Adversarial design

2024-02-042024-01-30 PNRJ core principles, critique of neoclassical economics, current events, futurism, politics, public policy Adblock Plus, advertising, Amazon, art, Electronic Arts, intellectual property, media, Patreon, patronage, Temu, Ubisoft, WelcomeToCyberpunk

Feb 4 JDN 2460346

Have you noticed how Amazon feels a lot worse lately? Years ago, it was extremely convenient: You’d just search for what you want, it would give you good search results, you could buy what you want and be done. But now you have to slog through “sponsored results” and a bunch of random crap made by no-name companies in China before you can get to what you actually want.

Temu is even worse, and has been from the start: You can’t buy anything on Temu without first being inundated in ads. It’s honestly such an awful experience, I don’t understand why anyone is willing to buy anything from Temu.

#WelcomeToCyberpunk, I guess.

Even some video games have become like this: The free-to-play or “freemium” business model seems to be taking off, where you don’t pay money for the game itself, but then have to deal with ads inside the game trying to sell you additional content, because that’s where the developers actually make their money. And now AAA firms like EA and Ubisoft are talking about going to a subscription-based model where you don’t even own your games anymore. (Fortunately there’s been a lot of backlash against that; I hope it persists.)

Why is this happening? Isn’t capitalism supposed to make life better for consumers? Isn’t competition supposed to make products and services supposed to improve over time?

Well, first of all, these markets are clearly not as competitive as they should be. Amazon has a disturbingly large market share, and while the video game market is more competitive, it’s still dominated by a few very large firms (like EA and Ubisoft).

But I think there’s a deeper problem here, one which may be specific to media content.

What I mean by “media content” here is fairly broad: I would include art, music, writing, journalism, film, and video games.

What all of these things have in common is that they are not physical products (they’re not like a car or a phone that is a single physical object), but they are also not really services either (they aren’t something you just do as an action and it’s done, like a haircut, a surgery, or a legal defense).

Another way of thinking about this is that media content can be copied with zero marginal cost.

Because it can be copied with zero marginal cost, media content can’t simply be made and sold the way that conventional products and services are. There are a few different ways it can be monetized.

The most innocuous way is commission or patronage, where someone pays someone else to create a work because they want that work. This is totally unproblematic. You want a piece of art, you pay an artist, they make it for you; great. Maybe you share copies with the world, maybe you don’t; whatever. It’s good either way.

Unfortunately, it’s hard to sustain most artists and innovators on that model alone. (In a sense I’m using a patronage model, because I have a Patreon. But I’m not making anywhere near enough to live on that way.)

The second way is intellectual property, which I have written about before, and surely will again. If you can enforce limits on who is allowed to copy a work, then you can make a work and sell it for profit without fear of being undercut by someone else who simply copies it and sells it for cheaper. A detailed discussion of that is beyond the scope of this post, but you can read those previous posts, and I can give you the TLDR version: Some degree of intellectual property is probably necessary, but in our current society, it has clearly been taken much too far. I think artists and authors deserve to be able to copyright (or maybe copyleft) their work—but probably not for 70 years after their death.

And then there is a third way, the most insidious way: advertising. If you embed advertisements for other products and services within your content, you can then sell those ad slots for profit. This is how newspapers stay afloat, mainly; subscriptions have never been the majority of their revenue. It’s how TV was supported before cable and streaming—and cable usually has ads too, and streaming is starting to.

There is something fundamentally different about advertising as a service. Whereas most products and services you encounter in a capitalist society are made for you, designed for you to use, advertising it made at you, designed to manipulate you.

I’ve heard it put well this way:

If you’re not paying, you aren’t the customer; you’re the product.

Monetizing content by advertising effectively makes your readers (or viewers, players, etc.) into the product instead of the customer.

I call this effect adversarial design.

I chose this term because it not only conveys the right sense of being an adversary: it also includes the word ‘ad’ and the same Latin root ‘advertere‘ as ‘advertising’.

When a company designs a car or a phone, they want it to appeal to customers—they want you to like it. Yes, they want to take your money; but it’s a mutually beneficial exchange. They get money, you get a product; you’re both happier.

When a company designs an ad, they want it to affect customers—they want you to do what it says, whether you like it or not. And they wouldn’t be doing it if they thought you would buy it anyway—so they are basically trying to make you do something you wouldn’t otherwise have done.

In other words, when designing a product, corporations want to be your friend.

When designing an ad, they become your enemy.

You would absolutely prefer not to have ads. You don’t want your attention taken in this way. But they way that these corporations make money—disgustingly huge sums of money—is by forcing those ads in your face anyway.

