How personality makes cognitive science hard

August 13, JDN 2457614

Why is cognitive science so difficult? First of all, let’s acknowledge that it is difficult—that even those of us who understand it better than most are still quite baffled by it in quite fundamental ways. The Hard Problem still looms large over us all, and while I know that the Chinese Room Argument is wrong, I cannot precisely pin down why.

The recursive, reflexive character of cognitive science is part of the problem; can a thing understand itself without understanding understanding itself, understanding understanding understanding itself, and on in an infinite regress? But this recursiveness applies just as much to economics and sociology, and honestly to physics and biology as well. We are physical biological systems in an economic and social system, yet most people at least understand these sciences at the most basic level—which is simply not true of cognitive science.

One of the most basic facts of cognitive science (indeed I am fond of calling it The Basic Fact of Cognitive Science) is that we are our brains, that everything human consciousness does is done by and within the brain. Yet the majority of humans believe in souls (including the majority of Americans and even the majority of Brits), and just yesterday I saw a news anchor say “Based on a new study, that feeling may originate in your brain!” He seriously said “may”. “may”? Why, next you’ll tell me that when my arms lift things, maybe they do it with muscles! Other scientists are often annoyed by how many misconceptions the general public has about science, but this is roughly the equivalent of a news anchor saying, “Based on a new study, human bodies may be made of cells!” or “Based on a new study, diamonds may be made of carbon atoms!” The misunderstanding of many sciences is widespread, but the misunderstanding of cognitive science is fundamental.

So what makes cognitive science so much harder? I have come to realize that there is a deep feature of human personality that makes cognitive science inherently difficult in a way other sciences are not.

Decades of research have uncovered a number of consistent patterns in human personality, where people’s traits tend to lie along a continuum from one extreme to another, and usually cluster near either end. Most people are familiar with a few of these, such as introversion/extraversion and optimism/pessimism; but the one that turns out to be important here is empathizing/systematizing.

Empathizers view the world as composed of sentient beings, living agents with thoughts, feelings, and desires. They are good at understanding other people and providing social support. Poets are typically empathizers.

Systematizers view the world as composed of interacting parts, interlocking components that have complex inner workings which can be analyzed and understood. They are good at solving math problems and tinkering with machines. Engineers are typically systematizers.

Most people cluster near one end of the continuum or the other; they are either strong empathizers or strong systematizers. (If you’re curious, there’s an online test you can take to find out which you are.)

But a rare few of us, perhaps as little as 2% and no more than 10%, are both; we are empathizer-systematizers, strong on both traits (showing that it’s not really a continuum between two extremes after all, and only seemed to be because the two traits are negatively correlated). A comparable number are also low on both traits, which must quite frankly make the world a baffling place in general.

Empathizer-systematizers understand the world as it truly is: Composed of sentient beings that are made of interacting parts.

The very title of this blog shows I am among this group: “human” for the empathizer, “economics” for the systematizer!

We empathizer-systematizers can intuitively grasp that there is no contradiction in saying that a person is sad because he lost his job and he is sad because serotonin levels in his cingulate gyrus are low—because it was losing his job that triggered other thoughts and memories that lowered serotonin levels in his cingulate gyrus and thereby made him sad. No one fully understands the details of how low serotonin feels like sadness—hence, the Hard Problem—but most people can’t even seem to grasp the connection at all. How can something as complex and beautiful as a human mind be made of… sparking gelatin?

Well, what would you prefer it to be made of? Silicon chips? We’re working on that. Something else? Magical fairy dust, perhaps? Pray tell, what material could the human mind be constructed from that wouldn’t bother you on a deep level?

No, what really seems to bother people is the very idea that a human mind can be constructed from material, that thoughts and feelings can be divisible into their constituent parts.

This leads people to adopt one of two extreme positions on cognitive science, both of which are quite absurd—frankly I’m not sure they are even coherent.

Pure empathizers often become dualists, saying that the mind cannot be divisible, cannot be made of material, but must be… something else, somehow, outside the material universe—whatever that means.

Pure systematizers instead often become eliminativists, acknowledging the functioning of the brain and then declaring proudly that the mind does not exist—that consciousness, emotion, and experience are all simply illusions that advanced science will one day dispense with—again, whatever that means.

I can at least imagine what a universe would be like if eliminativism were true and there were no such thing as consciousness—just a vast expanse of stars and rocks and dust, lifeless and empty. Of course, I know that I’m not in such a universe, because I am experiencing consciousness right now, and the illusion of consciousness is… consciousness. (You are not experiencing what you are experiencing right now, I say!) But I can at least visualize what such a universe would be like, and indeed it probably was our universe (or at least our solar system) up until about a billion years ago when the first sentient animals began to evolve.

Dualists, on the other hand, are speaking words, structured into grammatical sentences, but I’m not even sure they are forming coherent assertions. Sure, you can sort of imagine our souls being floating wisps of light and energy (ala the “ascended beings”, my least-favorite part of the Stargate series, which I otherwise love), but ultimately those have to be made of something, because nothing can be both fundamental and complex. Moreover, the fact that they interact with ordinary matter strongly suggests that they are made of ordinary matter (and to be fair to Stargate, at one point in the series Rodney with his already-great intelligence vastly increased declares confidently that ascended beings are indeed nothing more than “protons and electrons, protons and electrons”). Even if they were made of some different kind of matter like dark matter, they would need to obey a common system of physical laws, and ultimately we would come to think of them as matter. Otherwise, how do the two interact? If we are made of soul-stuff which is fundamentally different from other stuff, then how do we even know that other stuff exists? If we are not our bodies, then how do we experience pain when they are damaged and control them with our volition? The most coherent theory of dualism is probably Malebranche’s, which is quite literally “God did it”. Epiphenomenalism, which says that thoughts are just sort of an extra thing that also happens but has no effect (an “epiphenomenon”) on the physical brain, is also quite popular for some reason. People don’t quite seem to understand that the Law of Conservation of Energy directly forbids an “epiphenomenon” in this sense, because anything that happens involves energy, and that energy (unlike, say, money) can’t be created out of nothing; it has to come from somewhere. Analogies are often used: The whistle of a train, the smoke of a flame. But the whistle of a train is a pressure wave that vibrates the train; the smoke from a flame is made of particulates that could be used to smother the flame. At best, there are some phenomena that don’t affect each other very much—but any causal interaction at all makes dualism break down.

How can highly intelligent, highly educated philosophers and scientists make such basic errors? I think it has to be personality. They have deep, built-in (quite likely genetic) intuitions about the structure of the universe, and they just can’t shake them.

And I confess, it’s very hard for me to figure out what to say in order to break those intuitions, because my deep intuitions are so different. Just as it seems obvious to them that the world cannot be this way, it seems obvious to me that it is. It’s a bit like living in a world where 45% of people can see red but not blue and insist the American Flag is red and white, another 45% of people can see blue but not red and insist the flag is blue and white, and I’m here in the 10% who can see all colors and I’m trying to explain that the flag is red, white, and blue.

The best I can come up with is to use analogies, and computers make for quite good analogies, not least because their functioning is modeled on our thinking.

Is this word processor program (LibreOffice Writer, as it turns out) really here, or is it merely an illusion? Clearly it’s really here, right? I’m using it. It’s doing things right now. Parts of it are sort of illusions—it looks like a blank page, but it’s actually an LCD screen lit up all the way; it looks like ink, but it’s actually where the LCD turns off. But there is clearly something here, an actual entity worth talking about which has properties that are usefully described without trying to reduce them to the constituent interactions of subatomic particles.

On the other hand, can it be reduced to the interactions of subatomic particles? Absolutely. A brief sketch is something like this: It’s a software program, running on an operating system, and these in turn are represented in the physical hardware as long binary sequences, stored by ever-so-slightly higher or lower voltages in particular hardware components, which in turn are due to electrons being moved from one valence to another. Those electrons move in precise accordance with the laws of quantum mechanics, I assure you; yet this in no way changes the fact that I’m typing a blog post on a word processor.

Indeed, it’s not even particularly useful to know that the electrons are obeying the laws of quantum mechanics, and quite literally no possible computer that could be constructed in our universe could ever be large enough to fully simulate all these quantum interactions within the amount of time since the dawn of the universe. If we are to understand it at all, it must be at a much higher level—and the “software program” level really seems to be the best one for most circumstances. The vast majority of problems I’m likely to encounter are either at the software level or the macro hardware level; it’s conceivable that a race condition could emerge in the processor cache or the voltage could suddenly spike or even that a cosmic ray could randomly ionize a single vital electron, but these scenarios are far less likely to affect my life than, say, I accidentally deleted the wrong file or the battery ran out of charge because I forgot to plug it in.

