Productivity can cope with laziness, but not greed

2023-10-082023-10-01 PNRJ core principles, critique of neoclassical economics, ethics, futurism, inequality Ammon Hennecy, anarchism, Bulgaria, Cory Doctorow, crime, economic growth, Ghana, greed, India, laziness, memetics, monastery, post-scarcity, productivity, psychopathy, scarcity, science fiction, sensitivity, signaling, specificity, United States, Ursula Le Guin, utopia, Venezuela

Oct 8 JDN 2460226

At least since Star Trek, it has been a popular vision of utopia: post-scarcity, an economy where goods are so abundant that there is no need for money or any kind of incentive to work, and people can just do what they want and have whatever they want.

It certainly does sound nice. But is it actually feasible? I’ve written about this before.

I’ve been reading some more books set in post-scarcity utopias, including Ursula K. Le Guin (who is a legend) and Cory Doctorow (who is merely pretty good). And it struck me that while there is one major problem of post-scarcity that they seem to have good solutions for, there is another one that they really don’t. (To their credit, neither author totally ignores it; they just don’t seem to see it as an insurmountable obstacle.)

The first major problem is laziness.

A lot of people assume that the reason we couldn’t achieve a post-scarcity utopia is that once your standard of living is no longer tied to your work, people would just stop working. I think this assumption rests on both an overly cynical view of human nature and an overly pessimistic view of technological progress.

Let’s do a thought experiment. If you didn’t get paid, and just had the choice to work or not, for whatever hours you wished, motivated only by the esteem of your peers, your contribution to society, and the joy of a job well done, how much would you work?

I contend it’s not zero. At least for most people, work does provide some intrinsic satisfaction. It’s also probably not as much as you are currently working; otherwise you wouldn’t insist on getting paid. Those are our lower and upper bounds.

Is it 80% of your current work? Perhaps not. What about 50%? Still too high? 20% seems plausible, but maybe you think that’s still too high. Surely it’s at least 10%. Surely you would be willing to work at least a few hours per week at a job you’re good at that you find personally fulfilling. My guess is that it would actually be more than that, because once people were free of the stress and pressure of working for a living, they would be more likely to find careers that truly brought them deep satisfaction and joy.

But okay, to be conservative, let’s estimate that people are only willing to work 10% as much under a system where labor is fully optional and there is no such thing as a wage. What kind of standard of living could we achieve?

Well, at the current level of technology and capital in the United States, per-capita GDP at purchasing power parity is about $80,000. 10% of that is $8,000. This may not sound like a lot, but it’s about how people currently live in Venezuela. India is slightly better, Ghana is slightly worse. This would feel poor to most Americans today, but it’s objectively a better standard of living than most humans have had throughout history, and not much worse than the world average today.

If per-capita GDP growth continues at its current rate of about 1.5% per year for another century, that $80,000 would become $320,000, 10% of which is $32,000—that would put us at the standard of living of present-day Bulgaria, or what the United States was like in the distant past of [checks notes] 1980. That wouldn’t even feel poor. In fact if literally everyone had this standard of living, nearly as many Americans today would be richer as would be poorer, since the current median personal income is only a bit higher than that.

Thus, the utopian authors are right about this one: Laziness is a solvable problem. We may not quite have it solved yet, but it’s on the ropes; a few more major breakthroughs in productivity-enhancing technology and we’ll basically be there.

In fact, on a small scale, this sort of utopian communist anarchy already works, and has for centuries. There are little places, all around the world, where people gather together and live and work in a sustainable, basically self-sufficient way without being motivated by wages or salaries, indeed often without owning any private property at all.

We call these places monasteries.

Granted, life in a monastery clearly isn’t for everyone: I certainly wouldn’t want to live a life of celibacy and constant religious observance. But the long-standing traditions of monastic life in several very different world religions does prove that it’s possible for human beings to live and even flourish in the absence of a profit motive.

Yet the fact that monastic life is so strict turns out to be no coincidence: In a sense, it had to be for the whole scheme to work. I’ll get back to that in a moment.

The second major problem with a post-scarcity utopia is greed.

This is the one that I think is the real barrier. It may not be totally insurmountable, but thus far I have yet to hear any good proposals that would seriously tackle it.

The issue with laziness is that we don’t really want to work as much as we do. But since we do actually want to work a little bit, the question is simply how to make as much as we currently do while working only as much as we want to. Hence, to deal with laziness, all we need to do is be more efficient. That’s something we are shockingly good at; the overall productivity of our labor is now something like 100 times what it was at the dawn of the Industrial Revolution, and still growing all the time.

Greed is different. The issue with greed is that, no matter how much we have, we always want more.

Some people are clearly greedier than others. In fact, I’m even willing to bet that most people’s greed could be kept in check by a society that provided for everyone’s basic needs for free. Yeah, maybe sometimes you’d fantasize about living in a gigantic mansion or going into outer space; but most of the time, most of us could actually be pretty happy as long as we had a roof over our heads and food on our tables. I know that in my own case, my grandest ambitions largely involve fighting global poverty—so if that became a solved problem, my life’s ambition would be basically fulfilled, and I wouldn’t mind so much retiring to a life of simple comfort.

But is everyone like that? This is what anarchists don’t seem to understand. In order for anarchy to work, you need everyone to fit into that society. Most of us or even nearly all of us just won’t cut it.

Ammon Hennecy famously declared: “An anarchist is someone who doesn’t need a cop to make him behave.” But this is wrong. An anarchist is someone who thinks that no one needs a cop to make him behave. And while I am the former, I am not the latter.

Perhaps the problem is that anarchists don’t realize that not everyone is as good as they are. They implicitly apply their own mentality to everyone else, and assume that the only reason anyone ever cheats, steals, or kills is because their circumstances are desperate.

Don’t get me wrong: A lot of crime—perhaps even most crime—is committed by people who are desperate. Improving overall economic circumstances does in fact greatly reduce crime. But there is also a substantial proportion of crime—especially the most serious crimes—which is committed by people who aren’t particularly desperate, they are simply psychopaths. They aren’t victims of circumstance. They’re just evil. And society needs a way to deal with them.

If you set up a society so that anyone can just take whatever they want, there will be some people who take much more than their share. If you have no system of enforcement whatsoever, there’s nothing to stop a psychopath from just taking everything he can get his hands on. And then it really doesn’t matter how productive or efficient you are; whatever you make will simply get taken by whoever is greediest—or whoever is strongest.

In order to avoid that, you need to either set up a system that stops people from taking more than their share, or you need to find a way to exclude people like that from your society entirely.

This brings us back to monasteries. Why are they so strict? Why are the only places where utopian anarchism seems to flourish also places where people have to wear a uniform, swear vows, carry out complex rituals, and continually pledge their fealty to an authority? (Note, by the way, that I’ve also just described life in the military, which also has a lot in common with life in a monastery—and for much the same reasons.)

It’s a selection mechanism. Probably no one consciously thinks of it this way—indeed, it seems to be important to how monasteries work that people are not consciously weighing the costs and benefits of all these rituals. This is probably something that memetically evolved over centuries, rather than anything that was consciously designed. But functionally, that’s what it does: You only get to be part of a monastic community if you are willing to pay the enormous cost of following all these strict rules.

That makes it a form of costly signaling. Psychopaths are, in general, more prone to impulsiveness and short-term thinking. They are therefore less willing than others to bear the immediate cost of donning a uniform and following a ritual in order to get the long-term gains of living in a utopian community. This excludes psychopaths from ever entering the community, and thus protects against their predation.

Even celibacy may be a feature rather than a bug: Psychopaths are also prone to promiscuity. (And indeed, utopian communes that practice free love seem to have a much worse track record of being hijacked by psychopaths than monasteries that require celibacy!)

Of course, lots of people who aren’t psychopaths aren’t willing to pay those costs either—like I said, I’m not. So the selection mechanism is in a sense overly strict: It excludes people who would support the community just fine, but aren’t willing to pay the cost. But in the long run, this turns out to be less harmful than being too permissive and letting your community get hijacked and destroyed by psychopaths.

Yet if our goal is to make a whole society that achieves post-scarcity utopia, we can’t afford to be so strict. We already know that most people aren’t willing to become monks or nuns.

