Tag Archives: Innovation

Why Not Hi-Tech Forms?

A half century ago, when people tried to imagine a future full of computers, I’m sure one of the most obvious predictions they made is that we today wouldn’t have to work so hard to fill out forms. Filling out forms seemed then to be a very mechanical task, based on explicit mechanical rules. So once computers had enough space to store the relevant data, and enough computing power to execute those rules, we should not longer need to fill out most tedious parts of forms.

Oh sure, you might need to write an essay for a school application, or make a design for the shed when you ask your homeowner’s association permission to build a shed. But all that other usual tedious detail, no.

Now this has in fact happened for businesses, at least for standard forms and for big business. In fact, this happened many decades ago. Most of them wrote or bought programs to fill out standard forms that they use to talk to customers, to suppliers, and to government. But for ordinary people, this mostly just hasn’t happened. Oh sure, maybe your web browser now fills in an address or a credit card number on a web form. (Though it mostly gets that wrong when I try it.) But not all the other detail. Why not?

Many poor people have to fill out a lot of forms to apply for many kinds of assistance. Roughly once a year I’m told, at least. They see many of these forms as so hard to fill our that many of them just don’t bother unless they get help from someone like a social worker. So a lot of programs to help the poor don’t actually help many of those who are eligible, because they don’t fill out the forms.

So why doesn’t some tech company offer a form app, where you give all your personal info to the form and it fills out most parts of most forms for you? You just have to do the unusual parts. And they could have a separate app to give to orgs that create forms, so they can help make it easier for their forms to get filled out. Yes, much of the effort to make this work is more in standardization than in abstract computer algorithms. But still, why doesn’t some big firm do it?

I suggested all this to a social worker I know, who was aghast; she didn’t want this tech firm knowing all these details, like her social security number. But if you fill out all these forms by hand today, you are telling it all to one new org per year. Adding one firm to the list to make it all much easier doesn’t seem like such a high cost to me.

But maybe this is all about the optics; tech firms fear looking like big brother if they know all this stuff about you. Or many legal liability would fall on these tech firms if the form had any mistakes. Or maybe privacy laws prevent them from even asking for the key info. And so we all suffer with forms, and poor folks don’t get the assistance offer to them. And we all lose, though those of us who are better at filling out forms lose less.

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Pay More For Results

A simple and robust way to get others to do useful things is to “pay for results”, i.e., to promise to make particular payments for particular measurable outcomes. The better the outcomes, the more someone gets paid. This approach has long been used in production piece-rates, worker bonuses, sales commissions, CEO incentive paylawyer contingency fees, sci-tech prizes, auctions, and outcome-contracts in PR, marketing, consulting, IT, medicine, charities, development, and in government contracting more generally. 

Browsing many articles on the topic, I mostly see either dispassionate analyses of its advantages and disadvantages, or passionate screeds warning against its evils, especially re sacred sectors like charity, government, law, and medicine. Clearly many see paying for results as risking too much greed, money, and markets in places where higher motives should reign supreme.

Which is too bad, as those higher motives are often missing, and paying for results has a lot of untapped potential. Even though the basic idea is old, we have yet to explore a great many possible variations. For example, many of social reforms that I’ve considered promising over the years can be framed as paying for results. For example, I’ve liked science prizes, combinatorial auctions, and:

  1. Buy health, not health careGet an insurer to sell you both life & health insurance, so that they lose a lot of money if you are disabled, in pain, or dead. Then if they pay for your medical expenses, you can trust them to trade those expenses well against lower harm chances.
  2. Fine-insure-bounty criminal law systemCatch criminals by paying a bounty to whomever proves that a particular person did a particular crime, require everyone to get crime insurance, have fines as the official punishment, and then let insurers and clients negotiate individual punishments, monitoring, freedoms, and co-liabilities. 
  3. Prediction & decision markets – There’s a current market probability, and if you buy at that price you expect to profit if you believe a higher probability. In this way you are paid to fix any error in our current probabilities, via winning your bets. We can use the resulting market prices to make many useful decisions, like firing CEOs. 

We have some good basic theory on paying for results. For example, paying your agents for results works better when you can measure the things that you want sooner and more accurately, when you are more risk-averse, and when your agents are less risk-averse. It is less less useful when you can watch your agents well, and you know what they should be doing to get good outcomes.

The worst case is when you are a big risk-neutral org with lots of relevant expertise who pays small risk-averse individuals or organizations, and when you can observe your agents well and know roughly what they should do to achieve good outcomes, outcomes that are too complex or hidden to measure. In this case you should just pay your agents to do things the right way, and ignore outcomes.