Likewise, when dealing with a relationship problem, or mediating a conflict between two friends, it’s rarely relevant that some particular neuron is firing in someone’s nucleus accumbens, or that one of my friends is very low on dopamine in his mesolimbic system today. It could be, particularly if some sort of mental or neurological illness in involved, but in most cases the real issues are better understood as higher level phenomena—people being angry, or tired, or sad. These emotions are ultimately constructed of axon potentials and neurotransmitters, but that doesn’t make them any less real, nor does it change the fact that it is at the emotional level that most human matters are best understood.

Perhaps part of the problem is that human emotions take on moral significance, which other higher-level entities generally do not? But they sort of do, really, in a more indirect way. It matters a great deal morally whether or not climate change is a real phenomenon caused by carbon emissions (it is). Ultimately this moral significance can be tied to human experiences, so everything rests upon human experiences being real; but they are real, in much the same way that rocks and trees and carbon emissions are real. No amount of neuroscience will ever change that, just as no amount of biological science would disprove the existence of trees.

Indeed, some of the world’s greatest moral problems could be better solved if people were better empathizer-systematizers, and thus more willing to do cost-benefit analysis.

The difference between price, cost, and value

JDN 2457559

This topic has been on the voting list for my Patreons for several months, but it never quite seems to win the vote. Well, this time it did. I’m glad, because I was tempted to do it anyway.

“Price”, “cost”, and “value”; the words are often used more or less interchangeably, not only by regular people but even by economists. I’ve read papers that talked about “rising labor costs” when what they clearly meant was rising wages—rising labor prices. I’ve read papers that tried to assess the projected “cost” of climate change by using the prices of different commodity futures. And hardly a day goes buy that I don’t see a TV commercial listing one (purely theoretical) price, cutting it in half (to the actual price), and saying they’re now giving you “more value”.

As I’ll get to, there are reasons to think they would be approximately the same for some purposes. Indeed, they would be equal, at the margin, in a perfectly efficient market—that may be why so many economists use them this way, because they implicitly or explicitly assume efficient markets. But they are fundamentally different concepts, and it’s dangerous to equate them casually.

Price

Price is exactly what you think it is: The number of dollars you must pay to purchase something. Most of the time when we talk about “cost” or “value” and then give a dollar figure, we’re actually talking about some notion of price.

Generally we speak in terms of nominal prices, which are the usual concept of prices in actual dollars paid, but sometimes we do also speak in terms of real prices, which are relative prices of different things once you’ve adjusted for overall inflation. “Inflation-adjusted price” can be a somewhat counter-intuitive concept; if a good’s (nominal) price rises, but by less than most other prices have risen, its real price has actually fallen.

You also need to be careful about just what price you’re looking at. When we look at labor prices, for example, we need to consider not only cash wages, but also fringe benefits and other compensation such as stock options. But other than that, prices are fairly straightforward.

Cost

Cost is probably not at all what you think it is. The real cost of something has nothing to do with money; saying that a candy bar “costs $2” or a computer “costs $2,000” is at best a somewhat sloppy shorthand and at worst a fundamental distortion of what cost is and why it matters. No, those are prices. The cost of a candy bar is the toil of children in cocoa farms in Cote d’Ivoire. The cost of a computer is the ecological damage and displaced indigenous people caused by coltan mining in Congo.

The cost of something is the harm that it does to human well-being (or for that matter to the well-being of any sentient being). It is not measured in money but in “the sweat of our laborers, the genius of our scientists, the hopes of our children” (to quote Eisenhower, who understood real cost better than most economists). There is also opportunity cost, the real cost we pay not by what we did, but by what we didn’t do—what we could have done instead.

This is important precisely because while costs should always be reduced when possible, prices can in fact be too low—and indeed, artificially low prices of goods due to externalities are probably the leading reason why humanity bears so many excess real costs. If the price of that chocolate bar accurately reflected the suffering of those African children (perhaps by—Gasp! Paying them a fair wage?), and the price of that computer accurately reflected the ecological damage of those coltan mines (a carbon tax, at least?), you might not want to buy them anymore; in which case, you should not have bought them. In fact, as I’ll get to once I discuss value, there is reason to think that even if you would buy them at a price that accurately reflected the dollar value of the real cost to their producers, we would still buy more than we should.

There is a point at which we should still buy things even though people get hurt making them; if you deny this, stop buying literally anything ever again. We don’t like to think about it, but any product we buy did cause some person, in some place, some degree of discomfort or unpleasantness in production. And many quite useful products will in fact cause death to a nonzero number of human beings.

For some products this is only barely true—it’s hard to feel bad for bestselling authors and artists who sell their work for millions, for whatever toil they may put into their work, whatever their elevated suicide rate (which is clearly endogenous; people aren’t randomly assigned to be writers), they also surely enjoy it a good deal of the time, and even if they didn’t, their work sells for millions. But for many products it is quite obviously true: A certain proportion of roofers, steelworkers, and truck drivers will die doing their jobs. We can either accept that, recognizing that it’s worth it to have roofs, steel, and trucking—and by extension, industrial capitalism, and its whole babies not dying thing—or we can give up on the entire project of human civilization, and go back to hunting and gathering; even if we somehow managed to avoid the direct homicide most hunter-gatherers engage in, far more people would simply die of disease or get eaten by predators.

Of course, we should have safety standards; but the benefits of higher safety must be carefully weighed against the potential costs of inefficiency, unemployment, and poverty. Safety regulations can reduce some real costs and increase others, even if they almost always increase prices. A good balance is struck when real cost is minimized, where any additional regulation would increase inefficiency more than it improves safety.

Actually OSHA are unsung heroes for their excellent performance at striking this balance, just as EPA are unsung heroes for their balance in environmental regulations (and that whole cutting crime in half business). If activists are mad at you for not banning everything bad and business owners are mad at you for not letting them do whatever they want, you’re probably doing it right. Would you rather people saved from fires, or fires prevented by good safety procedures? Would you rather murderers imprisoned, or boys who grow up healthy and never become murderers? If an ounce of prevention is worth a pound of cure, why does everyone love firefighters and hate safety regulators?So let me take this opportunity to say thank you, OSHA and EPA, for doing the jobs of firefighters and police way better than they do, and unlike them, never expecting to be lauded for it.

And now back to our regularly scheduled programming. Markets are supposed to reflect costs in prices, which is why it’s not totally nonsensical to say “cost” when you mean “price”; but in fact they aren’t very good at that, for reasons I’ll get to in a moment.

Value

Value is how much something is worth—not to sell it (that’s the price again), but to use it. One of the core principles of economics is that trade is nonzero-sum, because people can exchange goods that they value differently and thereby make everyone better off. They can’t price them differently—the buyer and the seller must agree upon a price to make the trade. But they can value them differently.

To see how this works, let’s look at a very simple toy model, the simplest essence of trade: Alice likes chocolate ice cream, but all she has is a gallon of vanilla ice cream. Bob likes vanilla ice cream, but all he has is a gallon of chocolate ice cream. So Alice and Bob agree to trade their ice cream, and both of them are happier.

We can measure value in “willingness-to-pay” (WTP), the highest price you’d willingly pay for something. That makes value look more like a price; but there are several reasons we must be careful when we do that. The obvious reason is that WTP is obviously going to vary based on overall inflation; since $5 isn’t worth as much in 2016 as it was in 1956, something with a WTP of $5 in 1956 would have a much higher WTP in 2016. The not-so-obvious reason is that money is worth less to you the more you have, so we also need to take into account the effect of wealth, and the marginal utility of wealth. The more money you have, the more money you’ll be willing to pay in order to get the same amount of real benefit. (This actually creates some very serious market distortions in the presence of high income inequality, which I may make the subject of a post or even a paper at some point.) Similarly there is “willingness-to-accept” (WTA), the lowest price you’d willingly accept for it. In theory these should be equal; in practice, WTA is usually slightly higher than WTP in what’s called endowment effect.

So to make our model a bit more quantitative, we could suppose that Alice values vanilla at $5 per gallon and chocolate at $10 per gallon, while Bob also values vanilla at $5 per gallon but only values chocolate at $4 per gallon. (I’m using these numbers to point out that not all the valuations have to be different for trade to be beneficial, as long as some are.) Therefore, if Alice sells her vanilla ice cream to Bob for $5, both will (just barely) accept that deal; and then Alice can buy chocolate ice cream from Bob for anywhere between $4 and $10 and still make both people better off. Let’s say they agree to also sell for $5, so that no net money is exchanged and it is effectively the same as just trading ice cream for ice cream. In that case, Alice has gained $5 in consumer surplus (her WTP of $10 minus the $5 she paid) while Bob has gained $1 in producer surplus (the $5 he received minus his $4 WTP). The total surplus will be $6 no matter what price they choose, which we can compute directly from Alice’s WTP of $10 minus Bob’s WTA of $4. The price ultimately decides how that total surplus is distributed between the two parties, and in the real world it would very likely be the result of which one is the better negotiator.