Yes, to be fair, there might be some kinds of ads that aren’t too bad. Simple, informative, unobtrusive ads that inform you that something is available you might not otherwise have known about. Movie trailers are like this; people often enjoy watching movie trailers, and they want to see what movies are going to come out next. That’s fine. I have no objection to that.

But it should be clear to anyone who has, um, used the Internet in the past decade that we have gone far, far beyond that sort of advertising. Ads have become aggressive, manipulative, aggravating, and—above all—utterly ubiquitous. You can’t escape them. They’re everywhere. Even when you use ad-block software (which I highly recommend, particularly Adblock Plus—which is free), you still can’t completely escape them.

That’s another thing that should make it pretty clear that there’s something wrong with ads: People are willing to make efforts or even pay money to make ads go away.

Whenever there is a game I like that’s ad-supported but you can pay to make the ads go away, I always feel like I’m being extorted, even if what I have to pay would have been a totally reasonable price for the game. Come on, just sell me the game. Don’t give me the game for free and then make me pay to make it not unpleasant. Don’t add anti-features.

This is clearly not a problem that market competition alone will solve. Even in highly competitive markets, advertising is still ubiquitous, aggressive and manipulative. In fact, competition may even make it worse—a true monopoly wouldn’t need to advertise very much.

Consider Coke and Pepsi ads; they’re actually relatively pleasant, aren’t they? Because all they’re trying to do is remind you and make you thirsty so you’ll buy more of the product you were already buying. They aren’t really trying to get you to buy something you wouldn’t have otherwise. They know that their duopoly is solid, and only a true Black Swan event would unseat their hegemony.

And have you ever seen an ad for your gas company? I don’t think I have—probably because I didn’t have a choice in who my gas company was; there was only one that covered my area. So why bother advertising to me?

If competition won’t fix this, what will? Is there some regulation we could impose that would make advertising less obtrusive? People have tried, without much success. I think imposing an advertising tax would help, but even that might not do enough.

What I really think we need right now is to recognize the problem and invest in solving it. Right now we have megacorporations which are thoroughly (literally) invested in making advertising more obtrusive and more ubiquitous. We need other institutions—maybe government, maybe civil society more generally—that are similarly invested in counteracting it.

Otherwise, it’s only going to get worse.

Reflections at the crossroads

2024-01-222024-01-16 PNRJ critique of neoclassical economics, experimental economics, inequality, personal, science and academia academia, career, Edinburgh, education, Elon Musk, job, journals, publishing, Reinhart, rejection, research, Rogoff, teaching, UC Irvine, United King, United States, University of Edinburgh, University of Michigan, writing

Jan 21 JDN 2460332

When this post goes live, I will have just passed my 36^th birthday. (That means I’ve lived for about 1.1 billion seconds, so in order to be as rich as Elon Musk, I’d need to have made, on average, since birth, $200 per second—$720,000 per hour.)

I certainly feel a lot better turning 36 than I did 35. I don’t have any particular additional accomplishments to point to, but my life has already changed quite a bit, in just that one year: Most importantly, I quit my job at the University of Edinburgh, and I am currently in the process of moving out of the UK and back home to Michigan. (We moved the cat over Christmas, and the movers have already come and taken most of our things away; it’s really just us and our luggage now.)

But I still don’t know how to field the question that people have been asking me since I announced my decision to do this months ago:

“What’s next?”

I’m at a crossroads now, trying to determine which path to take. Actually maybe it’s more like a roundabout; it has a whole bunch of different paths, surely not just two or three. The road straight ahead is labeled “stay in academia”; the others at the roundabout are things like “freelance writing”, “software programming”, “consulting”, and “tabletop game publishing”. There’s one well-paved and superficially enticing road that I’m fairly sure I don’t want to take, labeled “corporate finance”.

Right now, I’m just kind of driving around in circles.

Most people don’t seem to quit their jobs without a clear plan for where they will go next. Often they wait until they have another offer in hand that they intend to take. But when I realized just how miserable that job was making me, I made the—perhaps bold, perhaps courageous, perhaps foolish—decision to get out as soon as I possibly could.

It’s still hard for me to fully understand why working at Edinburgh made me so miserable. Many features of an academic career are very appealing to me. I love teaching, I like doing research; I like the relatively flexible hours (and kinda need them, because of my migraines).

I often construct formal decision models to help me make big choices—generally it’s a linear model, where I simply rate each option by its relative quality in a particular dimension, then try different weightings of all the different dimensions. I’ve used this successfully to pick out cars, laptops, even universities. I’m not entrusting my decisions to an algorithm; I often find myself tweaking the parameters to try to get a particular result—but that in itself tells me what I really want, deep down. (Don’t do that in research—people do, and it’s bad—but if the goal is to make yourself happy, your gut feelings are important too.)