In contrast, the best case for paying for results is when you are more risk-averse than your agents, you can’t see much of what they do, you don’t know much about how they should act to best achieve good outcomes, and you have good fast measure of the outcomes you want. So this theory suggests that ordinary people trying to get relatively simple things from experts tend to be good situations for paying for results, especially when those experts can collect together into large more-risk-neutral organizations.

For example, when selling a house or a car, the main outcome you care about is the sale price, which is quite observable, and you don’t know much about how best to sell to future buyers. So for you a good system is to hold an auction and give it to the agent who offers the highest price. Then that agent can use their expertise to figure out how to best sell your item to someone who wants to use it.

While medicine is complex and can require great expertise, the main outcomes that you want from medicine are simple and relatively easy to measure. You want to be alive, able to do your usual things, and not in pain. (Yes, you also have a more hidden motive to show that you are willing to spend resources to help allies, but that is also easy to measure.) Which is why relatively simple ways to pay for health seem like they should work. 

Similarly, once we have defined a particular kind of crime, and have courts to rule on particular accusations, then we know a lot about what outcomes we want out of a crime system: we want less crime. If the process of trying to detect or punish a criminal could hurt third parties, then we want laws to discourage those effects. But with such laws in place, we can more directly pay to catch criminals, and to discourage the committing of crimes. 

Finally when we know well what events we are trying to predict, we can just pay people who predict them well, without needing to know much about their prediction strategies. Overall, paying for results seems to still have enormous untapped potential, and I’m doing my part to help that potential be realized.

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Why Age of Em Will Happen

In some technology competitions, winners dominate strongly. For example, while gravel may cover a lot of roads if we count by surface area, if we weigh by vehicle miles traveled then asphalt strongly dominates as a road material. Also, while some buildings are cooled via fans and very thick walls, the vast majority of buildings in rich and hot places use air-conditioning. In addition, current versions of software systems also tend to dominate over old older versions. (E.g., Windows 10 over Windows 8.)

However, in many other technology competitions, older technologies remain widely used over long periods. Cities were invented ten thousand years ago, yet today only about half of the population lives in them. Cars, trains, boats, and planes have taken over much transportation, yet we still do plenty of walking. Steel has replaced wood in many structures, yet wood is still widely used. Fur, wool, and cotton aren’t used as often as they once were, but they are still quite common as clothing materials. E-books are now quite popular, but paper books sales are still growing.

Whether or not an old tech still retains wide areas of substantial use depends on the average advantage of the new tech, relative to the variation of that advantage across the environments where these techs are used, and the variation within each tech category. All else equal, the wider the range of environments, and the more diverse is each tech category, the longer that old tech should remain in wide use.

For example, compare the set of techs that start with the letter A (like asphalt) to the set that start with the letter G (like gravel). As these are relatively arbitrary sets that do not “cut nature at its joints”, there is wide diversity within each category, and each set is all applied to a wide range of environments. This makes it quite unlikely that one of these sets will strongly dominate the other.

Note that techs that tend to dominate strongly, like asphalt, air-conditioning, and new software versions, more often appear as a lumpy change, e.g., all at once, rather than via a slow accumulation of many changes. That is, they more often result from one or a few key innovations, and have some simple essential commonality. In contrast, techs that have more internal variety and structure tend more to result from the accumulation of more smaller innovations.

Now consider the competition between humans and computers for mental work. Today human brains earn more than half of world income, far more than the costs of computer hardware and software. But over time, artificial hardware and software have been improving, and slowly commanding larger fractions. Eventually this could become a majority. And a key question is then: how quickly might computers come to dominate overwhelmingly, doing virtually all mental work?

On the one hand, the ranges here are truly enormous. We are talking about all mental work, which covers a very wide of environments. And not only do humans vary widely in abilities and inclinations, but computer systems seem to encompass an even wider range of designs and approaches. And many of these are quite complex systems. These facts together suggest that the older tech of human brains could last quite a long time (relative of course to relevant timescales) after computers came to do the majority of tasks (weighted by income), and that the change over that period could be relatively gradual.

For an analogy, consider the space of all possible non-mental work. While machines have surely been displacing humans for a long time in this area, we still do many important tasks “by hand”, and overall change has been pretty steady for a long time period. This change looked nothing like a single “general” machine taking over all the non-mental tasks all at once.

On the other hand, human minds are today stuck in old bio hardware that isn’t improving much, while artificial computer hardware has long been improving rapidly. Both these states, of hardware being stuck and improving fast, have been relatively uniform within each category and across environments. As a result, this hardware advantage might plausibly overwhelm software variety to make humans quickly lose most everywhere.