The enormous cost of our distorted understanding

(See what I did there?) If markets were perfectly efficient, prices would automatically adjust so that, at the margin, value is equal to price is equal to cost. What I mean by “at the margin” might be clearer with an example: Suppose we’re selling apples. How many apples do you decide to buy? Well, the value of each successive apple to you is lower, the more apples you have (the law of diminishing marginal utility, which unlike most “laws” in economics is actually almost always true). At some point, the value of the next apple will be just barely above what you have to pay for it, so you’ll stop there. By a similar argument, the cost of producing apples increases the more apples you produce (the law of diminishing returns, which is a lot less reliable, more like the Pirate Code), and the producers of apples will keep selling them until the price they can get is only just barely larger than the cost of production. Thus, in the theoretical limit of infinitely-divisible apples and perfect rationality, marginal value = price = marginal cost. In such a world, markets are perfectly efficient and they maximize surplus, which is the difference between value and cost.

But in the real world of course, none of those assumptions are true. No product is infinitely divisible (though the gasoline in a car is obviously a lot more divisible than the car itself). No one is perfectly rational. And worst of all, we’re not measuring value in the same units. As a result, there is basically no reason to think that markets are optimizing anything; their optimization mechanism is setting two things equal that aren’t measured the same way, like trying to achieve thermal equilibrium by matching the temperature of one thing in Celsius to the temperature of other things in Fahrenheit.

An implicit assumption of the above argument that didn’t even seem worth mentioning was that when I set value equal to price and set price equal to cost, I’m setting value equal to cost; transitive property of equality, right? Wrong. The value is equal to the price, as measured by the buyer. The cost is equal to the price, as measured by the seller.

If the buyer and seller have the same marginal utility of wealth, no problem; they are measuring in the same units. But if not, we convert from utility to money and then back to utility, using a different function to convert each time. In the real world, wealth inequality is massive, so it’s wildly implausible that we all have anything close to the same marginal utility of wealth. Maybe that’s close enough if you restrict yourself to middle-class people in the First World; so when a tutoring client pays me, we might really be getting close to setting marginal value equal to marginal cost. But once you include corporations that are owned by billionaires and people who live on $2 per day, there’s simply no way that those price-to-utility conversions are the same at each end. For Bill Gates, a million dollars is a rounding error. For me, it would buy a house, give me more flexible work options, and keep me out of debt, but not radically change the course of my life. For a child on a cocoa farm in Cote d’Ivoire, it could change her life in ways she can probably not even comprehend.

The market distortions created by this are huge; indeed, most of the fundamental flaws in capitalism as we know it are ultimately traceable to this. Why do Americans throw away enough food to feed all the starving children in Africa? Marginal utility of wealth. Why are Silicon Valley programmers driving the prices for homes in San Francisco higher than most Americans will make in their lifetimes? Marginal utility of wealth. Why are the Koch brothers spending more on this year’s elections than the nominal GDP of the Gambia? Marginal utility of wealth. It’s the sort of pattern that once you see it suddenly seems obvious and undeniable, a paradigm shift a bit like the heliocentric model of the solar system. Forget trade barriers, immigration laws, and taxes; the most important market distortions around the world are all created by wealth inequality. Indeed, the wonder is that markets work as well as they do.

The real challenge is what to do about it, how to reduce this huge inequality of wealth and therefore marginal utility of wealth, without giving up entirely on the undeniable successes of free market capitalism. My hope is that once more people fully appreciate the difference between price, cost, and value, this paradigm shift will be much easier to make; and then perhaps we can all work together to find a solution.

What is the processing power of the human brain?

JDN 2457485

Futurists have been predicting that AI will “surpass humans” any day now for something like 50 years. Eventually they’ll be right, but it will be more or less purely by chance, since they’ve been making the same prediction longer than I’ve been alive. (Similarity, whenever someone projects the date at which immortality will be invented, it always seems to coincide with just slightly before the end of the author’s projected life expectancy.) Any technology that is “20 years away” will be so indefinitely.

There are a lot of reasons why this prediction keeps failing so miserably. One is an apparent failure to grasp the limitations of exponential growth. I actually think the most important is that a lot of AI fans don’t seem to understand how human cognition actually works—that it is primarily social cognition, where most of the processing has already been done and given to us as cached results, some of them derived centuries before we were born. We are smart enough to run a civilization with airplanes and the Internet not because any individual human is so much smarter than any other animal, but because all humans together are—and other animals haven’t quite figured out how to unite their cognition in the same way. We’re about 3 times smarter than any other animal as individuals—and several billion times smarter when we put our heads together.

A third reason is that even if you have sufficient computing power, that is surprisingly unimportant; what you really need are good heuristics to make use of your computing power efficiently. Any nontrivial problem is too complex to brute-force by any conceivable computer, so simply increasing computing power without improving your heuristics will get you nowhere. Conversely, if you have really good heuristics like the human brain does, you don’t even need all that much computing power. A chess grandmaster was once asked how many moves ahead he can see on the board, and he replied: “I only see one move ahead. The right one.” In cognitive science terms, people asked him how much computing power he was using, expecting him to say something far beyond normal human capacity, and he replied that he was using hardly any—it was all baked into the heuristics he had learned from years of training and practice.

Making an AI capable of human thought—a true artificial person—will require a level of computing power we can already reach (as long as we use huge supercomputers), but that is like having the right material. To really create the being we will need to embed the proper heuristics. We are trying to make David, and we have finally mined enough marble—now all we need is Michelangelo.

But another reason why so many futurists have failed in their projections is that they have wildly underestimated the computing power of the human brain. Reading 1980s cyberpunk is hilarious in hindsight; Neuromancer actually quite accurately projected the number of megabytes that would flow through the Internet at any given moment, but somehow thought that a few hundred megaflops would be enough to copy human consciousness. The processing power of the human brain is actually on the order of a few petaflops. So, you know, Gibson was only off by a factor of a few million.

We can now match petaflops—the world’s fastest supercomputer is actually about 30 petaflops. Of course, it cost half a month of China’s GDP to build, and requires 24 megawatts to run and cool, which is about the output of a mid-sized solar power station. The human brain consumes only about 400 kcal per day, which is about 20 watts—roughly the consumption of a typical CFL lightbulb. Even if you count the rest of the human body as necessary to run the human brain (which I guess is sort of true), we’re still clocking in at about 100 watts—so even though supercomputers can now process at the same speed, our brains are almost a million times as energy-efficient.

How do I know it’s a few petaflops?

Earlier this year a study was published showing that a conservative lower bound for the total capacity of human memory is about 4 bits per synapse, where previously some scientists thought that each synapse might carry only 1 bit (I’ve always suspected it was more like 10 myself).

So then we need to figure out how many synapses we have… which turns out to be really difficult actually. They are in a constant state of flux, growing, shrinking, and moving all the time; and when we die they fade away almost immediately (reason #3 I’m skeptical of cryonics). We know that we have about 100 billion neurons, and each one can have anywhere between 100 and 15,000 synapses with other neurons. The average seems to be something like 5,000 (but highly skewed in a power-law distribution), so that’s about 500 trillion synapses. If each one is carrying 4 bits to be as conservative as possible, that’s a total storage capacity of about 2 quadrillion bits, which is about 0.2 petabytes.

Of course, that’s assuming that our brains store information the same way as a computer—every bit flipped independently, each bit stored forever. Not even close. Human memory is constantly compressing and decompressing data, using a compression scheme that’s lossy enough that we not only forget things, we can systematically misremember and even be implanted with false memories. That may seem like a bad thing, and in a sense it is; but if the compression scheme is that lossy, it must be because it’s also that efficient—that our brains are compressing away the vast majority of the data to make room for more. Our best lossy compression algorithms for video are about 100:1; but the human brain is clearly much better than that. Our core data format for long-term memory appears to be narrative; more or less we store everything not as audio or video (that’s short-term memory, and quite literally so), but as stories.