That means that we need a selection mechanism which is more reliable—more precisely, one with higher specificity.

I mentioned this in a previous post in the context of testing for viruses, but it bears repeating. Sensitivity and specificity are two complementary measures of a test’s accuracy. The sensitivity of a test is how likely it is to show positive if the truth is positive. The specificity of a test is how likely it is to show negative if the truth is negative.

As a test of psychopathy, monastic strictness has very high sensitivity: If you are a psychopath, there’s a very high chance it will weed you out. But it has quite low specificity: Even if you’re not a psychopath, there’s still a very high chance you won’t want to become a monk.

For a utopian society to work, we need something that’s more specific, something that won’t exclude a lot of people who don’t deserve to be excluded. But it still needs to have much the same sensitivity, because letting psychopaths into your utopia is a very easy way to let that utopia destroy itself. We do not yet have such a test, nor any clear idea how we might create one.

And that, my friends, is why we can’t have nice things. At least, not yet.

AI and the “generalization faculty”

2023-10-012023-09-24 PNRJ cognitive science, computer science, critique of neoclassical economics, current events, ethics, futurism, inequality, public policy art, artificial general intelligence, artificial intelligence, chess, cognitive science, computer science, cyberpunk, psychology, Salvador Dali, Sydney Harris, technology

Oct 1 JDN 2460219

The phrase “artificial intelligence” (AI) has now become so diluted by overuse that we needed to invent a new term for its original meaning. That term is now “artificial general intelligence” (AGI). In the 1950s, AI meant the hypothetical possibility of creating artificial minds—machines that could genuinely think and even feel like people. Now it means… pathing algorithms in video games and chatbots? The goalposts seem to have moved a bit.

It seems that AGI has always been 20 years away. It was 20 years away 50 years ago, and it will probably be 20 years away 50 years from now. Someday it will really be 20 years away, and then, 20 years after that, it will actually happen—but I doubt I’ll live to see it. (XKCD also offers some insight here: “It has not been conclusively proven impossible.”)

We make many genuine advances in computer technology and software, which have profound effects—both good and bad—on our lives, but the dream of making a person out of silicon always seems to drift ever further into the distance, like a mirage on the desert sand.

Why is this? Why do so many people—even, perhaps especially,experts in the field—keep thinking that we are on the verge of this seminal, earth-shattering breakthrough, and ending up wrong—over, and over, and over again? How do such obviously smart people keep making the same mistake?

I think it may be because, all along, we have been laboring under the tacit assumption of a generalization faculty.

What do I mean by that? By “generalization faculty”, I mean some hypothetical mental capacity that allows you to generalize your knowledge and skills across different domains, so that once you get good at one thing, it also makes you good at other things.

This certainly seems to be how humans think, at least some of the time: Someone who is very good at chess is likely also pretty good at go, and someone who can drive a motorcycle can probably also drive a car. An artist who is good at portraits is probably not bad at landscapes. Human beings are, in fact, able to generalize, at least sometimes.

But I think the mistake lies in imagining that there is just one thing that makes us good at generalizing: Just one piece of hardware or software that allows you to carry over skills from any domain to any other. This is the “generalization faculty”—the imagined faculty that I think we do not have, indeed I think does not exist.

Computers clearly do not have the capacity to generalize. A program that can beat grandmasters at chess may be useless at go, and self-driving software that works on one type of car may fail on another, let alone a motorcycle. An art program that is good at portraits of women can fail when trying to do portraits of men, and produce horrific Daliesque madness when asked to make a landscape.

But if they did somehow have our generalization capacity, then, once they could compete with us at some things—which they surely can, already—they would be able to compete with us at just about everything. So if it were really just one thing that would let them generalize, let them leap from AI to AGI, then suddenly everything would change, almost overnight.

And so this is how the AI hype cycle goes, time and time again:

A computer program is made that does something impressive, something that other computer programs could not do, perhaps even something that human beings are not very good at doing.
If that same prowess could be generalized to other domains, the result would plainly be something on par with human intelligence.
Therefore, the only thing this computer program needs in order to be sapient is a generalization faculty.
Therefore, there is just one more step to AGI! We are nearly there! It will happen any day now!

And then, of course, despite heroic efforts, we are unable to generalize that program’s capabilities except in some very narrow way—even decades after having good chess programs, getting programs to be good at go was a major achievement. We are unable to find the generalization faculty yet again. And the software becomes yet another “AI tool” that we will use to search websites or make video games.

For there never was a generalization faculty to be found. It always was a mirage in the desert sand.

Humans are in fact spectacularly good at generalizing, compared to, well, literally everything else in the known universe. Computers are terrible at it. Animals aren’t very good at it. Just about everything else is totally incapable of it. So yes, we are the best at it.

Yet we, in fact, are not particularly good at it in any objective sense.

In experiments, people often fail to generalize their reasoning even in very basic ways. There’s a famous one where we try to get people to make an analogy between a military tactic and a radiation treatment, and while very smart, creative people often get it quickly, most people are completely unable to make the connection unless you give them a lot of specific hints. People often struggle to find creative solutions to problems even when those solutions seem utterly obvious once you know them.

I don’t think this is because people are stupid or irrational. (To paraphrase Sydney Harris: Compared to what?) I think it is because generalization is hard.

People tend to be much better at generalizing within familiar domains where they have a lot of experience or expertise; this shows that there isn’t just one generalization faculty, but many. We may have a plethora of overlapping generalization faculties that apply across different domains, and can learn to improve some over others.

But it isn’t just a matter of gaining more expertise. Highly advanced expertise is in fact usually more specialized—harder to generalize. A good amateur chess player is probably a good amateur go player, but a grandmaster chess player is rarely a grandmaster go player. Someone who does well in high school biology probably also does well in high school physics, but most biologists are not very good physicists. (And lest you say it’s simply because go and physics are harder: The converse is equally true.)

Humans do seem to have a suite of cognitive tools—some innate hardware, some learned software—that allows us to generalize our skills across domains. But even after hundreds of millions of years of evolving that capacity under the highest possible stakes, we still basically suck at it.

To be clear, I do not think it will take hundreds of millions of years to make AGI—or even millions, or even thousands. Technology moves much, much faster than evolution. But I would not be surprised if it took centuries, and I am confident it will at least take decades.

But we don’t need AGI for AI to have powerful effects on our lives. Indeed, even now, AI is already affecting our lives—in mostly bad ways, frankly, as we seem to be hurtling gleefully toward the very same corporatist cyberpunk dystopia we were warned about in the 1980s.

A lot of technologies have done great things for humanity—sanitation and vaccines, for instance—and even automation can be a very good thing, as increased productivity is how we attained our First World standard of living. But AI in particular seems best at automating away the kinds of jobs human beings actually find most fulfilling, and worsening our already staggering inequality. As a civilization, we really need to ask ourselves why we got automated writing and art before we got automated sewage cleaning or corporate management. (We should also ask ourselves why automated stock trading resulted in even more money for stock traders, instead of putting them out of their worthless parasitic jobs.) There are technological reasons for this, yes; but there are also cultural and institutional ones. Automated teaching isn’t far away, and education will be all the worse for it.

To change our lives, AI doesn’t have to be good at everything. It just needs to be good at whatever we were doing to make a living. AGI may be far away, but the impact of AI is already here.

Indeed, I think this quixotic quest for AGI, and all the concern about how to control it and what effects it will have upon our society, may actually be distracting from the real harms that “ordinary” “boring” AI is already having upon our society. I think a Terminator scenario, where the machines rapidly surpass our level of intelligence and rise up to annihilate us, is quite unlikely. But a scenario where AI puts millions of people out of work with insufficient safety net, triggering economic depression and civil unrest? That could be right around the corner.

Frankly, all it may take is getting automated trucks to work, which could be just a few years. There are nearly 4 million truck drivers in the United States—a full percentage point of employment unto itself. And the Governor of California just vetoed a bill that would require all automated trucks to have human drivers. From an economic efficiency standpoint, his veto makes perfect sense: If the trucks don’t need drivers, why require them? But from an ethical and societal standpoint… what do we do with all the truck drivers!?