However, eventually brain emulations (i.e. “ems”) should be possible, after which artificial software would no longer have a hardware advantage over brain software; they would both have access to the same hardware. (As ems are an all-or-nothing tech that quite closely substitutes for humans and yet can have a huge hardware advantage, ems should displace most all humans over a short period.) At that point, the broad variety of mental task environments, and of approaches to both artificial and em software, suggests that ems many well stay competitive on many job tasks, and that this status might last a long time, with change being gradual.

Note also that as ems should soon become much cheaper than humans, the introduction of ems should initially cause a big reversion, wherein ems take back many of the mental job tasks that humans had recently lost to computers.

In January I posted a theoretical account that adds to this expectation. It explains why we should expect brain software to be a marvel of integration and abstraction, relative to the stronger reliance on modularity that we see in artificial software, a reliance that allows those systems to be smaller and faster built, but also causes them to rot faster. This account suggests that for a long time it would take unrealistically large investments for artificial software to learn to be as good as brain software on the tasks where brains excel.

A contrary view often expressed is that at some point someone will “invent” AGI (= Artificial General Intelligence). Not that society will eventually have broadly capable and thus general systems as a result of the world economy slowly collecting many specific tools and abilities over a long time. But that instead a particular research team somewhere will discover one or a few key insights that allow that team to quickly create a system that can do most all mental tasks much better than all the other systems, both human and artificial, in the world at that moment. This insight might quickly spread to other teams, or it might be hoarded to give this team great relative power.

Yes, under this sort of scenario it becomes more plausible that artificial software will either quickly displace humans on most all jobs, or do the same to ems if they exist at the time. But it is this scenario that I have repeatedly argued is pretty crazy. (Not impossible, but crazy enough that only a small minority should assume or explore it.) While the lumpiness of innovation that we’ve seen so far in computer science has been modest and not out of line with most other research fields, this crazy view postulates an enormously lumpy innovation, far out of line with anything we’ve seen in a long while. We have no good reason to believe that such a thing is at all likely.

If we presume that no one team will ever invent AGI, it becomes far more plausible that there will still be plenty of jobs tasks for ems to do, whenever ems show up. Even if working ems only collect 10% of world income soon after ems appear, the scenario I laid out in my book Age of Em is still pretty relevant. That scenario is actually pretty robust to such variations. As a result of thinking about these considerations, I’m now much more confident that the Age of Em will happen.

In Age of Em, I said:

Conditional on my key assumptions, I expect at least 30 percent of future situations to be usefully informed by my analysis. Unconditionally, I expect at least 5 percent.

I now estimate an unconditional 80% chance of it being a useful guide, and so will happily take bets based on a 50-50 chance estimate. My claim is something like:

Within the first D econ doublings after ems are as cheap as the median human worker, there will be a period where >X% of world income is paid for em work. And during that period Age of Em will be a useful guide to that world.

Note that this analysis suggests that while the arrival of ems might cause a relatively sudden and disruptive transition, the improvement of other artificial software would likely be more gradual. While overall rates of growth and change should increase as a larger fraction of the means of production comes to be made in factories, the risk is low of a sudden AI advance relative to that overall rate of change. Those concerned about risks caused by AI changes can more reasonably wait until we see clearer signs of problems.

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Who Likes Simple Rules?

Some puzzles:

  • People are often okay with having either policy A or policy B adopted as the standard policy for all cases. But then they object greatly to a policy of randomly picking A or B in particular cases in order to find out which one works better, and then adopt it for everyone.
  • People don’t like speed and red-light cameras; they prefer human cops who will use discretion. On average people don’t think that speeding enforcement discretion will be used to benefit society, but 3 out of 4 expect that it will benefit them personally. More generally people seem to like a crime law system where at least a dozen different people are authorized to in effect pardon any given person accused of any given crime; most people expect to benefit personally from such discretion.
  • In many European nations citizens send their tax info into the government who then tells them how much tax they owe. But in the US and many other nations, too many people oppose this policy. The most vocal opponents think they benefit personally from being able to pay less than what the government would say they owe.
  • The British National Health Service gets a lot of criticism from choosing treatments by estimating their cost per quality-adjusted-life-year. US folks wouldn’t tolerate such a policy. Critics lobbying to get exceptional treatment say things like “one cannot assume that someone who is wheel-chair bound cannot live as or more happily. … [set] both false limits on healthcare and reducing freedom of choice. … reflects an overly utilitarian approach”
  • There’s long been opposition to using an official value of life parameter in deciding government policies. Juries have also severely punished firms for using such parameters to make firm decisions.
  • In academic departments like mine, we tell new professors that to get tenure they need to publish enough papers in good journals. But we refuse to say how many is enough or which journals count as how good. We’d keep the flexibility to make whatever decision we want at the last minute.
  • People who hire lawyers rarely know their track record at winning vs. losing court cases. The info is public, but so few are interested that it is rarely collected or consulted. People who hire do know the prestige of their schools and employers, and decide based on that.
  • When government leases its land to private parties, sometimes it uses centralized, formal mechanisms, like auctions, and sometimes it uses decentralized and informal mechanisms. People seem to intuitively prefer the latter sort of mechanism, even though the former seems to works better. In one study “auctioned leases generate 67% larger up-front payments … [and were] 44% more productive”.
  • People consistently invest in managed investment funds, which after the management fee consistently return less than index funds, which follow a simple clear rule. Investors seem to enjoy bragging about personal connections to people running prestigious investment funds.
  • When firms go public via an IPO, they typically pay a bank 7% of their value to manage the process, which is supposedly spent on lobbying others to buy. Google famously used an auction to cut that fee, but banks have succeed in squashing that rebellion. When firms try to sell themselves to other firms to acquire, they typically pay 10% if they are priced at less than $1M, 6-8% if priced $10-30M, and 2-4% if priced over $100M.
  • Most elite colleges decide who to admit via opaque and frequently changing criteria, criteria which allow much discretion by admissions personnel, and criteria about which some communities learn much more than others. Many elites learn to game such systems to give their kids big advantages. While some complain, the system seems stable.
  • In a Twitter poll, the main complaints about my fire-the-CEO decisions markets proposal are that they don’t want a simple clear mechanical process to fire CEOs, and they don’t want to explicitly say that the firm makes such choices in order to maximize profits. They instead want some people to have discretion on CEO firing, and they want firm goals to be implicit and ambiguous.

The common pattern here seems to me to be a dislike of clear formal overt rules, mechanisms, and criteria, relative to informal decisions and negotiations. Especially disliked are rules based on explicit metrics that might reject or disapprove people. To the extent that there are rules, there seems to be a preference for authorizing some people to have discretion to make arbitrary choices, regarding which they are not held strongly to account.

To someone concerned about bribes, corruption, and self-perpetuating cabals of insiders, a simple clear mechanism like an auction might seem an elegant way to prevent all of that. And most people give lip service to being concerned about such things. Also, yes explicit rules don’t always capture all subtleties, and allowing some discretion can better accommodate unusual details of particular situations.

However, my best guess is that most people mainly favor discretion as a way to promote an informal favoritism from which they expect to benefit. They believe that they are unusually smart, attractive, charismatic, well-connected, and well-liked, just the sort of people who tend to be favored by informal discretion.

Furthermore, they want to project to associates an image of being the sort of person who is confidently supports the elites who have discretion, and who expects in general to benefit from their discretion. (This incentive tends to induce overconfidence.)

That is, the sort of people who are eager to have a fair neutral objective decision-making process tend to be losers who don’t expect to be able to work the informal system of favors well, and who have accepted this fact about themselves. And that’s just not the sort of image that most people want to project.

This whole equilibrium is of course a serious problem for we economists, computer scientists, and other mechanism and institution designers. We can’t just propose explicit rules that would work if adopted, if people prefer to reject such rules to signal their social confidence.

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Rebooting Justice

In 2017, I read a WSJ review of Rebooting Justice by Barton & Bibas:

When it comes to securing justice in an efficient and affordable fashion, lawyers can in fact be the primary obstacle. … Even basic legal services at small or mid-size firms may cost more than $200 an hour, placing meaningful legal representation beyond the reach of many Americans. … Why … costs of legal education and guildlike restrictions on entry to the profession … increasing complexity of legal processes, … choosing to represent oneself is … on the rise … [but puts one] at a severe disadvantage. … There’s no reason that paralegals, notaries, social workers and others with relevant training could not [help]. … inexpensive, downloadable forms to cover basic legal matters, like living wills or articles of incorporation. … computer-assisted mediation … Their more radical suggestion is to restructure the system so that many processes are specifically designed to omit lawyers. … The biggest obstacle to such reforms could well come from the legal profession itself.