How much compression can you get by storing things as narrative? Think about The Lord of the Rings. The extended edition of the films runs to 6 discs of movie (9 discs of other stuff), where a Blu-Ray disc can store about 50 GB. So that’s 300 GB. Compressed into narrative form, we have the books (which, if you’ve read them, are clearly not optimally compressed—no, we do not need five paragraphs about the trees, and I’m gonna say it, Tom Bombadil is totally superfluous and Peter Jackson was right to remove him), which run about 500,000 words altogether. If the average word is 10 letters (normally it’s less than that, but this is Tolkien we’re talking about), each word will take up about 10 bytes (because in ASCII or Unicode a letter is a byte). So altogether the total content of the entire trilogy, compressed into narrative, can be stored in about 5 million bytes, that is, 5 MB. So the compression from HD video to narrative takes us all the way from 300 GB to 5 MB, which is a factor of 60,000. Sixty thousand. I believe that this is the proper order of magnitude for the compression capability of the human brain.

Even more interesting is the fact that the human brain is almost certainly in some sense holographic storage; damage to a small part of your brain does not produce highly selective memory loss as if you had some bad sectors of your hard drive, but rather an overall degradation of your total memory processing as if you in some sense stored everything everywhere—that is, holographically. How exactly this is accomplished by the brain is still very much an open question; it’s probably not literally a hologram in the quantum sense, but it definitely seems to function like a hologram. (Although… if the human brain is a quantum computer that would explain an awful lot—it especially helps with the binding problem. The problem is explaining how a biological system at 37 C can possibly maintain the necessary quantum coherences.) The data storage capacity of holograms is substantially larger than what can be achieved by conventional means—and furthermore has similar properties to human memory in that you can more or less always add more, but then what you had before gradually gets degraded. Since neural nets are much closer to the actual mechanics of the brain as we know them, understanding human memory will probably involve finding ways to simulate holographic storage with neural nets.

With these facts in mind, the amount of information we can usefully take in and store is probably not 0.2 petabytes—it’s probably more like 10 exabytes. The human brain can probably hold just about as much as the NSA’s National Cybersecurity Initiative Data Center in Utah, which is itself more or less designed to contain the Internet. (The NSA is at once awesome and terrifying.)

But okay, maybe that’s not fair if we’re comparing human brains to computers; even if you can compress all your data by a factor of 100,000, that isn’t the same thing as having 100,000 times as much storage.

So let’s use that smaller figure, 0.2 petabytes. That’s how much we can store; how much can we process?

The next thing to understand is that our processing architecture is fundamentally difference from that of computers.

Computers generally have far more storage than they have processing power, because they are bottlenecked through a CPU that can only process 1 thing at once (okay, like 8 things at once with a hyperthreaded quad-core; as you’ll see in a moment this is a trivial difference). So it’s typical for a new computer these days to have processing power in gigaflops (It’s usually reported in gigahertz, but that’s kind of silly; hertz just tells you clock cycles, while what you really wanted to know is calculations—and that you get from flops. They’re generally pretty comparable numbers though.), while they have storage in terabytes—meaning that it would take about 1000 seconds (about 17 minutes) for the computer to process everything in its entire storage once. In fact it would take a good deal longer than that, because there are further bottlenecks in terms of memory access, especially from hard-disk drives (RAM and solid-state drives are faster, but would still slow it down to a couple of hours).

The human brain, by contrast, integrates processing and memory into the same system. There is no clear distinction between “memory synapses” and “processing synapses”, and no single CPU bottleneck that everything has to go through. There is however something like a “clock cycle” as it turns out; synaptic firings are synchronized across several different “rhythms”, the fastest of which is about 30 Hz. No, not 30 GHz, not 30 MHz, not even 30 kHz; 30 hertz. Compared to the blazing speed of billions of cycles per second that goes on in our computers, the 30 cycles per second our brains are capable of may seem bafflingly slow. (Even more bafflingly slow is the speed of nerve conduction, which is not limited by the speed of light as you might expect, but is actually less than the speed of sound. When you trigger the knee-jerk reflex doctors often test, it takes about a tenth of a second for the reflex to happen—not because your body is waiting for anything, but because it simply takes that long for the signal to travel to your spinal cord and back.)

The reason we can function at all is because of our much more efficient architecture; instead of passing everything through a single bottleneck, we do all of our processing in parallel. All of those 100 billion neurons with 500 trillion synapses storing 2 quadrillion bits work simultaneously. So whereas a computer does 8 things at a time, 3 billion times per second, a human brain does 2 quadrillion things at a time, 30 times per second. Provided that the tasks can be fully parallelized (vision, yes; arithmetic, no), a human brain can therefore process 60 quadrillion bits per second—which turns out to be just over 6 petaflops, somewhere around 6,000,000,000,000,000 calculations per second.

So, like I said, a few petaflops.

Will robots take our jobs?

JDN 2457451
I briefly discussed this topic before, but I thought it deserved a little more depth. Also, the SF author in me really likes writing this sort of post where I get to speculate about futures that are utopian, dystopian, or (most likely) somewhere in between.

The fear is quite widespread, but how realistic is it? Will robots in fact take all our jobs?

Most economists do not think so. Robert Solow famously quipped, “You can see the computer age everywhere but in the productivity statistics.” (It never quite seemed to occur to him that this might be a flaw in the way we measure productivity statistics.)

By the usual measure of labor productivity, robots do not appear to have had a large impact. Indeed, their impact appears to have been smaller than almost any other major technological innovation.

Using BLS data (which was formatted badly and thus a pain to clean, by the way—albeit not as bad as the World Bank data I used on my master’s thesis, which was awful), I made this graph of the growth rate of labor productivity as usually measured:

Productivity_growth

The fluctuations are really jagged due to measurement errors, so I also made an annually smoothed version:

Productivity_growth_smooth

Based on this standard measure, productivity has grown more or less steadily during my lifetime, fluctuating with the business cycle around a value of about 3.5% per year (3.4 log points). If anything, the growth rate seems to be slowing down; in recent years it’s been around 1.5% (1.5 lp).

This was clearly the time during which robots became ubiquitous—autonomous robots did not emerge until the 1970s and 1980s, and robots became widespread in factories in the 1980s. Then there’s the fact that computing power has been doubling every 1.5 years during this period, which is an annual growth rate of 59% (46 lp). So why hasn’t productivity grown at anywhere near that rate?

I think the main problem is that we’re measuring productivity all wrong. We measure it in terms of money instead of in terms of services. Yes, we try to correct for inflation; but we fail to account for the fact that computers have allowed us to perform literally billions of services every day that could not have been performed without them. You can’t adjust that away by plugging into the CPI or the GDP deflator.

Think about it: Your computer provides you the services of all the following:

  1. A decent typesetter and layout artist
  2. A truly spectacular computer (remember, that used to be a profession!)
  3. A highly skilled statistician (who takes no initiative—you must tell her what calculations to do)
  4. A painting studio
  5. A photographer
  6. A video camera operator
  7. A professional orchestra of the highest quality
  8. A decent audio recording studio
  9. Thousands of books, articles, and textbooks
  10. Ideal seats at every sports stadium in the world

And that’s not even counting things like social media and video games that can’t even be readily compared to services that were provided before computers.

If you added up the value of all of those jobs, the amount you would have had to pay in order to hire all those people to do all those things for you before computers existed, your computer easily provides you with at least $1 million in professional services every year. Put another way, your computer has taken jobs that would have provided $1 million in wages. You do the work of a hundred people with the help of your computer.

This isn’t counted in our productivity statistics precisely because it’s so efficient. If we still had to pay that much for all these services, it would be included in our GDP and then our GDP per worker would properly reflect all this work that is getting done. But then… whom would we be paying? And how would we have enough to pay that? Capitalism isn’t actually set up to handle this sort of dramatic increase in productivity—no system is, really—and thus the market price for work has almost no real relation to the productive capacity of the technology that makes that work possible.

Instead it has to do with scarcity of work—if you are the only one in the world who can do something (e.g. write Harry Potter books), you can make an awful lot of money doing that thing, while something that is far more important but can be done by almost anyone (e.g. feed babies) will pay nothing or next to nothing. At best we could say it has to do with marginal productivity, but marginal in the sense of your additional contribution over and above what everyone else could already do—not in the sense of the value actually provided by the work that you are doing. Anyone who thinks that markets automatically reward hard work or “pay you what you’re worth” clearly does not understand how markets function in the real world.

So, let’s ask again: Will robots take our jobs?

Well, they’ve already taken many jobs already. There isn’t even a clear high-skill/low-skill dichotomy here; robots are just as likely to make pharmacists obsolete as they are truck drivers, just as likely to replace surgeons as they are cashiers.

Labor force participation is declining, though slowly:

Labor_force_participation

Yet I think this also underestimates the effect of technology. As David Graeber points out, most of the new jobs we’ve been creating seem to be for lack of a better term bullshit jobs—jobs that really don’t seem like they need to be done, other than to provide people with something to do so that we can justify paying them salaries.