The inequality of factor mobility

2023-09-242023-09-17 PNRJ critique of neoclassical economics, public policy, tribal paradigm #Scandinaviaisbetter, Cambodia, capital, capitalism, China, factor mobility, free trade, GDP, immigration, intelelctual property, Ireland, labor, labor productivity, Norwa, Norway, production, productivity, trade, TRIPS accord, United States

Sep 24 JDN 2460212

I’ve written before about how free trade has brought great benefits, but also great costs. It occurred to me this week that there is a fairly simple reason why free trade has never been as good for the world as the models would suggest: Some factors of production are harder to move than others.

To some extent this is due to policy, especially immigration policy. But it isn’t just that.There are certain inherent limitations that render some kinds of inputs more mobile than others.

Broadly speaking, there are five kinds of inputs to production: Land, labor, capital, goods, and—oft forgotten—ideas.

You can of course parse them differently: Some would subdivide different types of labor or capital, and some things are hard to categorize this way. The same product, such as an oven or a car, can be a good or capital depending on how it’s used. (Or, consider livestock: is that labor, or capital? Or perhaps it’s a good? Oddly, it’s often discussed as land, which just seems absurd.) Maybe ideas can be considered a form of capital. There is a whole literature on human capital, which I increasingly find distasteful, because it seems to imply that economists couldn’t figure out how to value human beings except by treating them as a machine or a financial asset.

But this four-way categorization is particularly useful for what I want to talk about today. Because the rate at which those things move is very different.

Ideas move instantly. It takes literally milliseconds to transmit an idea anywhere in the world. This wasn’t always true; in ancient times ideas didn’t move much faster than people, and it wasn’t until the invention of the telegraph that their transit really became instantaneous. But it is certainly true now; once this post is published, it can be read in a hundred different countries in seconds.

Goods move in hours. Air shipping can take a product just about anywhere in less than a day. Sea shipping is a bit slower, but not radically so. It’s never been easier to move goods all around the world, and this has been the great success of free trade.

Capital moves in weeks. Here it might be useful to subdivide different types of capital: It’s surely faster to move an oven or even a car (the more good-ish sort of capital) than it is to move an entire factory (capital par excellence). But all in all, we can move stuff pretty fast these days. If you want to move your factory to China or Indonesia, you can probably get it done in a matter of weeks or at most months.

Labor moves in months. This one is a bit ironic, since it is surely easier to carry a single human person—or even a hundred human people—than all the equipment necessary to run an entire factory. But moving labor isn’t just a matter of physically carrying people from one place to another. It’s not like tourism, where you just pack and go. Moving labor requires uprooting people from where they used to live and letting them settle in a new place. It takes a surprisingly long time to establish yourself in a new environment—frankly even after two years in Edinburgh I’m not sure I quite managed it. And all the additional restrictions we’ve added involving border crossings and immigration laws and visas only make it that much slower.

Land moves never. This one seems perfectly obvious, but is also often neglected. You can’t pick up a mountain, a lake, a forest, or even a corn field and carry it across the border. (Yes, eventually plate tectonics will move our land around—but that’ll be millions of years.) Basically, land stays put—and so do all the natural environments and ecosystems on that land. Land isn’t as important for production as it once was; before industrialization, we were dependent on the land for almost everything. But we absolutely still are dependent on the land! If all the topsoil in the world suddenly disappeared, the economy wouldn’t simply collapse: the human race would face extinction. Moreover, a lot of fixed infrastructure, while technically capital, is no more mobile than land. We couldn’t much more easily move the Interstate Highway System to China than we could move Denali.

So far I have said nothing particularly novel. Yeah, clearly it’s much easier to move a mathematical theorem (if such a thing can even be said to “move”) than it is to move a factory, and much easier to move a factory than to move a forest. So what?

But now let’s consider the impact this has on free trade.

Ideas can move instantly, so free trade in ideas would allow all the world to instantaneously share all ideas. This isn’t quite what happens—but in the Internet age, we’re remarkably close to it. If anything, the world’s governments seem to be doing their best to stop this from happening: One of our most strictly-enforced trade agreements, the TRIPS Accord, is about stopping ideas from spreading too easily. And as far as I can tell, region-coding on media goes against everything free trade stands for, yet here we are. (Why, it’s almost as if these policies are more about corporate profits than they ever were about freedom!)

Goods and capital can move quickly. This is where we have really felt the biggest effects of free trade: Everything in the US says “made in China” because the capital is moved to China and then the goods are moved back to the US.

But it would honestly have made more sense to move all those workers instead. For all their obvious flaws, US institutions and US infrastructure are clearly superior to those in China. (Indeed, consider this: We may be so aware of the flaws because the US is especially transparent.) So, the most absolutely efficient way to produce all those goods would be to leave the factories in the US, and move the workers from China instead. If free trade were to achieve its greatest promises, this is the sort of thing we would be doing.

Of course that is not what we did. There are various reasons for this: A lot of the people in China would rather not have to leave. The Chinese government would not want them to leave. A lot of people in the US would not want them to come. The US government might not want them to come.

Most of these reasons are ultimately political: People don’t want to live around people who are from a different nation and culture. They don’t consider those people to be deserving of the same rights and status as those of their own country.

It may sound harsh to say it that way, but it’s clearly the truth. If the average American person valued a random Chinese person exactly the same as they valued a random other American person, our immigration policy would look radically different. US immigration is relatively permissive by world standards, and that is a great part of American success. Yet even here there is a very stark divide between the citizen and the immigrant.

There are morally and economically legitimate reasons to regulate immigration. There may even be morally and economically legitimate reasons to value those in your own nation above those in other nations (though I suspect they would not justify the degree that most people do). But the fact remains that in terms of pure efficiency, the best thing to do would obviously be to move all the people to the place where productivity is highest and do everything there.

But wouldn’t moving people there reduce the productivity? Yes. Somewhat. If you actually tried to concentrate the entire world’s population into the US, productivity in the US would surely go down. So, okay, fine; stop moving people to a more productive place when it has ceased to be more productive. What this should do is average out all the world’s labor productivity to the same level—but a much higher level than the current world average, and frankly probably quite close to its current maximum.

Once you consider that moving people and things does have real costs, maybe fully equaling productivity wouldn’t make sense. But it would be close. The differences in productivity across countries would be small.

They are not small.

Labor productivity worldwide varies tremendously. I don’t count Ireland, because that’s Leprechaun Economics (this is really US GDP with accounting tricks, not Irish GDP). So the prize for highest productivity goes to Norway, at $100 per worker hour (#ScandinaviaIsBetter). The US is doing the best among large countries, at an impressive $73 per hour. And at the very bottom of the list, we have places like Bangladesh at $4.79 per hour and Cambodia at $3.43 per hour. So, roughly speaking, there is about a 20-to-1 ratio between the most productive and least productive countries.

I could believe that it’s not worth it to move US production at $73 per hour to Norway to get it up to $100 per hour. (For one thing, where would we fit it all?) But I find it far more dubious that it wouldn’t make sense to move most of Cambodia’s labor to the US. (Even all 16 million people is less than what the US added between 2010 and 2020.) Even given the fact that these Cambodian workers are less healthy and less educated than American workers, they would almost certainly be more productive on the other side of the Pacific, quite likely ten times as productive as they are now. Yet we haven’t moved them, and have no plans to.

That leaves the question of whether we will move our capital to them. We have been doing so in China, and it worked (to a point). Before that, we did it in Korea and Japan, and it worked. Cambodia will probably come along sooner or later. For now, that seems to be the best we can do.

But I still can’t shake the thought that the world is leaving trillions of dollars on the table by refusing to move people. The inequality of factor mobility seems to be a big part of the world’s inequality, period.

When maximizing utility doesn’t

2023-06-042023-05-28 PNRJ critique of neoclassical economics, ethics, futurism artificial intelligence, biotechnology, dinosaurs, existential risk, Nick Beckstead, nuclear, philosophy, probability, risk, Teruji Thomas, Toba Event, utility

Jun 4 JDN 2460100

Expected utility theory behaves quite strangely when you consider questions involving mortality.

Nick Beckstead and Teruji Thomas recently published a paper on this: All well-defined utility functions are either reckless in that they make you take crazy risks, or timid in that they tell you not to take even very small risks. It’s starting to make me wonder if utility theory is even the right way to make decisions after all.