My decision models consistently rank university teaching quite high. It generally only gets beaten by freelance writing—which means that maybe I should give freelance writing another try after all.

And yet, my actual experience at Edinburgh was miserable.

What went wrong?

Well, first of all, I should acknowledge that when I separate out the job “university professor” into teaching and research as separate jobs in my decision model, and include all that goes into both jobs—not just the actual teaching, but the grading and administrative tasks; not just doing the research, but also trying to fund and publish it—they both drop lower on the list, and research drops down a lot.

Also, I would rate them both even lower now, having more direct experience of just how awful the exam-grading, grant-writing and journal-submitting can be.

Designing and then grading an exam was tremendously stressful: I knew that many of my students’ futures rested on how they did on exams like this (especially in the UK system, where exams are absurdly overweighted! In most of my classes, the final exam was at least 60% of the grade!). I struggled mightily to make the exam as fair as I could, all the while knowing that it would never really feel fair and I didn’t even have the time to make it the best it could be. You really can’t assess how well someone understands an entire subject in a multiple-choice exam designed to take 90 minutes. It’s impossible.

The worst part of research for me was the rejection.

I mentioned in a previous post how I am hypersensitive to rejection; applying for grants and submitting to journals was clearly the worst feelings of rejection I’ve felt in any job. It felt like they were evaluting not only the value of my work, but my worth as a scientist. Failure felt like being told that my entire career was a waste of time.

It was even worse than the feeling of rejection in freelance writing (which is one of the few things that my model tells me is bad about freelancing as a career for me, along with relatively low and uncertain income). I think the difference is that a book publisher is saying “We don’t think we can sell it.”—’we’ and ‘sell’ being vital. They aren’t saying “this is a bad book; it shouldn’t exist; writing it was a waste of time.”; they’re just saying “It’s not a subgenre we generally work with.” or “We don’t think it’s what the market wants right now.” or even “I personally don’t care for it.”. They acknowledge their own subjective perspective and the fact that it’s ultimately dependent on forecasting the whims of an extremely fickle marketplace. They aren’t really judging my book, and they certainly aren’t judging me.

But in research publishing, it was different. Yes, it’s all in very polite language, thoroughly spiced with sophisticated jargon (though some reviewers are more tactful than others). But when your grant application gets rejected by a funding agency or your paper gets rejected by a journal, the sense really basically is “This project is not worth doing.”; “This isn’t good science.”; “It was/would be a waste of time and money.”; “This (theory or experiment you’ve spent years working on) isn’t interesting or important.” Nobody ever came out and said those things, nor did they come out and say “You’re a bad economist and you should feel bad.”; but honestly a couple of the reviews did kinda read to me like they wanted to say that. They thought that the whole idea that human beings care about each other is fundamentally stupid and naive and not worth talking about, much less running experiments on.

It isn’t so much that I believed them that my work was bad science. I did make some mistakes along the way (but nothing vital; I’ve seen far worse errors by Nobel Laureates). I didn’t have very large samples (because every person I add to the experiment is money I have to pay, and therefore funding I have to come up with). But overall I do believe that my work is sufficiently rigorous to be worth publishing in scientific journals.

It’s more that I came to feel that my work is considered bad, that the kind of work I wanted to do would forever be an uphill battle against an implacable enemy. I already feel exhausted by that battle, and it had only barely begun. I had thought that behavioral economics was a more successful paradigm by now, that it had largely displaced the neoclassical assumptions that came before it; but I was wrong. Except specifically in journals dedicated to experimental and behavioral economics (of which prestigious journals are few—I quickly exhausted them), it really felt like a lot of the feedback I was getting amounted to, “I refuse to believe your paradigm.”.

Part of the problem, also, was that there simply aren’t that many prestigious journals, and they don’t take that many papers. The top 5 journals—which, for whatever reason, command far more respect than any other journals among economists—each accept only about 5-10% of their submissions. Surely more than that are worth publishing; and, to be fair, much of what they reject probably gets published later somewhere else. But it makes a shockingly large difference in your career how many “top 5s” you have; other publications almost don’t matter at all. So once you don’t get into any of those (which of course I didn’t), should you even bother trying to publish somewhere else?

And what else almost doesn’t matter? Your teaching. As long as you show up to class and grade your exams on time (and don’t, like, break the law or something), research universities basically don’t seem to care how good a teacher you are. That was certainly my experience at Edinburgh. (Honestly even their responses to professors sexually abusing their students are pretty unimpressive.)