As he puts it:

Again, an objective measure is hard to find, but one easy way to get a sense is to ask: what would happen were this entire class of people to simply disappear? Say what you like about nurses, garbage collectors, or mechanics, it’s obvious that were they to vanish in a puff of smoke, the results would be immediate and catastrophic. A world without teachers or dock-workers would soon be in trouble, and even one without science fiction writers or ska musicians would clearly be a lesser place. It’s not entirely clear how humanity would suffer were all private equity CEOs, lobbyists, PR researchers, actuaries, telemarketers, bailiffs or legal consultants to similarly vanish. (Many suspect it might markedly improve.)

The paragon of all bullshit jobs is sales. Sales is a job that simply should not exist. If something is worth buying, you should be able to present it to the market and people should choose to buy it. If there are many choices for a given product, maybe we could have some sort of independent product rating agencies that decide which ones are the best. But sales means trying to convince people to buy your product—you have an absolutely overwhelming conflict of interest that makes your statements to customers so utterly unreliable that they are literally not even information anymore. The vast majority of advertising, marketing, and sales is thus, in a fundamental sense, literally noise. Sales contributes absolutely nothing to our economy, and because we spend so much effort on it and advertising occupies so much of our time and attention, takes a great deal away. But sales is one of our most steadily growing labor sectors; once we figure out how to make things without people, we employ the people in trying to convince customers to buy the new things we’ve made. Sales is also absolutely miserable for many of the people who do it, as I know from personal experience in two different sales jobs that I had to quit before the end of the first week.

Fortunately we have not yet reached the point where sales is the fastest growing labor sector. Currently the fastest-growing jobs fall into three categories: Medicine, green energy, and of course computers—but actually mostly medicine. Yet even this is unlikely to last; one of the easiest ways to reduce medical costs would be to replace more and more medical staff with automated systems. A nursing robot may not be quite as pleasant as a real professional nurse—but if by switching to robots the hospital can save several million dollars a year, they’re quite likely to do so.

Certain tasks are harder to automate than others—particularly anything requiring creativity and originality is very hard to replace, which is why I believe that in the 2050s or so there will be a Revenge of the Humanities Majors as all the supposedly so stable and forward-thinking STEM jobs disappear and the only jobs that are left are for artists, authors, musicians, game designers and graphic designers. (Also, by that point, very likely holographic designers, VR game designers, and perhaps even neurostim artists.) Being good at math won’t mean anything anymore—frankly it probably shouldn’t right now. No human being, not even great mathematical savants, is anywhere near as good at arithmetic as a pocket calculator. There will still be a place for scientists and mathematicians, but it will be the creative aspects of science and math that persist—design of experiments, development of new theories, mathematical intuition to develop new concepts. The grunt work of cleaning data and churning through statistical models will be fully automated.

Most economists appear to believe that we will continue to find tasks for human beings to perform, and this improved productivity will simply raise our overall standard of living. As any ECON 101 textbook will tell you, “scarcity is a fundamental fact of the universe, because human needs are unlimited and resources are finite.”

In fact, neither of those claims are true. Human needs are not unlimited; indeed, on Maslow’s hierarchy of needs First World countries have essentially reached the point where we could provide the entire population with the whole pyramid, guaranteed, all the time—if we were willing and able to fundamentally reform our economic system.

Resources are not even finite; what constitutes a “resource” depends on technology, as does how accessible or available any given source of resources will be. When we were hunter-gatherers, our only resources were the plants and animals around us. Agriculture turned seeds and arable land into a vital resource. Whale oil used to be a major scarce resource, until we found ways to use petroleum. Petroleum in turn is becoming increasingly irrelevant (and cheap) as solar and wind power mature. Soon the waters of the oceans themselves will be our power source as we refine the deuterium for fusion. Eventually we’ll find we need something for interstellar travel that we used to throw away as garbage (perhaps it will in fact be dilithium!) I suppose that if the universe is finite or if FTL is impossible, we will be bound by what is available in the cosmic horizon… but even that is not finite, as the universe continues to expand! If the universe is open (as it probably is) and one day we can harness the dark energy that seethes through the ever-expanding vacuum, our total energy consumption can grow without bound just as the universe does. Perhaps we could even stave off the heat death of the universe this way—we after all have billions of years to figure out how.

If scarcity were indeed this fundamental law that we could rely on, then more jobs would always continue to emerge, producing whatever is next on the list of needs ordered by marginal utility. Life would always get better, but there would always be more work to be done. But in fact, we are basically already at the point where our needs are satiated; we continue to try to make more not because there isn’t enough stuff, but because nobody will let us have it unless we do enough work to convince them that we deserve it.

We could continue on this route, making more and more bullshit jobs, pretending that this is work that needs done so that we don’t have to adjust our moral framework which requires that people be constantly working for money in order to deserve to live. It’s quite likely in fact that we will, at least for the foreseeable future. In this future, robots will not take our jobs, because we’ll make up excuses to create more.

But that future is more on the dystopian end, in my opinion; there is another way, a better way, the world could be. As technology makes it ever easier to produce as much wealth as we need, we could learn to share that wealth. As robots take our jobs, we could get rid of the idea of jobs as something people must have in order to live. We could build a new economic system: One where we don’t ask ourselves whether children deserve to eat before we feed them, where we don’t expect adults to spend most of their waking hours pushing papers around in order to justify letting them have homes, where we don’t require students to take out loans they’ll need decades to repay before we teach them history and calculus.

This second vision is admittedly utopian, and perhaps in the worst way—perhaps there’s simply no way to make human beings actually live like this. Perhaps our brains, evolved for the all-too-real scarcity of the ancient savannah, simply are not plastic enough to live without that scarcity, and so create imaginary scarcity by whatever means they can. It is indeed hard to believe that we can make so fundamental a shift. But for a Homo erectus in 500,000 BP, the idea that our descendants would one day turn rocks into thinking machines that travel to other worlds would be pretty hard to believe too.

Will robots take our jobs? Let’s hope so.

Nature via Nurture

JDN 2457222 EDT 16:33.

One of the most common “deep questions” human beings have asked ourselves over the centuries is also one of the most misguided, the question of “nature versus nurture”: Is it genetics or environment that makes us what we are?

Humans are probably the single entity in the universe for which this question makes least sense. Artificial constructs have no prior existence, so they are “all nurture”, made what we choose to make them. Most other organisms on Earth behave accordingly to fixed instinctual programming, acting out a specific series of responses that have been honed over millions of years, doing only one thing, but doing it exceedingly well. They are in this sense “all nature”. As the saying goes, the fox knows many things, but the hedgehog knows one very big thing. Most organisms on Earth are in this sense hedgehogs, but we Homo sapiens are the ultimate foxes. (Ironically, hedgehogs are not actually “hedgehogs” in this sense: Being mammals, they have an advanced brain capable of flexibly responding to environmental circumstances. Foxes are a good deal more intelligent still, however.)

But human beings are by far the most flexible, adaptable organism on Earth. We live on literally every continent; despite being savannah apes we even live deep underwater and in outer space. Unlike most other species, we do not fit into a well-defined ecological niche; instead, we carve our own. This certainly has downsides; human beings are ourselves a mass extinction event.

Does this mean, therefore, that we are tabula rasa, blank slates upon which anything can be written?

Hardly. We’re more like word processors. Staring (as I of course presently am) at the blinking cursor of a word processor on a computer screen, seeing that wide, open space where a virtual infinity of possible texts could be written, depending entirely upon a sequence of miniscule key vibrations, you could be forgiven for thinking that you are looking at a blank slate. But in fact you are looking at the pinnacle of thousands of years of technological advancement, a machine so advanced, so precisely engineered, that its individual components are one ten-thousandth the width of a human hair (Intel just announced that we can now do even better than that). At peak performance, it is capable of over 100 billion calculations per second. Its random-access memory stores as much information as all the books on a stacks floor of the Hatcher Graduate Library, and its hard drive stores as much as all the books in the US Library of Congress. (Of course, both libraries contain digital media as well, exceeding anything my humble hard drive could hold by a factor of a thousand.)

All of this, simply to process text? Of course not; word processing is an afterthought for a processor that is specifically designed for dealing with high-resolution 3D images. (Of course, nowadays even a low-end netbook that is designed only for word processing and web browsing can typically handle a billion calculations per second.) But there the analogy with humans is quite accurate as well: Written language is about 10,000 years old, while the human visual mind is at least 100,000. We were 3D image analyzers long before we were word processors. This may be why we say “a picture is worth a thousand words”; we process each with about as much effort, even though the image necessarily contains thousands of times as many bits.