This book was also reviewed in the New York Times:

In many contexts the presence of more lawyers actually reduces the speed and effectiveness of achieving justice. …
Few realize that the long-accepted understanding that courts have the final say on the interpretation of laws in general is not explicitly established by the Constitution. … State courts have simply asserted that they have “inherent authority” over the administration of the legal system. Under this view, rules governing lawyers, as “officers of the court,” and the practice of law in theory are “not subject to legislative reversal or encroachment.” … [Lawyers] benefit from a secretive disciplinary process that almost never results in penalties or expulsion, combined with aggressive policing of the “unauthorized practice of law” … The most powerful innovations documented in “Rebooting Justice” appear to have sprung from the creative minds of nonlawyers.

Every review I’ve found has been positive. Yet the book only got 6 reviews at Amazon (all 5 star), and only 2 at Goodreads. I bought the book back then, but only now just finished it. So the book is far from a page turner, and obviously didn’t sell many copies. But all reviews I’ve seen say it is basically right:

It is hard to argue with most of the arguments in this book: the present legal system is predicated on the assumption of litigants with relatively equal legal representation; however, in the modern age, this is all too frequently an unmet assumption. (more)

One review argued that deregulating who can practice law won’t be enough:

It is far from clear that it is the high cost of legal services—driven by alleged overregulation—that is preventing Americans from obtaining legal assistance. A recent study … found that cost explains the decision to not seek legal assistance in less than a fifth of civil justice situations. … In several states, one can become a lawyer without attending an ABA-accredited law school; some do not require attending a brick-and-mortar law school at all. … The United Kingdom began allowing corporations, known as alternative business structures (ABS), to own law firms and offer legal services since 2007. … not led to the collapse of the legal system. But … also not had an appreciable effect on access to justice.

Which is probably right. But no review disputed the book’s most radical suggestion: switch to an inquisitorial legal system, wherein judges take the initiative. From the book:

We can learn from the American system of administrative law judges and from European courts. We can adapt the inquisitorial system, in which court officials actively investigate the facts and probe the evidence instead of relying on the parties’ lawyers. That approach can cut through distracting procedural games to focus on the facts and issues at the heart of a case. Though inquisitorial judging sounds like an exotic foreign transplant, American administrative agencies already use similar methods to adjudicate unemployment and Social Security disability claims, and so do small claims courts. … Most courts in the world, including virtually all of the courts in continental Europe and most of the courts in Asia, South America, and Africa, run on an inquisitorial system.

This system is not only used in most of the world, in US administrative law, and in our small claims courts, it was also the main legal system in ancient societies, and it is used today by most non-government dispute-resolution systems, such as in churches, schools, firms, and families. This system is usually paired with a less precedent-based and more text-based system for deciding are the legal rules.

We in Anglo societies are often told that our different more adversarial and precedent-based system is superior, because it less allows corrupt judges. But as the book says,

[In a precedent based legal system] exceptions and balancing tests offer judges great discretion to adjust the law to reach almost any set of facts. They also create a massive amount of uncertainty in the system.

And if the main issue were corrupt judges, we could easily spend far more on that. For example, allow entrapment and pay many to try to bribe judges. Make 10% of court cases be fake cases designed to test judges. Often have several judges review the same case independently, and compare judge ruling stats. Monitor judge activities full time. Billion dollar bounties to those who prove corruption. Death penalties for the guilty.

Today most people simply can’t afford to use the courts to sue, and if accused of a crime they must mostly settle as if guilty, even if they are innocent, all because the system is now crazy expensive. (It didn’t use to be.) Inquisitorial judges would change that, and give most people meaningful access to a legal system to defend themselves.

By the way, requiring legal liability insurance would be another way to make sure both sides have equal access to effective lawyer support.

Here are a few more interesting quotes from the book: Continue reading "Rebooting Justice" »

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Classic Style Intellectual Worlds

The photo above is one I recently took at the Vatican. For me, it illustrates some key principles of classic artistic style. This style tends to be a fractal collection of structures at different scales, structures that frame spaces of many different sizes. Each structure doesn’t use up all local space, but instead leaves open holes for other works of art. Most all space is used by art of some sort, but items that help define larger structures are more homogeneous.

For example, the key arches in this picture have some patterns within them, but these vary less in texture, style, color, and theme, so that the arch itself can be more clearly visible. A similar pattern happens for the art in the spaces between the arches: works in larger spaces can be more complex, with more variations in textures, styles, colors, and themes. In contrast, works in smaller spaces are more constrained to fit well into the patterns around them.