Consider a game of Russian roulette where the prize is $1 million. The revolver has 6 chambers, 3 with a bullet. So that’s a 1/2 chance of $1 million, and a 1/2 chance of dying. Should you play?

I think it’s probably a bad idea to play. But the prize does matter; if it were $100 million, or $1 billion, maybe you should play after all. And if it were $10,000, you clearly shouldn’t.

And lest you think that there is no chance of dying you should be willing to accept for any amount of money, consider this: Do you drive a car? Do you cross the street? Do you do anything that could ever have any risk of shortening your lifespan in exchange for some other gain? I don’t see how you could live a remotely normal life without doing so. It might be a very small risk, but it’s still there.

This raises the question: Suppose we have some utility function over wealth; ln(x) is a quite plausible one. What utility should we assign to dying?

The fact that the prize matters means that we can’t assign death a utility of negative infinity. It must be some finite value.

But suppose we choose some value, -V, (so V is positive), for the utility of dying. Then we can find some amount of money that will make you willing to play: ln(x) = V, x = e^(V).

Now, suppose that you have the chance to play this game over and over again. Your marginal utility of wealth will change each time you win, so we may need to increase the prize to keep you playing; but we could do that. The prizes could keep scaling up as needed to make you willing to play. So then, you will keep playing, over and over—and then, sooner or later, you’ll die. So, at each step you maximized utility—but at the end, you didn’t get any utility.

Well, at that point your heirs will be rich, right? So maybe you’re actually okay with that. Maybe there is some amount of money ($1 billion?) that you’d be willing to die in order to ensure your heirs have.

But what if you don’t have any heirs? Or, what if we consider making such a decision as a civilization? What if death means not only the destruction of you, but also the destruction of everything you care about?

As a civilization, are there choices before us that would result in some chance of a glorious, wonderful future, but also some chance of total annihilation? I think it’s pretty clear that there are. Nuclear technology, biotechnology, artificial intelligence. For about the last century, humanity has been at a unique epoch: We are being forced to make this kind of existential decision, to face this kind of existential risk.

It’s not that we were immune to being wiped out before; an asteroid could have taken us out at any time (as happened to the dinosaurs), and a volcanic eruption nearly did. But this is the first time in humanity’s existence that we have had the power to destroy ourselves. This is the first time we have a decision to make about it.

One possible answer would be to say we should never be willing to take any kind of existential risk. Unlike the case of an individual, when we speaking about an entire civilization, it no longer seems obvious that we shouldn’t set the utility of death at negative infinity. But if we really did this, it would require shutting down whole industries—definitely halting all research in AI and biotechnology, probably disarming all nuclear weapons and destroying all their blueprints, and quite possibly even shutting down the coal and oil industries. It would be an utterly radical change, and it would require bearing great costs.

On the other hand, if we should decide that it is sometimes worth the risk, we will need to know when it is worth the risk. We currently don’t know that.

Even worse, we will need some mechanism for ensuring that we don’t take the risk when it isn’t worth it. And we have nothing like such a mechanism. In fact, most of our process of research in AI and biotechnology is widely dispersed, with no central governing authority and regulations that are inconsistent between countries. I think it’s quite apparent that right now, there are research projects going on somewhere in the world that aren’t worth the existential risk they pose for humanity—but the people doing them are convinced that they are worth it because they so greatly advance their national interest—or simply because they could be so very profitable.

In other words, humanity finally has the power to make a decision about our survival, and we’re not doing it. We aren’t making a decision at all. We’re letting that responsibility fall upon more or less randomly-chosen individuals in government and corporate labs around the world. We may be careening toward an abyss, and we don’t even know who has the steering wheel.

Reckoning costs in money distorts them

2023-05-072023-04-30 PNRJ core principles, critique of neoclassical economics, ethics, inequality, market failure, public policy Berkson's paradox, chocolate, climate, cost, HSBC, labor, oil, opportunity cost, poverty, price, QALY, Trump, value, wealth

May 7 JDN 2460072

Consider for a moment what it means when an economic news article reports “rising labor costs”. What are they actually saying?

They’re saying that wages are rising—perhaps in some industry, perhaps in the economy as a whole. But this is not a cost. It’s a price. As I’ve written about before, the two are fundamentally distinct.

The cost of labor is measured in effort, toil, and time. It’s the pain of having to work instead of whatever else you’d like to do with your time.

The price of labor is a monetary amount, which is delivered in a transaction.

This may seem perfectly obvious, but it has important and oft-neglected implications. A cost, one paid, is gone. That value has been destroyed. We hope that it was worth it for some benefit we gained. A price, when paid, is simply transferred: One person had that money before, now someone else has it. Nothing was gained or lost.

So in fact when reports say that “labor costs have risen”, what they are really saying is that income is being transferred from owners to workers without any change in real value taking place. They are framing as a loss what is fundamentally a zero-sum redistribution.

In fact, it is disturbingly common to see a fundamentally good redistribution of income framed in the press as a bad outcome because of its expression as “costs”; the “cost” of chocolate is feared to go up if we insist upon enforcing bans on forced labor—when in fact it is only the price that goes up, and the cost actually goes down: chocolate would no longer include complicity in an atrocity. The real suffering of making chocolate would be thereby reduced, not increased. Even when they aren’t literally enslaved, those workers are astonishingly poor, and giving them even a few more cents per hour would make a real difference in their lives. But God forbid we pay a few cents more for a candy bar!

If labor costs were to rise, that would mean that work had suddenly gotten harder, or more painful; or else, that some outside circumstance had made it more difficult to work. Having a child increases your labor costs—you now have the opportunity cost of not caring for the child. COVID increased the cost of labor, by making it suddenly dangerous just to go outside in public. That could also increase prices—you may demand a higher wage, and people do seem to have demanded higher wages after COVID. But these are two separate effects, and you can have one without the other. In fact, women typically see wage stagnation or even reduction after having kids (but men largely don’t), despite their real opportunity cost of labor having obviously greatly increased.

On an individual level, it’s not such a big mistake to equate price and cost. If you are buying something, its cost to you basically just is its price, plus a little bit of transaction cost for actually finding and buying it. But on a societal level, it makes an enormous difference. It distorts our policy priorities and can even lead to actively trying to suppress things that are beneficial—such as rising wages.

This false equivalence between price and costs seems to be at least as common among economists as it is among laypeople. Economists will often justify it on the grounds that in an ideal perfect competitive market the two would be in some sense equated. But of course we don’t live in that ideal perfect market, and even if we did, they would only beproportional at the margin, not fundamentally equal across the board. It would still be obviously wrong to characterize the total value or cost of work by the price paid for it; only the last unit of effort would be priced so that marginal value equals price equals marginal cost. The first 39 hours of your work would cost you less than what you were paid, and produce more than you were paid; only that 40^th hour would set the three equal.

Once you account for all the various market distortions in the world, there’s no particular relationship between what something costs—in terms of real effort and suffering—and its price—in monetary terms. Things can be expensive and easy, or cheap and awful. In fact, they often seem to be; for some reason, there seems to be a pattern where the most terrible, miserable jobs (e.g. coal mining) actually pay the leastand the easiest, most pleasant jobs (e.g. stock trading) pay the most. Some jobs that benefit society pay well (e.g. doctors) and others pay terribly or not at all (e.g. climate activists). Some actions that harm the world get punished (e.g. armed robbery) and others get rewarded with riches (e.g. oil drilling). In the real world, whether a job is good or bad and whether it is paid well or poorly seem to be almost unrelated.

In fact, sometimes they seem even negatively related, where we often feel tempted to “sell out” and do something destructive in order to get higher pay. This is likely due to Berkson’s paradox: If people are willing to do jobs if they are either high-paying or beneficial to humanity, then we should expect that, on average, most of the high-paying jobs people do won’t be beneficial to humanity. Even if there were inherently no correlation or a small positive one, people’s refusal to do harmful low-paying work removes those jobs from our sample and results in a negative correlation in what remains.

I think that the best solution, ultimately, is to stop reckoning costs in money entirely. We should reckon them in happiness.