Why is the computer capable of so many different things? Why is the human mind capable of so many more? Not because they are simple and impinged upon by their environments, but because they are complex and precision-engineered to nonlinearly amplify tiny inputs into vast outputs—but only certain tiny inputs.

That is, it is because of our nature that we are capable of being nurtured. It is precisely the millions of years of genetic programming that have optimized the human brain that allow us to learn and adapt so flexibly to new environments and form a vast multitude of languages and cultures. It is precisely the genetically-programmed humanity we all share that makes our environmentally-acquired diversity possible.

In fact, causality also runs the other direction. Indeed, when I said other organisms were “all nature” that wasn’t right either; for even tightly-programmed instincts are evolved through millions of years of environmental pressure. Human beings have even been involved in cultural interactions long enough that it has begun to affect our genetic evolution; the reason I can digest lactose is that my ancestors about 10,000 years ago raised goats. We have our nature because of our ancestors’ nurture.

And then of course there’s the fact that we need a certain minimum level of environmental enrichment even to develop normally; a genetically-normal human raised into a deficient environment will suffer a kind of mental atrophy, as when children raised feral lose their ability to speak.

Thus, the question “nature or nurture?” seems a bit beside the point: We are extremely flexible and responsive to our environment, because of innate genetic hardware and software, which requires a certain environment to express itself, and which arose because of thousands of years of culture and millions of years of the struggle for survival—we are nurture because nature because nurture.

But perhaps we didn’t actually mean to ask about human traits in general; perhaps we meant to ask about some specific trait, like spatial intelligence, or eye color, or gender identity. This at least can be structured as a coherent question: How heritable is the trait? What proportion of the variance in this population is caused by genetic variation? Heritability analysis is a well-established methodology in behavioral genetics.
Yet, that isn’t the same question at all. For while height is extremely heritable within a given population (usually about 80%), human height worldwide has been increasing dramatically over time due to environmental influences and can actually be used as a measure of a nation’s economic development. (Look at what happened to the height of men in Japan.) How heritable is height? You have to be very careful what you mean.

Meanwhile, the heritability of neurofibromatosis is actually quite low—as many people acquire the disease by new mutations as inherit it from their parents—but we know for a fact it is a genetic disorder, because we can point to the specific genes that mutate to cause the disease.

Heritability also depends on the population under consideration; speaking English is more heritable within the United States than it is across the world as a whole, because there are a larger proportion of non-native English speakers in other countries. In general, a more diverse environment will lead to lower heritability, because there are simply more environmental influences that could affect the trait.

As children get older, their behavior gets more heritablea result which probably seems completely baffling, until you understand what heritability really means. Your genes become a more important factor in your behavior as you grow up, because you become separated from the environment of your birth and immersed into the general environment of your whole society. Lower environmental diversity means higher heritability, by definition. There’s also an effect of choosing your own environment; people who are intelligent and conscientious are likely to choose to go to college, where they will be further trained in knowledge and self-control. This latter effect is called niche-picking.

This is why saying something like “intelligence is 80% genetic” is basically meaningless, and “intelligence is 80% heritable” isn’t much better until you specify the reference population. The heritability of intelligence depends very much on what you mean by “intelligence” and what population you’re looking at for heritability. But even if you do find a high heritability (as we do for, say, Spearman’s g within the United States), this doesn’t mean that intelligence is fixed at birth; it simply means that parents with high intelligence are likely to have children with high intelligence. In evolutionary terms that’s all that matters—natural selection doesn’t care where you got your traits, only that you have them and pass them to your offspring—but many people do care, and IQ being heritable because rich, educated parents raise rich, educated children is very different from IQ being heritable because innately intelligent parents give birth to innately intelligent children. If genetic variation is systematically related to environmental variation, you can measure a high heritability even though the genes are not directly causing the outcome.

We do use twin studies to try to sort this out, but because identical twins raised apart are exceedingly rare, two very serious problems emerge: One, there usually isn’t a large enough sample size to say anything useful; and more importantly, this is actually an inaccurate measure in terms of natural selection. The evolutionary pressure is based on the correlation with the genes—it actually doesn’t matter whether the genes are directly causal. All that matters is that organisms with allele X survive and organisms with allele Y do not. Usually that’s because allele X does something useful, but even if it’s simply because people with allele X happen to mostly come from a culture that makes better guns, that will work just as well.

We can see this quite directly: White skin spread across the world not because it was useful (it’s actually terrible in any latitude other than subarctic), but because the cultures that conquered the world happened to be comprised mostly of people with White skin. In the 15th century you’d find a very high heritability of “using gunpowder weapons”, and there was definitely a selection pressure in favor of that trait—but it obviously doesn’t take special genes to use a gun.

The kind of heritability you get from twin studies is answering a totally different, nonsensical question, something like: “If we reassigned all offspring to parents randomly, how much of the variation in this trait in the new population would be correlated with genetic variation?” And honestly, I think the only reason people think that this is the question to ask is precisely because even biologists don’t fully grasp the way that nature and nurture are fundamentally entwined. They are trying to answer the intuitive question, “How much of this trait is genetic?” rather than the biologically meaningful “How strongly could a selection pressure for this trait evolve this gene?”

And if right now you’re thinking, “I don’t care how strongly a selection pressure for the trait could evolve some particular gene”, that’s fine; there are plenty of meaningful scientific questions that I don’t find particularly interesting and are probably not particularly important. (I hesitate to provide a rigid ranking, but I think it’s safe to say that “How does consciousness arise?” is a more important question than “Why are male platypuses venomous?” and “How can poverty be eradicated?” is a more important question than “How did the aircraft manufacturing duopoly emerge?”) But that’s really the most meaningful question we can construct from the ill-formed question “How much of this trait is genetic?” The next step is to think about why you thought that you were asking something important.

What did you really mean to ask?

For a bald question like, “Is being gay genetic?” there is no meaningful answer. We could try to reformulate it as a meaningful biological question, like “What is the heritability of homosexual behavior among males in the United States?” or “Can we find genetic markers strongly linked to self-identification as ‘gay’?” but I don’t think those are the questions we really meant to ask. I think actually the question we meant to ask was more fundamental than that: Is it legitimate to discriminate against gay people? And here the answer is unequivocal: No, it isn’t. It is a grave mistake to think that this moral question has anything to do with genetics; discrimination is wrong even against traits that are totally environmental (like religion, for example), and there are morally legitimate actions to take based entirely on a person’s genes (the obvious examples all coming from medicine—you don’t treat someone for cystic fibrosis if they don’t actually have it).

Similarly, when we ask the question “Is intelligence genetic?” I don’t think most people are actually interested in the heritability of spatial working memory among young American males. I think the real question they want to ask is about equality of opportunity, and what it would look like if we had it. If success were entirely determined by intelligence and intelligence were entirely determined by genetics, then even a society with equality of opportunity would show significant inequality inherited across generations. Thus, inherited inequality is not necessarily evidence against equality of opportunity. But this is in fact a deeply disingenuous argument, used by people like Charles Murray to excuse systemic racism, sexism, and concentration of wealth.

We didn’t have to say that inherited inequality is necessarily or undeniably evidence against equality of opportunity—merely that it is, in fact, evidence of inequality of opportunity. Moreover, it is far from the only evidence against equality of opportunity; we also can observe the fact that college-educated Black people are no more likely to be employed than White people who didn’t even finish high school, for example, or the fact that otherwise identical resumes with predominantly Black names (like “Jamal”) are less likely to receive callbacks compared to predominantly White names (like “Greg”). We can observe that the same is true for resumes with obviously female names (like “Sarah”) versus obviously male names (like “David”), even when the hiring is done by social scientists. We can directly observe that one-third of the 400 richest Americans inherited their wealth (and if you look closer into the other two-thirds, all of them had some very unusual opportunities, usually due to their family connections—“self-made” is invariably a great exaggeration). The evidence for inequality of opportunity in our society is legion, regardless of how genetics and intelligence are related. In fact, I think that the high observed heritability of intelligence is largely due to the fact that educational opportunities are distributed in a genetically-biased fashion, but I could be wrong about that; maybe there really is a large genetic influence on human intelligence. Even so, that does not justify widespread and directly-measured discrimination. It does not justify a handful of billionaires luxuriating in almost unimaginable wealth as millions of people languish in poverty. Intelligence can be as heritable as you like and it is still wrong for Donald Trump to have billions of dollars while millions of children starve.

This is what I think we need to do when people try to bring up a “nature versus nurture” question. We can certainly talk about the real complexity of the relationship between genetics and environment, which I think are best summarized as “nature via nurture”; but in fact usually we should think about why we are asking that question, and try to find the real question we actually meant to ask.