Higher status artists are allocated to fill the larger spaces, where artists are allowed more discretion. This is a sense in which status is correlated with creativity. But its not at all that all artists are being as creative as they can, with the highest status artists capable of the most creativity. Instead, artists are only allowed to be more creative when they are higher status. That is, we don’t so much like high status because that indicates an ability to be creative, we instead like to see creativity because that indicates that the creator was high status.

Now consider this as a metaphor for academia and related intellectual worlds. These worlds make many products that fit into many structures on many different scales, including fields, subfields, topics, theories, and methods. Imagine that the world mainly wants all these products to fit into a pleasing aesthetic structure with an overall classic artistic style. When an individual makes a product and proposes to put it in some particular place within this overall structure, that proposal is accepted or rejected largely on the basis of how well it improves the overall artistic composition.

If so, most individuals will be rewarded for making impressive small variations. For example, if lots of people are talking about how AI will soon take all the jobs, and how a UBI could solve that, then aspiring intellectuals are rewarded for talking about modest variations on such topics. It is not that hard to be very critical of such scenarios, or to talk about very different future problems and solutions. And yes that might create more social value in terms of intellectual progress. But talking about those things clashes with the rest of the conversation, and so doesn’t make as aesthetically pleasing a whole. So you’ll instead want to be the most witty, clever, articulate, inspiring, rigorous person who talks about relatively small variations on what others are talking about.

That is, as an aspiring intellectual, you should mainly imagine yourself bidding to make one of the tiny artworks in the picture above. You’ll need to both stand out in some way, but also make something that fits very closely with nearby works. You may be capable of far greatly creativity, which could lead to greater intellectual progress. At least if anyone were to listen to you, and be tempted to build on your work. But if you aren’t one of the most impressive people doing lots of stuff that fits aesthetically with what others are doing nearby, its quite likely that no one will listen to you.

If you succeed on that usual path, you may someday be allowed more creativity, to contribute something bigger. Perhaps even something that produces real intellectual progress. And then future historians may say yay, what a great system that gives the biggest rewards to the most creative, surely it must be designed to maximize intellectual progress. Which if you are paying attention, you’ll know to be bull. Though you’ll probably also know to keep quiet about, as most everyone around you prefers a more flattering view of how their world contributes to intellectual progress.

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Enforce Common Norms On Elites

In my experience, elites tend to differ in how they adhere to social norms: their behavior is more context-dependent. Ordinary people use relatively simple strategies of being generally nice, tough, silly, serious, etc., strategies that depend on relatively few context variables. That is, they are mostly nice or tough overall. In contrast, elite behavior is far more sensitive to context. Elites are often very nice to some people, and quite mean to others, in ways that can surprise and seem strange to ordinary people.

The obvious explanation is that context-dependence is gives higher payoffs when one has the intelligence, experience, and social training to execute this strategy well. When you can tell which norms will tend to be enforced how when and by whom, then you can adhere strongly to the norms most likely to be enforced, and neglect the others. And skirt right up to the edge of enforcement boundaries. For weakly enforced norms, your power as an elite gives you more ways to threaten retaliation against those who might try to enforce them on you. And for norms that your elite associates are not particularly eager to enforce, you are more likely to be given the benefit of the doubt, and also second and third chances even when you are clearly caught.

One especially important human norm says that we should each do things to promote a general good when doing so is cheap/easy, relative to the gains to others. Applied to our systems, this norm says that we should all do cheap/easy things to make the systems that we share more effective and beneficial to all. This is a weakly enforced norm that elite associates are not particularly eager to enforce.

And so elites do typically neglect this system-improving norm more. Ordinary people look at a broken system, talk a bit out how it might be improved, and even make a few weak moves in such directions. But ordinary people know that elites are in a far better position to make such moves, and they tend to presume that elites are doing what they can. So if nothing is happening, probably nothing can be done. Which often isn’t remotely close to true, given that elites usually see the system-improving norm as one they can safely neglect.

Oh elites tend to be fine with getting out in front of a popular movement for change, if that will help them personally. They’ll even take credit and pretend to have started such a movement, pushing aside the non-elites who actually did. And they are also fine with taking the initiative to propose system changes that are likely to personally benefit themselves and their allies. But otherwise elites give only lip service to the norm that says to make mild efforts to seek good system changes.

This is one of the reasons that I favor making blackmail legal. That is, while one might have laws like libel against making false claims, and laws against privacy invasions such as posting nude picts or stealing your passwords, if you are going to allow people to tell true negative info that they gain through legitimate means, then you should also let them threaten to not tell this info in trade for compensation.

