty, their ratings are correlated. So imagine taking many standardized pictures and video of a client, across across their usual range of clothes and environments, and then paying many independent observers to rate their attractiveness. Do this at the start to get an initial value, and plan to do it again in, say, six months. A client might pay a beauty agent based on the change in this measure.

Potential beauty agents could bid by offering how much money they want to be paid per unit of increased beauty, how much they would pay up front to gain this role, and which particular beauty decisions they want to control, rather than merely advise, at least until the second measurement. There are probably clever ways to use auctions or decision markets to select from among these bids, but such details need not concern us now.

Yes, it would be a problem if a beauty agent could corrupt beauty measurements, or exploit their biases. But if such effects are modest, expert beauty agents can likely substantially increase a client’s beauty, relative to that client’s amateur efforts. Consider that movies don’t usually let actors pick their own clothes and hairstyle to look good in each movie; beauty experts instead make those choices. Yes, clients may care less about beauty as seen by average people, and more as seen by particular communities. But measuring such local versions of beauty should only cost a bit more.

Now consider happiness. If happiness were an entirely internal mental state that never influenced our external appearances, well then yes it would be hard to measure happiness. At least until we can better read brains. But most humans leak their feelings in many ways. So a 24/7 audio/video feed of a person, especially their facial expression and tone of voice, perhaps augmented by watch-based measures of heart rates, etc., seems plenty sufficient. Especially if processed via self and other reports, rather than artificially. Happiness could be measured pretty accurately from such things, especially for a client who wants it to be measurable, so that they can hire an agent to increase their happiness. (And especially as things like smiles and laughter probably evolved to signal happy internal states.)

A happiness agent is given control over some elements of a client’s life, and can advise on others. Especially on which other agents to hire for beauty, health, career, etc. Happiness agents pay some initial fee to gain this role, and then they are paid in proportion to the client’s measured happiness. Such agents might be big firms that combine many kinds of happiness expertise, and who can take big risks. If there are things that an expert can learn about how to be happy, things an ordinary amateur doesn’t know, then there is likely substantial scope for using agents to directly buy happiness. If so, money can buy happiness, directly.

Well this is enough for one blog post. The key conclusion: it looks feasible to much more directly buy many things we care greatly about, including beauty, happiness, health, career success, popularity, and status. Yes it would be work to set up systems to measure such things, work that could not be recouped for just from one client. But the prospect of many millions of clients should be quite sufficient.

One key question remains: why hasn’t there been more interest in such possibilities? Are these new innovations that could spread widely, or are they blocked by key fundamental permanent obstacles not yet considered in the above discussion?

Added 20Apr: Most seem to actually be comforted by the fact that it can be hard to buy things with money, and seem uninterested in finding ways to make it easier to buy things with money. I suspect they feel that better methods of this sort would give a relative advantage to people with more money, who they see as other people. While everyone could benefit from better ways to buy things with money, that matters little to those focused on relative status.

GD Star Rating
a WordPress rating system
Tagged as: , ,

Respectable Rants

I’m not very impressed with most political arguments, especially those targeted at mass audiences. I don’t mind such things being informal, passionate, rude, speculative, rambling, or redundant. But I need them to address what I see as key issues. Yes, my tastes may be unusual, but there are many others like me. So let me explain what I want to hear in a good political “rant”.

Don’t Exaggerate – You know who you are, and you know what I mean. There is plenty enough at stake in most areas to motivate me without your exaggerating. At least pretend toward honesty. All of history isn’t at stake, and no this by itself won’t decide between freedom and despotism. Yes, I can roughly correct for your exaggerations, so this item does the least harm. But it still bugs me.

Admit Tradeoffs – We usually can’t get more of something good without also getting less of something else good. Or more of something bad. I might be willing to go for the package, but don’t pretend there won’t be costs. If this choice isn’t new, tell me why we made the wrong tradeoff before. If this used to be a private choice, explain why private choices about this tend to go wrong.

Show Search – The world is complex, our systems in it have many parts, and things keep changing. So much of finding better policy consists of searching in a vast space of possible system-situation combos. Don’t pretend that the best combo is obvious, or that you are sure what will happen under your favored option. Tell me about what options we’ve tried, what we’ve seen there, and about new promising combinations. Tell me about key design principles, and how you may have found a rare design option that happens to embody many good design principles at once.

Prepare To Learn – This is the most important, and neglected, item. Don’t just tell me you have a plan, with details on request. Tell me how we will learn to adapt and improve your plan. What size experiments do we start with, where, and measured how? How we will change our designs in response in new iterations? Don’t tell me we will all make those decisions together, that just won’t work. Instead, tell me who will make those decisions, and especially, what will be their incentives to do this well.

If you want to just copy something that’s worked out pretty well elsewhere, okay maybe I mainly want to hear about tradeoffs seen there. Data. But if you want to do something new, then I need to hear a lot more about your learning plan. Especially when your proposal has a wide scope, its outcomes are hard to measure, and take a long time to be revealed.

Look, our main social problem is how to organize activity so that we can learn together how to be productive and useful to each other. There are other problems, but they are minor by comparison. Somehow each of us must react to the signals our world sends us, and send our own signals in response, to induce all the stuff that needs to happen, and efficiently and well. It is all terribly complex, but also terribly important.

Every policy proposal is of some way to change this huge system. We need some theory not only to estimate consequences of your proposal, but also to deal with its many unanticipated consequences later. Please give me some indication of what theories you’ll rely on. The weaker the theories you need, the better of course, but you’ll need something.

For example, if you propose to nationalize US medicine, tell me which other nationalized system you plan to copy. How does it decide on which treatments are covered, and where new facilities are built? How are doctors evaluated and if needed disciplined? How do patients express their differing individual preferences within this system? And since those other systems don’t contribute much to global medical innovation, tell me that you are okay with a big reduction in global medical innovation, or tell me how your system will be different enough to promote a lot more innovation.

For example, if you propose to regulate social media to be less addictive, stressful, and fake-news-promoting, tell us exactly what is the scope of powers you propose to grant regulators, what standards they will use to measure such things, and how the rest of us are to judge if they do a good job. Is this new proposed feedback process plausibly more effective than each of us individually switching our social media platforms when we feel addicted, stressed, or faked?

As you know, most political discourse purposely avoids most of what I’ve asked for here. Advocates instead tend to frame each dispute as a simple and fundamental moral choice. Details are avoided, dangers are exaggerated, and tradeoffs, search, and learning are rarely unacknowledged as issues. Politicians refer to goals and avoid talking about difficulties of implementation, incentives, measurement, or learning.

And that’s a big reason to be wary of letting political systems manage complex things of wide scope. When I buy something from a private source, they tend to say more about details, about how to measure payoffs, and about how they and I will learn about what works best. Maybe not an ideal amount, but definitely more. They more tell me what I want to hear in a rant, or an ad. If you want to make a to-my-ears good political rant, learn a bit from them.

GD Star Rating
a WordPress rating system
Tagged as: ,