Some of the other faculty cared, I could tell; there were even some attempts to build a community of colleagues to support each other in improving teaching. But the administration seemed almost actively opposed to it; they didn’t offer any funding to support the program—they wouldn’t even buy us pizza at the meetings, the sort of thing I had as an undergrad for my activist groups—and they wanted to take the time we spent in such pedagogy meetings out of our grading time (probably because if they didn’t, they’d either have to give us less grading, or some of us would be over our allotted hours and they’d owe us compensation).

And honestly, it is teaching that I consider the higher calling.

The difference between 0 people knowing something and 1 knowing it is called research; the difference between 1 person knowing it and 8 billion knowing it is called education.

Yes, of course, research is important. But if all the research suddenly stopped, our civilization would stagnate at its current level of technology, but otherwise continue unimpaired. (Frankly it might spare us the cyberpunk dystopia/AI apocalypse we seem to be hurtling rapidly toward.) Whereas if all education suddenly stopped, our civilization would slowly decline until it ultimately collapsed into the Stone Age. (Actually it might even be worse than that; even Stone Age cultures pass on knowledge to their children, just not through formal teaching. If you include all the ways parents teach their children, it may be literally true that humans cannot survive without education.)

Yet research universities seem to get all of their prestige from their research, not their teaching, and prestige is the thing they absolutely value above all else, so they devote the vast majority of their energy toward valuing and supporting research rather than teaching. In many ways, the administrators seem to see teaching as an obligation, as something they have to do in order to make money that they can spend on what they really care about, which is research.

As such, they are always making classes bigger and bigger, trying to squeeze out more tuition dollars (well, in this case, pounds) from the same number of faculty contact hours. It becomes impossible to get to know all of your students, much less give them all sufficient individual attention. At Edinburgh they even had the gall to refer to their seminars as “tutorials” when they typically had 20+ students. (That is not tutoring!)And then of course there were the lectures, which often had over 200 students.

I suppose it could be worse: It could be athletics they spend all their money on, like most Big Ten universities. (The University of Michigan actually seems to strike a pretty good balance: they are certainly not hurting for athletic funding, but they also devote sizeable chunks of their budget to research, medicine, and yes, even teaching. And unlike virtually all other varsity athletic programs, University of Michigan athletics turns a profit!)

If all the varsity athletics in the world suddenly disappeared… I’m not convinced we’d be any worse off, actually. We’d lose a source of entertainment, but it could probably be easily replaced by, say, Netflix. And universities could re-focus their efforts on academics, instead of acting like a free training and selection system for the pro leagues. The University of California, Irvine certainly seemed no worse off for its lack of varsity football. (Though I admit it felt a bit strange, even to a consummate nerd like me, to have a varsity League of Legends team.)

They keep making the experience of teaching worse and worse, even as they cut faculty salaries and make our jobs more and more precarious.

That might be what really made me most miserable, knowing how expendable I was to the university. If I hadn’t quit when I did, I would have been out after another semester anyway, and going through this same process a bit later. It wasn’t even that I was denied tenure; it was never on the table in the first place. And perhaps because they knew I wouldn’t stay anyway, they didn’t invest anything in mentoring or supporting me. Ostensibly I was supposed to be assigned a faculty mentor immediately; I know the first semester was crazy because of COVID, but after two and a half years I still didn’t have one. (I had a small research budget, which they reduced in the second year; that was about all the support I got. I used it—once.)

So if I do continue on that “academia” road, I’m going to need to do a lot of things differently. I’m not going to put up with a lot of things that I did. I’ll demand a long-term position—if not tenure-track, at least renewable indefinitely, like a lecturer position (as it is in the US, where the tenure-track position is called “assistant professor” and “lecturer” is permanent but not tenured; in the UK, “lecturers” are tenure-track—except at Oxford, and as of 2021, Cambridge—just to confuse you). Above all, I’ll only be applying to schools that actually have some track record for valuing teaching and supporting their faculty.

And if I can’t find any such positions? Then I just won’t apply at all. I’m not going in with the “I’ll take what I can get” mentality I had last time. Our household finances are stable enough that I can afford to wait awhile.

But maybe I won’t even do that. Maybe I’ll take a different path entirely.

For now, I just don’t know.

The problem with “human capital”

2023-12-042023-11-26 PNRJ core principles, critique of neoclassical economics, ethics Buzz Aldrin, capital, capitalism, Elon Musk, human capital, ideology, investment, labor, Mauritania, neoliberal, net wealth, net worth, profit, slavery, value, worth

Dec 3 JDN 2460282

By now, human capital is a standard part of the economic jargon lexicon. It has even begun to filter down into society at large. Business executives talk frequently about “investing in their employees”. Politicians describe their education policies as “investing in our children”.