Legalized blackmail of this sort would have only modest effects on ordinary people, who don’t have much money, and who others aren’t that interested in hearing about. But it would have much stronger effects on elites; elites would be found out much more readily when they broke common social norms. They’d be punished for such violations either by the info going public, or by their having to pay blackmail to keep them quiet. Either way, they’d learn to adhere much more strongly to common norms.

Yes, this would cause harm in some areas where popular norms are dysfunctional. Such as norms to never give in to terrorists, or to never consider costs when deciding whether to save lives. Elites would have to push harder to get the public to accept norm changes in such areas, or they’d have to follow dysfunctional norms. But elites would also be pushed to adhere better to the key norm of working to improve systems when that is cheap and easy. Which could be a big win.

Yes trying to improve systems can hurt when proposed improvements are evaluated via naive public impressions on what behavior works well. But efforts to improve via making new small scale trials that are scaled up only when smaller versions work well, that’s much harder to screw up. We need a lot more of that.

Norms aren’t norms if most people don’t support them, via at least not disputing the claim that society is better off when they are enforced. If so, most people must say they expect society to be better off when we find more cost-effective ways to enforced current norms. Such as legalizing blackmail. This doesn’t necessarily result in our choosing to enforce norms more strictly, though this may often be the result. Yes, better norm enforcement can be bad when norms are bad. But in that case it seems better to persuade people to change norms, rather than throwing monkey-wrenches into the gears of norm enforcement.

So let’s hold our elites more accountable to our norms, listen to them when they suggest that we change norms, and especially enforce the norm of working to improve systems. Legalized blackmail could help with getting elites to adhere more closely to common norms.

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Conditional Harberger Tax Games

Baron Georges-Eugène Haussmann … transformed Paris with dazzling avenues, parks and other lasting renovations between 1853 and 1870. … Haussmann… resolved early on to pay generous compensation to [Paris] property owners, and he did. … [He] hoped to repay the larger loans he obtained from the private sector by capturing some of the increased value of properties lining along the roads he built. … [He] did confiscate properties on both sides of his new thoroughfares, and he had their edifices rebuilt. … Council of State … forced him to return these beautifully renovated properties to their original owners, who thus captured all of their increased value. (more)

In my last post I described abstractly how a system of conditional Harberger taxes (CHT) could help deal with zoning and other key city land use decisions. In this post, let me say a bit more about the behaviors I think we’d actually see in such a system. (I’m only considering here such taxes for land and property tied to land.)

First, I while many property owners would personally manage their official declared property values, many others would have them set by an agent or an app. Agents and apps may often come packaged with insurance against various things that can go wrong, such as losing one’s property.

Second, yes, under CHT, sometimes people would (be paid well to) lose their property. This would almost always be because someone else credibly demonstrated that they expect to gain more value from it. Even if owners strategically or mistakenly declare values too low, the feature I suggested of being able to buy back a property by paying a 1% premium would ensure that pricing errors don’t cause property misallocations. The highest value uses of land can change, and one of the big positive features of this system is that it makes the usage changes that should then result easier to achieve. In my mind that’s a feature, not a bug. Yes, owners could buy insurance against the risk of losing a property, though that needn’t result in getting their property back.

In the ancient world, it was common for people to keep the same marriage, home, neighbors, job, family, and religion for their entire life. In the modern world, in contrast, we expect many big changes during our lifetimes. While we can mostly count on family and religion remaining constant, we must accept bigger chances of change to marriages, neighbors, and jobs. Even our software environments change in ways we can’t control when new versions are issued. Renters today accept big risks of home changes, and even home “owners” face big risks due to job and financial risks. All of which seems normal and reasonable. Yes, a few people seem quite obsessed with wanting absolute guarantees on preservation of old property usage, but I can’t sympathize much with such fetishes for inefficient stasis. Continue reading "Conditional Harberger Tax Games" »

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Distant Future Tradeoffs

Over the last day on Twitter, I ran three similar polls. One asked:

Software design today faces many tradeoffs, e.g., getting more X costs less Y, or vice versa. By comparison, will distant future tradeoffs be mostly same ones, about as many but very different ones, far fewer (so usually all good features X,Y are feasible together), or far more?

Four answers were possible: mostly same tradeoffs, as many but mostly new, far fewer tradeoffs, and far more tradeoffs. The other two polls replaced “Software” with “Physical Device” and “Social Institution.”