This is of course much more difficult than simply using prices; it’s not easy to say exactly how many QALY are sacrificed in the extraction of cocoa beans or the drilling of offshore oil wells. But if we actually did find a way to count them, I strongly suspect we’d find that it was far more than we ought to be willing to pay.

A very rough approximation, surely flawed but at least a start, would be to simply convert all payments into proportions of their recipient’s income: For full-time wages, this would result in basically everyone being counted the same, as 1 hour of work if you work 40 hours per week, 50 weeks per year is precisely 0.05% of your annual income. So we could say that whatever is equivalent to your hourly wage constitutes 50 microQALY.

This automatically implies that every time a rich person pays a poor person, QALY increase, while every time a poor person pays a rich person, QALY decrease. This is not an error in the calculation. It is a fact of the universe. We ignore it only at out own peril. All wealth redistributed downward is a benefit, while all wealth redistributed upward is a harm. That benefit may cause some other harm, or that harm may be compensated by some other benefit; but they are still there.

This would also put some things in perspective. When HSBC was fined £70 million for its crimes, that can be compared against its £1.5 billion in net income; if it were an individual, it would have been hurt about 50 milliQALY, which is about what I would feel if I lost $2000. Of course, it’s not a person, and it’s not clear exactly how this loss was passed through to employees or shareholders; but that should give us at least some sense of how small that loss was for them. They probably felt it… a little.

When Trump was ordered to pay a $1.3 million settlement, based on his $2.5 billion net wealth (corresponding to roughly $125 million in annual investment income), that cost him about 10 milliQALY; for me that would be about $500.

At the other extreme, if someone goes from making $1 per day to making $1.50 per day, that’s a 50% increase in their income—500 milliQALY per year.

For those who have no income at all, this becomes even trickier; for them I think we should probably use their annual consumption, since everyone needs to eat and that costs something, though likely not very much. Or we could try to measure their happiness directly, trying to determine how much it hurts to not eat enough and work all day in sweltering heat.

Properly shifting this whole cultural norm will take a long time. For now, I leave you with this: Any time you see a monetary figure, ask yourself: “How much is that worth to them?” The world will seem quite different once you get in the habit of that.

What behavioral economics needs

2023-04-162023-04-09 PNRJ cognitive science, core principles, critique of neoclassical economics, heuristics and biases, mathematical models, science and academia baseball, behavioral economics, calculus, cognitive science, differential equation, fluid mechanics, Magnus effect, mathematics, maximization, Navier-Stokes equations, optimization, prospect theory, psychology, theory, utility

Apr 16 JDN 2460049

The transition from neoclassical to behavioral economics has been a vital step forward in science. But lately we seem to have reached a plateau, with no major advances in the paradigm in quite some time.

It could be that there is work already being done which will, in hindsight, turn out to be significant enough to make that next step forward. But my fear is that we are getting bogged down by our own methodological limitations.

Neoclassical economics shared with us its obsession with mathematical sophistication. To some extent this was inevitable; in order to impress neoclassical economists enough to convert some of them, we had to use fancy math. We had to show that we could do it their way in order to convince them why we shouldn’t—otherwise, they’d just have dismissed us the way they had dismissed psychologists for decades, as too “fuzzy-headed” to do the “hard work” of putting everything into equations.

But the truth is, putting everything into equations was never the right approach. Because human beings clearly don’t think in equations. Once we write down a utility function and get ready to take its derivative and set it equal to zero, we have already distanced ourselves from how human thought actually works.

When dealing with a simple physical system, like an atom, equations make sense. Nobody thinks that the electron knows the equation and is following it intentionally. That equation simply describes how the forces of the universe operate, and the electron is subject to those forces.

But human beings do actually know things and do things intentionally. And while an equation could be useful for analyzing human behavior in the aggregate—I’m certainly not objecting to statistical analysis—it really never made sense to say that people make their decisions by optimizing the value of some function. Most people barely even know what a function is, much less remember calculus well enough to optimize one.

Yet right now, behavioral economics is still all based in that utility-maximization paradigm. We don’t use the same simplistic utility functions as neoclassical economists; we make them more sophisticated and realistic. Yet in that very sophistication we make things more complicated, more difficult—and thus in at least that respect, even further removed from how actual human thought must operate.

The worst offender here is surely Prospect Theory. I recognize that Prospect Theory predicts human behavior better than conventional expected utility theory; nevertheless, it makes absolutely no sense to suppose that human beings actually do some kind of probability-weighting calculation in their heads when they make judgments. Most of my students—who are well-trained in mathematics and economics—can’t even do that probability-weighting calculation on paper, with a calculator, on an exam. (There’s also absolutely no reason to do it! All it does it make your decisions worse!) This is a totally unrealistic model of human thought.

This is not to say that human beings are stupid. We are still smarter than any other entity in the known universe—computers are rapidly catching up, but they haven’t caught up yet. It is just that whatever makes us smart must not be easily expressible as an equation that maximizes a function. Our thoughts are bundles of heuristics, each of which may be individually quite simple, but all of which together make us capable of not only intelligence, but something computers still sorely, pathetically lack: wisdom. Computers optimize functions better than we ever will, but we still make better decisions than they do.

I think that what behavioral economics needs now is a new unifying theory of these heuristics, which accounts for not only how they work, but how we select which one to use in a given situation, and perhaps even where they come from in the first place. This new theory will of course be complex; there’s a lot of things to explain, and human behavior is a very complex phenomenon. But it shouldn’t be—mustn’t be—reliant on sophisticated advanced mathematics, because most people can’t do advanced mathematics (almost by construction—we would call it something different otherwise). If your model assumes that people are taking derivatives in their heads, your model is already broken. 90% of the world’s people can’t take a derivative.

I guess it could be that our cognitive processes in some sense operate as if they are optimizing some function. This is commonly posited for the human motor system, for instance; clearly baseball players aren’t actually solving differential equations when they throw and catch balls, but the trajectories that balls follow do in fact obey such equations, and the reliability with which baseball players can catch and throw suggests that they are in some sense acting as if they can solve them.

But I think that a careful analysis of even this classic example reveals some deeper insights that should call this whole notion into question. How do baseball players actually do what they do? They don’t seem to be calculating at all—in fact, if you asked them to try to calculate while they were playing, it would destroy their ability to play. They learn. They engage in practiced motions, acquire skills, and notice patterns. I don’t think there is anywhere in their brains that is actually doing anything like solving a differential equation. It’s all a process of throwing and catching, throwing and catching, over and over again, watching and remembering and subtly adjusting.

One thing that is particularly interesting to me about that process is that is astonishingly flexible. It doesn’t really seem to matter what physical process you are interacting with; as long as it is sufficiently orderly, such a method will allow you to predict and ultimately control that process. You don’t need to know anything about differential equations in order to learn in this way—and, indeed, I really can’t emphasize this enough, baseball players typically don’t.

In fact, learning is so flexible that it can even perform better than calculation. The usual differential equations most people would think to use to predict the throw of a ball would assume ballistic motion in a vacuum, which absolutely not what a curveball is. In order to throw a curveball, the ball must interact with the air, and it must be launched with spin; curving a baseball relies very heavily on the Magnus Effect. I think it’s probably possible to construct an equation that would fully predict the motion of a curveball, but it would be a tremendously complicated one, and might not even have an exact closed-form solution. In fact, I think it would require solving the Navier-Stokes equations, for which there is an outstanding Millennium Prize. Since the viscosity of air is very low, maybe you could get away with approximating using the Euler fluid equations.

To be fair, a learning process that is adapting to a system that obeys an equation will yield results that become an ever-closer approximation of that equation. And it is in that sense that a baseball player can be said to be acting as if solving a differential equation. But this relies heavily on the system in question being one that obeys an equation—and when it comes to economic systems, is that even true?

What if the reason we can’t find a simple set of equations that accurately describe the economy (as opposed to equations of ever-escalating complexity that still utterly fail to describe the economy) is that there isn’t one? What if the reason we can’t find the utility function people are maximizing is that they aren’t maximizing anything?

What behavioral economics needs now is a new approach, something less constrained by the norms of neoclassical economics and more aligned with psychology and cognitive science. We should be modeling human beings based on how they actually think, not some weird mathematical construct that bears no resemblance to human reasoning but is designed to impress people who are obsessed with math.