The good news: This gives businesses a reason to train their employees, and governments a reason to support education.

The bad news: This is clearly the wrong reason, and it is inherently dehumanizing.

The notion of human capital means treating human beings as if they were a special case of machinery. It says that a business may own and value many forms of productive capital: Land, factories, vehicles, robots, patents, employees.

But wait: Employees?

Businesses don’t own their employees. They didn’t buy them. They can’t sell them. They couldn’t make more of them in another factory. They can’t recycle them when they are no longer profitable to maintain.

And the problem is precisely that they would if they could.

Indeed, they used to. Slavery pre-dates capitalism by millennia, but the two quite successfully coexisted for hundreds of years. From the dawn of civilization up until all too recently, people literally were capital assets—and we now remember it as one of the greatest horrors human beings have ever inflicted upon one another.

Nor is slavery truly defeated; it has merely been weakened and banished to the shadows. The percentage of the world’s population currently enslaved is as low as it has ever been, but there are still millions of people enslaved. In Mauritania, slavery wasn’t even illegal until 1981, and those laws weren’t strictly enforced until 2007. (I had graduated from high school!) One of the most shocking things about modern slavery is how cheaply human beings are willing to sell other human beings; I have bought sandwiches that cost more than some people have paid for other people.

The notion of “human capital” basically says that slavery is the correct attitude to have toward people. It says that we should value human beings for their usefulness, their productivity, their profitability.

Business executives are quite happy to see the world in that way. It makes the way they have spent their lives seem worthwhile—perhaps even best—while allowing them to turn a blind eye to the suffering they have neglected or even caused along the way.

I’m not saying that most economists believe in slavery; on the contrary, economists led the charge of abolitionism, and the reason we wear the phrase “the dismal science” like a badge is that the accusation was first leveled at us for our skepticism toward slavery.

Rather, I’m saying that jargon is not ethically neutral. The names we use for things have power; they affect how people view the world.

This is why I always endeavor to always speak of net wealth rather than net worth—because a billionare is not worth more than other people. I’m not even sure you should speak of the net worth of Tesla Incorporated; perhaps it would be better to simply speak of its net asset value or market capitalization. But at least Tesla is something you can buy and sell (piece by piece). Elon Musk is not.

Likewise, I think we need a new term for the knowledge, skills, training, and expertise that human beings bring to their work. It is clearly extremely important; in fact in some sense it’s the most important economic asset, as it’s the only one that can substitute for literally all the others—and the one that others can least substitute for.

Human ingenuity can’t substitute for air, you say? Tell that to Buzz Aldrin—or the people who were once babies that breathed liquid for their first months of life. Yes, it’s true, you need something for human ingenuity to work with; but it turns out that with enough ingenuity, you may not need much, or even anything in particular. One day we may manufacture the air, water and food we need to live from pure energy—or we may embody our minds in machines that no longer need those things.

Indeed, it is the expansion of human know-how and technology that has been responsible for the vast majority of economic growth. We may work a little harder than many of our ancestors (depending on which ancestors you have in mind), but we accomplish with that work far more than they ever could have, because we know so many things they did not.

All that capital we have now is the work of that ingenuity: Machines, factories, vehicles—even land, if you consider all the ways that we have intentionally reshaped the landscape.

Perhaps, then, what we really need to do is invert the expression:

Humans are not machines. Machines are embodied ingenuity.

We should not think of human beings as capital. We should think of capital as the creation of human beings.

Marx described capital as “embodied labor”, but that’s really less accurate: What makes a robot a robot is much less about the hours spent building it, than the centuries of scientific advancement needed to understand how to make it in the first place. Indeed, if that robot is made by another robot, no human need ever have done any labor on it at all. And its value comes not from the work put into it, but the work that comes out of it.

Like so much of neoliberal ideology, the notion of human capital seems to treat profit and economic growth as inherent ends in themselves. Human beings only become valued insofar as we advance the will of the almighty dollar. We forget that the whole reason we should care about economic growth in the first place is that it benefits people. Money is the means, not the end; people are the end, not the means.

We should not think in terms of “investing in children”, as if they were an asset that was meant to yield a return. We should think of enriching our children—of building a better world for them to live in.

We should not speak of “investing in employees”, as though they were just another asset. We should instead respect employees and seek to treat them with fairness and justice.

That would still give us plenty of reason to support education and training. But it would also give us a much better outlook on the world and our place in it.

You are worth more than your money or your job.

The economy exists for people, not the reverse.

Don’t ever forget that.

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.

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