In honor of Pi Day, I for one welcome our new robot overlords

JDN 2457096 EDT 16:08

Despite my preference to use the Julian Date Number system, it has not escaped my attention that this weekend was Pi Day of the Century, 3/14/15. Yesterday morning we had the Moment of Pi: 3/14/15 9:26:53.58979… We arguably got an encore that evening if we allow 9:00 PM instead of 21:00.

Though perhaps it is a stereotype and/or cheesy segue, pi and associated mathematical concepts are often associated with computers and robots. Robots are an increasing part of our lives, from the industrial robots that manufacture our cars to the precision-timed satellites that provide our GPS navigation. When you want to know how to get somewhere, you pull out your pocket thinking machine and ask it to commune with the space robots who will guide you to your destination.

There are obvious upsides to these robots—they are enormously productive, and allow us to produce great quantities of useful goods at astonishingly low prices, including computers themselves, creating a positive feedback loop that has literally lowered the price of a given amount of computing power by a factor of one trillion in the latter half of the 20th century. We now very much live in the early parts of a cyberpunk future, and it is due almost entirely to the power of computer automation.

But if you know your SF you may also remember another major part of cyberpunk futures aside from their amazing technology; they also tend to be dystopias, largely because of their enormous inequality. In the cyberpunk future corporations own everything, governments are virtually irrelevant, and most individuals can barely scrape by—and that sounds all too familiar, doesn’t it? This isn’t just something SF authors made up; there really are a number of ways that computer technology can exacerbate inequality and give more power to corporations.

Why? The reason that seems to get the most attention among economists is skill-biased technological change; that’s weird because it’s almost certainly the least important. The idea is that computers can automate many routine tasks (no one disputes that part) and that routine tasks tend to be the sort of thing that uneducated workers generally do more often than educated ones (already this is looking fishy; think about accountants versus artists). But educated workers are better at using computers and the computers need people to operate them (clearly true). Hence while uneducated workers are substitutes for computers—you can use the computers instead—educated workers are complements for computers—you need programmers and engineers to make the computers work. As computers get cheaper, their substitutes also get cheaper—and thus wages for uneducated workers go down. But their complements get more valuable—and so wages for educated workers go up. Thus, we get more inequality, as high wages get higher and low wages get lower.

Or, to put it more succinctly, robots are taking our jobs. Not all our jobs—actually they’re creating jobs at the top for software programmers and electrical engineers—but a lot of our jobs, like welders and metallurgists and even nurses. As the technology improves more and more jobs will be replaced by automation.

The theory seems plausible enough—and in some form is almost certainly true—but as David Card has pointed out, this fails to explain most of the actual variation in inequality in the US and other countries. Card is one of my favorite economists; he is also famous for completely revolutionizing the economics of minimum wage, showing that prevailing theory that minimum wages must hurt employment simply doesn’t match the empirical data.

If it were just that college education is getting more valuable, we’d see a rise in income for roughly the top 40%, since over 40% of American adults have at least an associate’s degree. But we don’t actually see that; in fact contrary to popular belief we don’t even really see it in the top 1%. The really huge increases in income for the last 40 years have been at the top 0.01%—the top 1% of 1%.

Many of the jobs that are now automated also haven’t seen a fall in income; despite the fact that high-frequency trading algorithms do what stockbrokers do a thousand times better (“better” at making markets more unstable and siphoning wealth from the rest of the economy that is), stockbrokers have seen no such loss in income. Indeed, they simply appropriate the additional income from those computer algorithms—which raises the question why welders couldn’t do the same thing. And indeed, I’ll get to in a moment why that is exactly what we must do, that the robot revolution must also come with a revolution in property rights and income distribution.

No, the real reasons why technology exacerbates inequality are twofold: Patent rents and the winner-takes-all effect.

In an earlier post I already talked about the winner-takes-all effect, so I’ll just briefly summarize it this time around. Under certain competitive conditions, a small fraction of individuals can reap a disproportionate share of the rewards despite being only slightly more productive than those beneath them. This often happens when we have network externalities, in which a product becomes more valuable when more people use it, thus creating a positive feedback loop that makes the products which are already successful wildly so and the products that aren’t successful resigned to obscurity.

Computer technology—more specifically, the Internet—is particularly good at creating such situations. Facebook, Google, and Amazon are all examples of companies that (1) could not exist without Internet technology and (2) depend almost entirely upon network externalities for their business model. They are the winners who take all; thousands of other software companies that were just as good or nearly so are now long forgotten. The winners are not always the same, because the system is unstable; for instance MySpace used to be much more important—and much more profitable—until Facebook came along.

But the fact that a different handful of upper-middle-class individuals can find themselves suddenly and inexplicably thrust into fame and fortune while the rest of us toil in obscurity really isn’t much comfort, now is it? While technically the rise and fall of MySpace can be called “income mobility”, it’s clearly not what we actually mean when we say we want a society with a high level of income mobility. We don’t want a society where the top 10% can by little more than chance find themselves becoming the top 0.01%; we want a society where you don’t have to be in the top 10% to live well in the first place.

Even without network externalities the Internet still nurtures winner-takes-all markets, because digital information can be copied infinitely. When it comes to sandwiches or even cars, each new one is costly to make and costly to transport; it can be more cost-effective to choose the ones that are made near you even if they are of slightly lower quality. But with books (especially e-books), video games, songs, or movies, each individual copy costs nothing to create, so why would you settle for anything but the best? This may well increase the overall quality of the content consumers get—but it also ensures that the creators of that content are in fierce winner-takes-all competition. Hence J.K. Rowling and James Cameron on the one hand, and millions of authors and independent filmmakers barely scraping by on the other. Compare a field like engineering; you probably don’t know a lot of rich and famous engineers (unless you count engineers who became CEOs like Bill Gates and Thomas Edison), but nor is there a large segment of “starving engineers” barely getting by. Though the richest engineers (CEOs excepted) are not nearly as rich as the richest authors, the typical engineer is much better off than the typical author, because engineering is not nearly as winner-takes-all.

But the main topic for today is actually patent rents. These are a greatly underappreciated segment of our economy, and they grow more important all the time. A patent rent is more or less what it sounds like; it’s the extra money you get from owning a patent on something. You can get that money either by literally renting it—charging license fees for other companies to use it—or simply by being the only company who is allowed to manufacture something, letting you sell it at monopoly prices. It’s surprisingly difficult to assess the real value of patent rents—there’s a whole literature on different econometric methods of trying to tackle this—but one thing is clear: Some of the largest, wealthiest corporations in the world are built almost entirely upon patent rents. Drug companies, R&D companies, software companies—even many manufacturing companies like Boeing and GM obtain a substantial portion of their income from patents.

What is a patent? It’s a rule that says you “own” an idea, and anyone else who wants to use it has to pay you for the privilege. The very concept of owning an idea should trouble you—ideas aren’t limited in number, you can easily share them with others. But now think about the fact that most of these patents are owned by corporationsnot by inventors themselves—and you’ll realize that our system of property rights is built around the notion that an abstract entity can own an idea—that one idea can own another.

The rationale behind patents is that they are supposed to provide incentives for innovation—in exchange for investing the time and effort to invent something, you receive a certain amount of time where you get to monopolize that product so you can profit from it. But how long should we give you? And is this really the best way to incentivize innovation?

I contend it is not; when you look at the really important world-changing innovations, very few of them were done for patent rents, and virtually none of them were done by corporations. Jonas Salk was indignant at the suggestion he should patent the polio vaccine; it might have made him a billionaire, but only by letting thousands of children die. (To be fair, here’s a scholar arguing that he probably couldn’t have gotten the patent even if he wanted to—but going on to admit that even then the patent incentive had basically nothing to do with why penicillin and the polio vaccine were invented.)

Who landed on the moon? Hint: It wasn’t Microsoft. Who built the Hubble Space Telescope? Not Sony. The Internet that made Google and Facebook possible was originally invented by DARPA. Even when corporations seem to do useful innovation, it’s usually by profiting from the work of individuals: Edison’s corporation stole most of its good ideas from Nikola Tesla, and by the time the Wright Brothers founded a company their most important work was already done (though at least then you could argue that they did it in order to later become rich, which they ultimately did). Universities and nonprofits brought you the laser, light-emitting diodes, fiber optics, penicillin and the polio vaccine. Governments brought you liquid-fuel rockets, the Internet, GPS, and the microchip. Corporations brought you, uh… Viagra, the Snuggie, and Furbies. Indeed, even Google’s vaunted search algorithms were originally developed by the NSF. I can think of literally zero examples of a world-changing technology that was actually invented by a corporation in order to secure a patent. I’m hesitant to say that none exist, but clearly the vast majority of seminal inventions have been created by governments and universities.