I’m of course not the first person to have suggested this. I probably won’t be the last, or even the one who most gets listened to. But I hope that I might get at least a few more people to listen to it, because I have gone through the mathematical gauntlet and earned my bona fides. It is too easy to dismiss this kind of reasoning from people who don’t actually understand advanced mathematics. But I do understand differential equations—and I’m telling you, that’s not how people think.

Implications of stochastic overload

2023-04-022023-03-26 PNRJ cognitive science, critique of neoclassical economics, heuristics and biases, mathematical models, personal, science and academia academia, cortisol, international relations, neuroeconomics, prestige, publishing, stochastic overload, stress, Yerkes-Dodson

Apr 2 JDN 2460037

A couple weeks ago I presented my stochastic overload model, which posits a neurological mechanism for the Yerkes-Dodson effect: Stress increases sympathetic activation, and this increases performance, up to the point where it starts to risk causing neural pathways to overload and shut down.

This week I thought I’d try to get into some of the implications of this model, how it might be applied to make predictions or guide policy.

One thing I often struggle with when it comes to applying theory is what actual benefits we get from a quantitative mathematical model as opposed to simply a basic qualitative idea. In many ways I think these benefits are overrated; people seem to think that putting something into an equation automatically makes it true and useful. I am sometimes tempted to try to take advantage of this, to put things into equations even though I know there is no good reason to put them into equations, simply because so many people seem to find equations so persuasive for some reason. (Studies have even shown that, particularly in disciplines that don’t use a lot of math, inserting a totally irrelevant equation into a paper makes it more likely to be accepted.)

The basic implications of the Yerkes-Dodson effect are already widely known, and utterly ignored in our society. We know that excessive stress is harmful to health and performance, and yet our entire economy seems to be based around maximizing the amount of stress that workers experience. I actually think neoclassical economics bears a lot of the blame for this, as neoclassical economists are constantly talking about “increasing work incentives”—which is to say, making work life more and more stressful. (And let me remind you that there has never been any shortage of people willing to work in my lifetime, except possibly briefly during the COVID pandemic. The shortage has always been employers willing to hire them.)

I don’t know if my model can do anything to change that. Maybe by putting it into an equation I can make people pay more attention to it, precisely because equations have this weird persuasive power over most people.

As far as scientific benefits, I think that the chief advantage of a mathematical model lies in its ability to make quantitative predictions. It’s one thing to say that performance increases with low levels of stress then decreases with high levels; but it would be a lot more useful if we could actually precisely quantify how much stress is optimal for a given person and how they are likely to perform at different levels of stress.

Unfortunately, the stochastic overload model can only make detailed predictions if you have fully specified the probability distribution of innate activation, which requires a lot of free parameters. This is especially problematic if you don’t even know what type of distribution to use, which we really don’t; I picked three classes of distribution because they were plausible and tractable, not because I had any particular evidence for them.

Also, we don’t even have standard units of measurement for stress; we have a vague notion of what more or less stressed looks like, but we don’t have the sort of quantitative measure that could be plugged into a mathematical model. Probably the best units to use would be something like blood cortisol levels, but then we’d need to go measure those all the time, which raises its own issues. And maybe people don’t even respond to cortisol in the same ways? But at least we could measure your baseline cortisol for awhile to get a prior distribution, and then see how different incentives increase your cortisol levels; and then the model should give relatively precise predictions about how this will affect your overall performance. (This is a very neuroeconomic approach.)

So, for now, I’m not really sure how useful the stochastic overload model is. This is honestly something I feel about a lot of the theoretical ideas I have come up with; they often seem too abstract to be usefully applicable to anything.

Maybe that’s how all theory begins, and applications only appear later? But that doesn’t seem to be how people expect me to talk about it whenever I have to present my work or submit it for publication. They seem to want to know what it’s good for, right now, and I never have a good answer to give them. Do other researchers have such answers? Do they simply pretend to?

Along similar lines, I recently had one of my students ask about a theory paper I wrote on international conflict for my dissertation, and after sending him a copy, I re-read the paper. There are so many pages of equations, and while I am confident that the mathematical logic is valid,I honestly don’t know if most of them are really useful for anything. (I don’t think I really believe that GDP is produced by a Cobb-Douglas production function, and we don’t even really know how to measure capital precisely enough to say.) The central insight of the paper, which I think is really important but other people don’t seem to care about, is a qualitative one: International treaties and norms provide an equilibrium selection mechanism in iterated games. The realists are right that this is cheap talk. The liberals are right that it works. Because when there are many equilibria, cheap talk works.

I know that in truth, science proceeds in tiny steps, building a wall brick by brick, never sure exactly how many bricks it will take to finish the edifice. It’s impossible to see whether your work will be an irrelevant footnote or the linchpin for a major discovery. But that isn’t how the institutions of science are set up. That isn’t how the incentives of academia work. You’re not supposed to say that this may or may not be correct and is probably some small incremental progress the ultimate impact of which no one can possibly foresee. You’re supposed to sell your work—justify how it’s definitely true and why it’s important and how it has impact. You’re supposed to convince other people why they should care about it and not all the dozens of other probably equally-valid projects being done by other researchers.

I don’t know how to do that, and it is agonizing to even try. It feels like lying. It feels like betraying my identity. Being good at selling isn’t just orthogonal to doing good science—I think it’s opposite. I think the better you are at selling your work, the worse you are at cultivating the intellectual humility necessary to do good science. If you think you know all the answers, you’re just bad at admitting when you don’t know things. It feels like in order to succeed in academia, I have to act like an unscientific charlatan.

Honestly, why do we even need to convince you that our work is more important than someone else’s? Are there only so many science points to go around? Maybe the whole problem is this scarcity mindset. Yes, grant funding is limited; but why does publishing my work prevent you from publishing someone else’s? Why do you have to reject 95% of the papers that get sent to you? Don’t tell me you’re limited by space; the journals are digital and searchable and nobody reads the whole thing anyway. Editorial time isn’t infinite, but most of the work has already been done by the time you get a paper back from peer review. Of course, I know the real reason: Excluding people is the main source of prestige.

The role of innate activation in stochastic overload

2023-03-262023-03-19 PNRJ cognitive science, critique of neoclassical economics, heuristics and biases, mathematical models, personal activation, beta distribution, beta function, exponential distribution, model, research, stochastic overload, uniform distribution, Yerkes-Dodson

Mar 26 JDN 2460030

Two posts ago I introduced my stochastic overload model, which offers an explanation for the Yerkes-Dodson effect by positing that additional stress increases sympathetic activation, which is useful up until the point where it starts risking an overload that forces systems to shut down and rest.

The central equation of the model is actually quite simple, expressed either as an expectation or as an integral:

Y = E[x + s | x + s < 1] P[x + s < 1]

Y = \int_{0}^{1-s} (x+s) dF(x)

The amount of output produced is the expected value of innate activation plus stress activation, times the probability that there is no overload. Increased stress raises this expectation value (the incentive effect), but also increases the probability of overload (the overload effect).

The model relies upon assuming that the brain starts with some innate level of activation that is partially random. Exactly what sort of Yerkes-Dodson curve you get from this model depends very much on what distribution this innate activation takes.

I’ve so far solved it for three types of distribution.

The simplest is a uniform distribution, where within a certain range, any level of activation is equally probable. The probability density function looks like this:

Assume the distribution has support between a and b, where a < b.

When b+s < 1, then overload is impossible, and only the incentive effect occurs; productivity increases linearly with stress.

The expected output is simply the expected value of a uniform distribution from a+s to b+s, which is:

E[x + s] = (a+b)/2+s

Then, once b+s > 1, overload risk begins to increase.

In this range, the probability of avoiding overload is:

P[x + s < 1] = F(1-s) = (1-s-a)/(b-a)

(Note that at b+s=1, this is exactly 1.)

The expected value of x+s in this range is:

E[x + s | x + s < 1] = (1-s)(1+s)/(2(b-a))

Multiplying these two together:

Y = [(1-s)(1+s)(1-s-a)]/[2(b-a)^2]

Here is what that looks like for a=0, b=1/2:

It does have the right qualitative features: increasing, then decreasing. But its sure looks weird, doesn’t it? It has this strange kinked shape.