I now see these four answers as picking out four future scenarios. A world with fewer tradeoffs is Utopian, where you can more get everything you want without having to give up other things. In contrast, a world with many more tradeoffs is more Complex. A world where most of the tradeoffs are like those today is Familiar. And a world where the current tradeoffs are replaced by new ones is Radical.  Using these terms, here are the resulting percentages:

The polls got from 105 to 131 responses each, with an average entry percentage of 25%, so I’m willing to believe differences of 10% or more. The most obvious results here are that only a minority foresee a familiar future in any area, and answers vary greatly; there is little consensus on which scenarios are more likely.

Beyond that, the strongest pattern I see is that respondents foresee more complexity, relative to a utopian lack of tradeoffs, at higher levels of organization. Physical devices are the most utopian, social institutions are the most complex, and software sits in the middle. The other possible result I see is that respondents foresee a less familiar social future. 

I also asked:

Which shapes the world more in the long run: the search for arrangements allowing better compromises regarding many complex tradeoffs, or fights between conflicting groups/values/perspectives?

In response, 43% said search for tradeoffs while 30% said value conflicts, and 27% said hard to tell. So these people see tradeoffs as mattering a lot.  

These respondents seriously disagree with science fiction, which usually describes relatively familiar social worlds in visibly changed physical contexts (and can’t be bothered to have an opinion on software). They instead say that the social world will change the most, becoming the most complex and/or radical. Oh brave new world, that has such institutions in it!

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Most Progress Not In Morals

Everyone without exception believes his own native customs, and the religion he was brought up in, to be the best. Herodotus 440bc

Over the eons, we humans have greatly increased our transportation abilities. Long ago, we mostly walked everywhere. Then over time, we accumulated more ways to move ourselves and goods faster, cheaper, and more reliably, from boats to horses to gondolas to spaceships. Today, for most points A and B, our total cost to move from A to B is orders of magnitude cheaper than it would be via walking.

Even so, walking remains an important part of our transport portfolio. While we are able to move people who can’t walk, such as via wheelchairs, that is expensive and limiting. Yet while walking still matters, improvements in walking have contributed little to our long term gains in transport abilities. Most gains came instead from other transport methods. Most walking gains even came from other areas. For example, we can now walk better due to better boots, lighting, route planners, and paved walkways. Our ability to walk without such aids has improved much less.

As with transport, so with many other areas of life. Our ancient human abilities still matter, but most gains over time have come from other improvements. This applies to both physical and social tech. That is, to our space-time arrangements of physical materials and objects, and also to our arrangements of human actions, info and incentives.

Social scientists often use the term “institutions” broadly to denote relatively stable components social arrangements of actions, info and incentives. Some of the earliest human institutions were language and social norms. We have modestly improved human languages, such as via expanded syntax forms and vocabulary. And over history humans have experimented with a great range of social norms, and also with new ways to enforce them, such as oaths, law, and CCTV.

We still rely greatly on social norms to manage small families, work groups and friend groups. As with walking, while we could probably manage such groups in other ways, doing so would be expensive and limiting. So social norms still matter. But as with our walking, relatively little of our gains overtime has come from improving our ancient institution of social norms.

When humans moved to new environments, such as marshes or antic tundra, they had to adapt their generic walking methods to these new contexts. No doubt learning and innovation was involved in that process. Similarly, we no doubt continue to evolve our social norms and their methods of enforcement to deal with changing social contexts. Even so, social norm innovation seems a small part of total institutional innovation over the eons.

With walking, we seem well aware that walking innovation has only been a small part of total transport innovation. But we humans were built to at least pretend to care a lot about social norms. We consider opinions on and adherence to norms, and the shared values they support, to be central to saying who are “good” or “bad” people, and who we see as in “our people”. So we make norms central to our political fights. And we put great weight on norms when evaluating which societies are good, and whether the world has gotten better over time.

Thus each society tends to see its own origin, and the changes which led to its current norms, as enormously important and positive historical events. But if we stand outside any one society and consider the overall sweep of history, we can’t automatically count these as big contributions to long term innovation. After all, the next society is likely to change norms yet again. Most innovation is in accumulating improvements in all those other social institutions.

Now it is true that we have seen some consistent trends in attitudes and norms over the last few centuries. But wealth has also been rising, and having humans attitudes be naturally conditional on wealth levels seems a much better explanation of this fact than the theory that after a million years of human evolution we suddenly learned how to learn about norms. Yes it is good to adapt norms to changing conditions, but as conditions will likely change yet again, we can’t count that as long term innovation.

In sum: most innovation comes in additions to basic human capacities, not in tweaks to those original capacities. Most transport innovation is not in improved ways to walk, and most social institution innovation is not in better social norms. Even if each society would like to tell itself otherwise. To help the future the most, search more for better institutions, less for better norms.

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