This has always been true throughout history. Rome’s fire departments were notorious for shoddy service—and wholly privately-owned—but their great aqueducts that still stand today were built as government projects. When China invented paper, turned it into money, and defended it with the Great Wall, it was all done on government funding.

The whole idea that patents are necessary for innovation is simply a lie; and even the idea that patents lead to more innovation is quite hard to defend. Imagine if instead of letting Google and Facebook patent their technology all the money they receive in patent rents were instead turned into tax-funded research—frankly is there even any doubt that the results would be better for the future of humanity? Instead of better ad-targeting algorithms we could have had better cancer treatments, or better macroeconomic models, or better spacecraft engines.

When they feel their “intellectual property” (stop and think about that phrase for awhile, and it will begin to seem nonsensical) has been violated, corporations become indignant about “free-riding”; but who is really free-riding here? The people who copy music albums for free—because they cost nothing to copy, or the corporations who make hundreds of billions of dollars selling zero-marginal-cost products using government-invented technology over government-funded infrastructure? (Many of these companies also continue receive tens or hundreds of millions of dollars in subsidies every year.) In the immortal words of Barack Obama, “you didn’t build that!”

Strangely, most economists seem to be supportive of patents, despite the fact that their own neoclassical models point strongly in the opposite direction. There’s no logical connection between the fixed cost of inventing a technology and the monopoly rents that can be extracted from its patent. There is some connection—albeit a very weak one—between the benefits of the technology and its monopoly profits, since people are likely to be willing to pay more for more beneficial products. But most of the really great benefits are either in the form of public goods that are unenforceable even with patents (go ahead, try enforcing on that satellite telescope on everyone who benefits from its astronomical discoveries!) or else apply to people who are so needy they can’t possibly pay you (like anti-malaria drugs in Africa), so that willingness-to-pay link really doesn’t get you very far.

I guess a lot of neoclassical economists still seem to believe that willingness-to-pay is actually a good measure of utility, so maybe that’s what’s going on here; if it were, we could at least say that patents are a second-best solution to incentivizing the most important research.

But even then, why use second-best when you have best? Why not devote more of our society’s resources to governments and universities that have centuries of superior track record in innovation? When this is proposed the deadweight loss of taxation is always brought up, but somehow the deadweight loss of monopoly rents never seems to bother anyone. At least taxes can be designed to minimize deadweight loss—and democratic governments actually have incentives to do that; corporations have no interest whatsoever in minimizing the deadweight loss they create so long as their profit is maximized.

I’m not saying we shouldn’t have corporations at all—they are very good at one thing and one thing only, and that is manufacturing physical goods. Cars and computers should continue to be made by corporations—but their technologies are best invented by government. Will this dramatically reduce the profits of corporations? Of course—but I have difficulty seeing that as anything but a good thing.

Why am I talking so much about patents, when I said the topic was robots? Well, it’s typically because of the way these patents are assigned that robots taking people’s jobs becomes a bad thing. The patent is owned by the company, which is owned by the shareholders; so when the company makes more money by using robots instead of workers, the workers lose.

If when a robot takes your job, you simply received the income produced by the robot as capital income, you’d probably be better off—you get paid more and you also don’t have to work. (Of course, if you define yourself by your career or can’t stand the idea of getting “handouts”, you might still be unhappy losing your job even though you still get paid for it.)

There’s a subtler problem here though; robots could have a comparative advantage without having an absolute advantage—that is, they could produce less than the workers did before, but at a much lower cost. Where it cost $5 million in wages to produce $10 million in products, it might cost only $3 million in robot maintenance to produce $9 million in products. Hence you can’t just say that we should give the extra profits to the workers; in some cases those extra profits only exist because we are no longer paying the workers.

As a society, we still want those transactions to happen, because producing less at lower cost can still make our economy more efficient and more productive than it was before. Those displaced workers can—in theory at least—go on to other jobs where they are needed more.

The problem is that this often doesn’t happen, or it takes such a long time that workers suffer in the meantime. Hence the Luddites; they don’t want to be made obsolete even if it does ultimately make the economy more productive.

But this is where patents become important. The robots were probably invented at a university, but then a corporation took them and patented them, and is now selling them to other corporations at a monopoly price. The manufacturing company that buys the robots now has to spend more in order to use the robots, which drives their profits down unless they stop paying their workers.

If instead those robots were cheap because there were no patents and we were only paying for the manufacturing costs, the workers could be shareholders in the company and the increased efficiency would allow both the employers and the workers to make more money than before.

What if we don’t want to make the workers into shareholders who can keep their shares after they leave the company? There is a real downside here, which is that once you get your shares, why stay at the company? We call that a “golden parachute” when CEOs do it, which they do all the time; but most economists are in favor of stock-based compensation for CEOs, and once again I’m having trouble seeing why it’s okay when rich people do it but not when middle-class people do.

Another alternative would be my favorite policy, the basic income: If everyone knows they can depend on a basic income, losing your job to a robot isn’t such a terrible outcome. If the basic income is designed to grow with the economy, then the increased efficiency also raises everyone’s standard of living, as economic growth is supposed to do—instead of simply increasing the income of the top 0.01% and leaving everyone else where they were. (There is a good reason not to make the basic income track economic growth too closely, namely the business cycle; you don’t want the basic income payments to fall in a recession, because that would make the recession worse. Instead they should be smoothed out over multiple years or designed to follow a nominal GDP target, so that they continue to rise even in a recession.)

We could also combine this with expanded unemployment insurance (explain to me again why you can’t collect unemployment if you weren’t working full-time before being laid off, even if you wanted to be or you’re a full-time student?) and active labor market policies that help people re-train and find new and better jobs. These policies also help people who are displaced for reasons other than robots making their jobs obsolete—obviously there are all sorts of market conditions that can lead to people losing their jobs, and many of these we actually want to happen, because they involve reallocating the resources of our society to more efficient ends.

Why aren’t these sorts of policies on the table? I think it’s largely because we don’t think of it in terms of distributing goods—we think of it in terms of paying for labor. Since the worker is no longer laboring, why pay them?

This sounds reasonable at first, but consider this: Why give that money to the shareholder? What did they do to earn it? All they do is own a piece of the company. They may not have contributed to the goods at all. Honestly, on a pay-for-work basis, we should be paying the robot!

If it bothers you that the worker collects dividends even when he’s not working—why doesn’t it bother you that shareholders do exactly the same thing? By definition, a shareholder is paid according to what they own, not what they do. All this reform would do is make workers into owners.

If you justify the shareholder’s wealth by his past labor, again you can do exactly the same to justify worker shares. (And as I said above, if you’re worried about the moral hazard of workers collecting shares and leaving, you should worry just as much about golden parachutes.)

You can even justify a basic income this way: You paid taxes so that you could live in a society that would protect you from losing your livelihood—and if you’re just starting out, your parents paid those taxes and you will soon enough. Theoretically there could be “welfare queens” who live their whole lives on the basic income, but empirical data shows that very few people actually want to do this, and when given opportunities most people try to find work. Indeed, even those who don’t, rarely seem to be motivated by greed (even though, capitalists tell us, “greed is good”); instead they seem to be de-motivated by learned helplessness after trying and failing for so long. They don’t actually want to sit on the couch all day and collect welfare payments; they simply don’t see how they can compete in the modern economy well enough to actually make a living from work.

One thing is certain: We need to detach income from labor. As a society we need to get over the idea that a human being’s worth is decided by the amount of work they do for corporations. We need to get over the idea that our purpose in life is a job, a career, in which our lives are defined by the work we do that can be neatly monetized. (I admit, I suffer from the same cultural blindness at times, feeling like a failure because I can’t secure the high-paying and prestigious employment I want. I feel this clear sense that my society does not value me because I am not making money, and it damages my ability to value myself.)

As robots do more and more of our work, we will need to redefine the way we live by something else, like play, or creativity, or love, or compassion. We will need to learn to see ourselves as valuable even if nothing we do ever sells for a penny to anyone else.

A basic income can help us do that; it can redefine our sense of what it means to earn money. Instead of the default being that you receive nothing because you are worthless unless you work, the default is that you receive enough to live on because you are a human being of dignity and a citizen. This is already the experience of people who have substantial amounts of capital income; they can fall back on their dividends if they ever can’t or don’t want to find employment. A basic income would turn us all into capital owners, shareholders in the centuries of established capital that has been built by our forebears in the form of roads, schools, factories, research labs, cars, airplanes, satellites, and yes—robots.