So let’s consider some other distributions.

The next one I was able to solve it for is an exponential distribution, where the most probable activation is zero, and then higher activation always has lower probability than lower activation in an exponential decay:

For this it was actually easiest to do the integral directly (I did it by integrating by parts, but I’m sure you don’t care about all the mathematical steps):

Y = \int_{0}^{1-s} (x+s) dF(x)

Y = (1/λ+s) – (1/ λ + 1)e^(-λ(1-s))

The parameter λdecides how steeply your activation probability decays. Someone with low λ is relatively highly activated all the time, while someone with high λ is usually not highly activated; this seems like it might be related to the personality trait neuroticism.

Here are graphs of what the resulting Yerkes-Dodson curve looks like for several different values of λ:

λ = 0.5:

λ = 1:

λ = 2:

λ = 4:

λ = 8:

The λ = 0.5 person has high activation a lot of the time. They are actually fairly productive even without stress, but stress quickly overwhelms them. The λ = 8 person has low activation most of the time. They are not very productive without stress, but can also bear relatively high amounts of stress without overloading.

(The low-λ people also have overall lower peak productivity in this model, but that might not be true in reality, if λ is inversely correlated with some other attributes that are related to productivity.)

Neither uniform nor exponential has the nice bell-curve shape for innate activation we might have hoped for. There is another class of distributions, beta distributions, which do have this shape, and they are sort of tractable—you need something called an incomplete beta function, which isn’t an elementary function but it’s useful enough that most statistical packages include it.

Beta distributions have two parameters, α and β. They look like this:

Beta distributions are quite useful in Bayesian statistics; if you’re trying to estimate the probability of a random event that either succeeds or fails with a fixed probability (a Bernoulli process), and so far you have observed a successes and b failures, your best guess of its probability at each trial is a beta distribution with α = a+1 and β = b+1.

For beta distributions with parameters α and β, the result comes out to (I is that incomplete beta function I mentioned earlier):

Y = I(1-s, α+1, β) + I(1-s, α, β)

For whole number values of α andβ, the incomplete beta function can be computed by hand (though it is more work the larger they are); here’s an example with α = β = 2.

The innate activation probability looks like this:

And the result comes out like this:

Y = 2(1-s)^3 – 3/2(1-s)^4 + 3s(1-s)^2 – 2s(1-s)^3

This person has pretty high innate activation most of the time, so stress very quickly overwhelms them. If I had chosen a much higher β, I could change that, making them less likely to be innately so activated.

These are the cases I’ve found to be relatively tractable so far. They all have the right qualitative pattern: Increasing stress increases productivity for awhile, then begins decreasing it once overload risk becomes too high. They also show a general pattern where people who are innately highly activated (neurotic?) are much more likely to overload and thus much more sensitive to stress.

What happens when a bank fails

2023-03-192023-03-11 PNRJ critique of neoclassical economics, current events, inequality, macroeconomics, market failure, public policy bank failure, bank run, banking, bankruptcy, central bank, credit, deposits, FDIC, finance, fractional-reserve, monetary policy, money, money multiplier, Positive Money, reserve ratio, Silicon Valley Bank, Washington Mutual

Mar 19 JDN 2460023

As of March 9, Silicon Valley Bank (SVB) has failed and officially been put into receivership under the FDIC. A bank that held $209 billion in assets has suddenly become insolvent.

This is the second-largest bank failure in US history, after Washington Mutual (WaMu) in 2008. In fact it will probably have more serious consequences than WaMu, for two reasons:

1. WaMu collapsed as part of the Great Recession, so there was already a lot of other things going on and a lot of policy responses already in place.

2. WaMu was mostly a conventional commercial bank that held deposits and loans for consumers, so its assets were largely protected by the FDIC, and thus its bankruptcy didn’t cause contagion the spread out to the rest of the system. (Other banks—shadow banks—did during the crash, but not so much WaMu.) SVB mostly served tech startups, so a whopping 89% of its deposits were not protected by FDIC insurance.

You’ve likely heard of many of the companies that had accounts at SVB: Roku, Roblox, Vimeo, even Vox. Stocks of the US financial industry lost $100 billion in value in two days.

The good news is that this will not be catastrophic. It probably won’t even trigger a recession (though the high interest rates we’ve been having lately potentially could drive us over that edge). Because this is commercial banking, it’s done out in the open, with transparency and reasonably good regulation. The FDIC knows what they are doing, and even though they aren’t covering all those deposits directly, they intend to find a buyer for the bank who will, and odds are good that they’ll be able to cover at least 80% of the lost funds.

In fact, while this one is exceptionally large, bank failures are not really all that uncommon. There have been nearly 100 failures of banks with assets over $1 billion in the US alone just since the 1970s. The FDIC exists to handle bank failures, and generally does the job well.

Then again, it’s worth asking whether we should really have a banking system in which failures are so routine.

The reason banks fail is kind of a dark open secret: They don’t actually have enough money to cover their deposits.

Banks loan away most of their cash, and rely upon the fact that most of their depositors will not want to withdraw their money at the same time. They are required to keep a certain ratio in reserves, but it’s usually fairly small, like 10%. This is called fractional-reserve banking.

As long as less than 10% of deposits get withdrawn at any given time, this works. But if a bunch of depositors suddenly decide to take out their money, the bank may not have enough to cover it all, and suddenly become insolvent.

In fact, the fear that a bank might become insolvent can actually cause it to become insolvent, in a self-fulfilling prophecy. Once depositors get word that the bank is about to fail, they rush to be the first to get their money out before it disappears. This is a bank run, and it’s basically what happened to SVB.

The FDIC was originally created to prevent or mitigate bank runs. Not only did they provide insurance that reduced the damage in the event of a bank failure; by assuring depositors that their money would be recovered even if the bank failed, they also reduced the chances of a bank run becoming a self-fulfilling prophecy.

Indeed, SVB is the exception that proves the rule, as they failed largely because their assets were mainly not FDIC insured.

Fractional-reserve banking effectively allows banks to create money, in the form of credit that they offer to borrowers. That credit gets deposited in other banks, which then go on to loan it out to still others; the result is that there is more money in the system than was ever actually printed by the central bank.

In most economies this commercial bank money is a far larger quantity than the central bank money actually printed by the central bank—often nearly 10 to 1. This ratio is called the money multiplier.

Indeed, it’s not a coincidence that the reserve ratio is 10% and the multiplier is 10; the theoretical maximum multiplier is always the inverse of the reserve ratio, so if you require reserves of 10%, the highest multiplier you can get is 10. Had we required 20% reserves, the multiplier would drop to 5.

Most countries have fractional-reserve banking, and have for centuries; but it’s actually a pretty weird system if you think about it.

Back when we were on the gold standard, fractional-reserve banking was a way of cheating, getting our money supply to be larger than the supply of gold would actually allow.

But now that we are on a pure fiat money system, it’s worth asking what fractional-reserve banking actually accomplishes. If we need more money, the central bank could just print more. Why do we delegate that task to commercial banks?

David Friedman of the Cato Institute had some especially harsh words on this, but honestly I find them hard to disagree with:

Before leaving the subject of fractional reserve systems, I should mention one particularly bizarre variant — a fractional reserve system based on fiat money. I call it bizarre because the essential function of a fractional reserve system is to reduce the resource cost of producing money, by allowing an ounce of reserves to replace, say, five ounces of currency. The resource cost of producing fiat money is zero; more precisely, it costs no more to print a five-dollar bill than a one-dollar bill, so the cost of having a larger number of dollars in circulation is zero. The cost of having more bills in circulation is not zero but small. A fractional reserve system based on fiat money thus economizes on the cost of producing something that costs nothing to produce; it adds the disadvantages of a fractional reserve system to the disadvantages of a fiat system without adding any corresponding advantages. It makes sense only as a discreet way of transferring some of the income that the government receives from producing money to the banking system, and is worth mentioning at all only because it is the system presently in use in this country.

Our banking system evolved gradually over time, and seems to have held onto many features that made more sense in an earlier era. Back when we had arbitrarily tied our central bank money supply to gold, creating a new money supply that was larger may have been a reasonable solution. But today, it just seems to be handing the reins over to private corporations, giving them more profits while forcing the rest of society to bear more risk.

The obvious alternative is full-reserve banking, where banks are simply required to hold 100% of their deposits in reserve and the multiplier drops to 1. This idea has been supported by a number of quite prominent economists, including Milton Friedman.

It’s not just a right-wing idea: The left-wing organization Positive Money is dedicated to advocating for a full-reserve banking system in the UK and EU. (The ECB VP’s criticism of the proposal is utterly baffling to me: it “would not create enough funding for investment and growth.” Um, you do know you can print more money, right? Hm, come to think of it, maybe the ECB doesn’t know that, because they think inflation is literally Hitler. There are legitimate criticisms to be had of Positive Money’s proposal, but “There won’t be enough money under this fiat money system” is a really weird take.)

There’s a relatively simple way to gradually transition from our current system to a full-reserve sytem: Simply increase the reserve ratio over time, and print more central bank money to keep the total money supply constant. If we find that it seems to be causing more problems than it solves, we could stop or reverse the trend.

Krugman has pointed out that this wouldn’t really fix the problems in the banking system, which actually seem to be much worse in the shadow banking sector than in conventional commercial banking. This is clearly right, but it isn’t really an argument against trying to improve conventional banking. I guess if stricter regulations on conventional banking push more money into the shadow banking system, that’s bad; but really that just means we should be imposing stricter regulations on the shadow banking system first (or simultaneously).

We don’t need to accept bank runs as a routine part of the financial system. There are other ways of doing things.

Optimization is unstable. Maybe that’s why we satisfice.

2023-02-262023-02-19 PNRJ cognitive science, core principles, critique of neoclassical economics, ethics, heuristics and biases artificial intelligence, constrained optimization, optimization, optimum, paperclipper, satisfice, satisficing, utility

Feb 26 JDN 2460002

Imagine you have become stranded on a deserted island. You need to find shelter, food, and water, and then perhaps you can start working on a way to get help or escape the island.

Suppose you are programmed to be an optimizer—to get the absolute best solution to any problem. At first this may seem to be a boon: You’ll build the best shelter, find the best food, get the best water, find the best way off the island.

But you’ll also expend an enormous amount of effort trying to make it the best. You could spend hours just trying to decide what the best possible shelter would be. You could pass up dozens of viable food sources because you aren’t sure that any of them are the best. And you’ll never get any rest because you’re constantly trying to improve everything.

In principle your optimization could include that: The cost of thinking too hard or searching too long could be one of the things you are optimizing over. But in practice, this sort of bounded optimization is often remarkably intractable.

And what if you forgot about something? You were so busy optimizing your shelter you forgot to treat your wounds. You were so busy seeking out the perfect food source that you didn’t realize you’d been bitten by a venomous snake.

This is not the way to survive. You don’t want to be an optimizer.

No, the person who survives is a satisficer—they make sure that what they have is good enough and then they move on to the next thing. Their shelter is lopsided and ugly. Their food is tasteless and bland. Their water is hard. But they have them.

Once they have shelter and food and water, they will have time and energy to do other things. They will notice the snakebite. They will treat the wound. Once all their needs are met, they will get enough rest.

Empirically, humans are satisficers. We seem to be happier because of it—in fact, the people who are the happiest satisfice the most. And really this shouldn’t be so surprising: Because our ancestral environment wasn’t so different from being stranded on a desert island.

Good enough is perfect. Perfect is bad.

Let’s consider another example. Suppose that you have created a powerful artificial intelligence, an AGI with the capacity to surpass human reasoning. (It hasn’t happened yet—but it probably will someday, and maybe sooner than most people think.)

What do you want that AI’s goals to be?

Okay, ideally maybe they would be something like “Maximize goodness”, where we actually somehow include all the panoply of different factors that go into goodness, like beneficence, harm, fairness, justice, kindness, honesty, and autonomy. Do you have any idea how to do that? Do you even know what your own full moral framework looks like at that level of detail?

Far more likely, the goals you program into the AGI will be much simpler than that. You’ll have something you want it to accomplish, and you’ll tell it to do that well.

Let’s make this concrete and say that you own a paperclip company. You want to make more profits by selling paperclips.

First of all, let me note that this is not an unreasonable thing for you to want. It is not an inherently evil goal for one to have. The world needs paperclips, and it’s perfectly reasonable for you to want to make a profit selling them.

But it’s also not a true ultimate goal: There are a lot of other things that matter in life besides profits and paperclips. Anyone who isn’t a complete psychopath will realize that.

But the AI won’t. Not unless you tell it to. And so if we tell it to optimize, we would need to actually include in its optimization all of the things we genuinely care about—not missing a single one—or else whatever choices it makes are probably not going to be the ones we want. Oops, we forgot to say we need clean air, and now we’re all suffocating. Oops, we forgot to say that puppies don’t like to be melted down into plastic.

The simplest cases to consider are obviously horrific: Tell it to maximize the number of paperclips produced, and it starts tearing the world apart to convert everything to paperclips. (This is the original “paperclipper” concept from Less Wrong.) Tell it to maximize the amount of money you make, and it seizes control of all the world’s central banks and starts printing $9 quintillion for itself. (Why that amount? I’m assuming it uses 64-bit signed integers, and 2^63 is over 9 quintillion. If it uses long ints, we’re even more doomed.) No, inflation-adjusting won’t fix that; even hyperinflation typically still results in more real seigniorage for the central banks doing the printing (which is, you know, why they do it). The AI won’t ever be able to own more than all the world’s real GDP—but it will be able to own that if it prints enough and we can’t stop it.

But even if we try to come up with some more sophisticated optimization for it to perform (what I’m really talking about here is specifying its utility function), it becomes vital for us to include everything we genuinely care about: Anything we forget to include will be treated as a resource to be consumed in the service of maximizing everything else.

Consider instead what would happen if we programmed the AI to satisfice. The goal would be something like, “Produce at least 400,000 paperclips at a price of at most $0.002 per paperclip.”

Given such an instruction, in all likelihood, it would in fact produce exactly 400,000 paperclips at a price of exactly $0.002 per paperclip. And maybe that’s not strictly the best outcome for your company. But if it’s better than what you were previously doing, it will still increase your profits.

Moreover, such an instruction is far less likely to result in the end of the world.

If the AI has a particular target to meet for its production quota and price limit, the first thing it would probably try is to use your existing machinery. If that’s not good enough, it might start trying to modify the machinery, or acquire new machines, or develop its own techniques for making paperclips. But there are quite strict limits on how creative it is likely to be—because there are quite strict limits on how creative it needs to be. If you were previously producing 200,000 paperclips at $0.004 per paperclip, all it needs to do is double production and halve the cost. That’s a very standard sort of industrial innovation— in computing hardware (admittedly an extreme case), we do this sort of thing every couple of years.

It certainly won’t tear the world apart making paperclips—at most it’ll tear apart enough of the world to make 400,000 paperclips, which is a pretty small chunk of the world, because paperclips aren’t that big. A paperclip weighs about a gram, so you’ve only destroyed about 400 kilos of stuff. (You might even survive the lawsuits!)

Are you leaving money on the table relative to the optimization scenario? Eh, maybe. One, it’s a small price to pay for not ending the world. But two, if 400,000 at $0.002 was too easy, next time try 600,000 at $0.001. Over time, you can gently increase its quotas and tighten its price requirements until your company becomes more and more successful—all without risking the AI going completely rogue and doing something insane and destructive.

Of course this is no guarantee of safety—and I absolutely want us to use every safeguard we possibly can when it comes to advanced AGI. But the simple change from optimizing to satisficing seems to solve the most severe problems immediately and reliably, at very little cost.

Good enough is perfect; perfect is bad.

I see broader implications here for behavioral economics. When all of our models are based on optimization, but human beings overwhelmingly seem to satisfice, maybe it’s time to stop assuming that the models are right and the humans are wrong.

Optimization is perfect if it works—and awful if it doesn’t. Satisficing is always pretty good. Optimization is unstable, while satisficing is robust.

In the real world, that probably means that satisficing is better.