We ignorant, incompetent gods

2023-05-212023-05-15 pnrj book reviews, cognitive science, core principles, ethics, futurism, inequality, tribal paradigm artificial intelligence, capitalism, compatibilism, determinism, ethics, free will, future, gods, history, humanism, hypocrisy, ideology, medieval, morality, racism, religion, science, sexism

May 21 JDN 2460086

A review of Homo Deus

The real problem of humanity is the following: We have Paleolithic emotions, medieval institutions and godlike technology.
E.O. Wilson

Homo Deus is a very good read—and despite its length, a quick one; as you can see, I read it cover to cover in a week. Yuval Noah Harari’s central point is surely correct: Our technology is reaching a threshold where it grants us unprecedented power and forces us to ask what it means to be human.

Biotechnology and artificial intelligence are now advancing so rapidly that advancements in other domains, such as aerospace and nuclear energy, seem positively mundane. Who cares about making flight or electricity a bit cleaner when we will soon have the power to modify ourselves or we’ll all be replaced by machines?

Indeed, we already have technology that would have seemed to ancient people like the powers of gods. We can fly; we can witness or even control events thousands of miles away; we can destroy mountains; we can wipeout entire armies in an instant; we can even travel into outer space.

Harari rightly warns us that our not-so-distant descendants are likely to have powers that we would see as godlike: Immortality, superior intelligence, self-modification, the power to create life.

And where it is scary to think about what they might do with that power if they think the way we do—as ignorant and foolish and tribal as we are—Harari points out that it is equally scary to think about what they might do if they don’t think the way we do—for then, how do they think? If their minds are genetically modified or even artificially created, who will they be? What values will they have, if not ours? Could they be better? What if they’re worse?

It is of course difficult to imagine values better than our own—if we thought those values were better, we’d presumably adopt them. But we should seriously consider the possibility, since presumably most of us believe that our values today are better than what most people’s values were 1000 years ago. If moral progress continues, does it not follow that people’s values will be better still 1000 years from now? Or at least that they could be?

I also think Harari overestimates just how difficult it is to anticipate the future. This may be a useful overcorrection; the world is positively infested with people making overprecise predictions about the future, often selling them for exorbitant fees (note that Harari was quite well-compensated for this book as well!). But our values are not so fundamentally alien from those of our forebears, and we have reason to suspect that our descendants’ values will be no more different from ours.

For instance, do you think that medieval people thought suffering and death were good? I assure you they did not. Nor did they believe that the supreme purpose in life is eating cheese. (They didn’t even believe the Earth was flat!) They did not have the concept of GDP, but they could surely appreciate the value of economic prosperity.

Indeed, our world today looks very much like a medieval peasant’s vision of paradise. Boundless food in endless variety. Near-perfect security against violence. Robust health, free from nearly all infectious disease. Freedom of movement. Representation in government! The land of milk and honey is here; there they are, milk and honey on the shelves at Walmart.

Of course, our paradise comes with caveats: Not least, we are by no means free of toil, but instead have invented whole new kinds of toil they could scarcely have imagined. If anything I would have to guess that coding a robot or recording a video lecture probably isn’t substantially more satisfying than harvesting wheat or smithing a sword; and reconciling receivables and formatting spreadsheets is surely less. Our tasks are physically much easier, but mentally much harder, and it’s not obvious which of those is preferable. And we are so very stressed! It’s honestly bizarre just how stressed we are, given the abudance in which we live; there is no reason for our lives to have stakes so high, and yet somehow they do. It is perhaps this stress and economic precarity that prevents us from feeling such joy as the medieval peasants would have imagined for us.

Of course, we don’t agree with our ancestors on everything. The medieval peasants were surely more religious, more ignorant, more misogynistic, more xenophobic, and more racist than we are. But projecting that trend forward mostly means less ignorance, less misogyny, less racism in the future; it means that future generations should see the world world catch up to what the best of us already believe and strive for—hardly something to fear. The values that I believe are surely not what we as a civilization act upon, and I sorely wish they were. Perhaps someday they will be.

I can even imagine something that I myself would recognize as better than me: Me, but less hypocritical. Strictly vegan rather than lacto-ovo-vegetarian, or at least more consistent about only buying free range organic animal products. More committed to ecological sustainability, more willing to sacrifice the conveniences of plastic and gasoline. Able to truly respect and appreciate all life, even humble insects. (Though perhaps still not mosquitoes; this is war. They kill more of us than any other animal, including us.) Not even casually or accidentally racist or sexist. More courageous, less burnt out and apathetic. I don’t always live up to my own ideals. Perhaps someday someone will.

Harari fears something much darker, that we will be forced to give up on humanist values and replace them with a new techno-religion he calls Dataism, in which the supreme value is efficient data processing. I see very little evidence of this. If it feels like data is worshipped these days, it is only because data is profitable. Amazon and Google constantly seek out ever richer datasets and ever faster processing because that is how they make money. The real subject of worship here is wealth, and that is nothing new. Maybe there are some die-hard techno-utopians out there who long for us all to join the unified oversoul of all optimized data processing, but I’ve never met one, and they are clearly not the majority. (Harari also uses the word ‘religion’ in an annoyingly overbroad sense; he refers to communism, liberalism, and fascism as ‘religions’. Ideologies, surely; but religions?)

Harari in fact seems to think that ideologies are strongly driven by economic structures, so maybe he would even agree that it’s about profit for now, but thinks it will become religion later. But I don’t really see history fitting this pattern all that well. If monotheism is directly tied to the formation of organized bureaucracy and national government, then how did Egypt and Rome last so long with polytheistic pantheons? If atheism is the natural outgrowth of industrialized capitalism, then why are Africa and South America taking so long to get the memo? I do think that economic circumstances can constrain culture and shift what sort of ideas become dominant, including religious ideas; but there clearly isn’t this one-to-one correspondence he imagines. Moreover, there was never Coalism or Oilism aside from the greedy acquisition of these commodities as part of a far more familiar ideology: capitalism.

He also claims that all of science is now, or is close to, following a united paradigm under which everything is a data processing algorithm, which suggests he has not met very many scientists. Our paradigms remain quite varied, thank you; and if they do all have certain features in common, it’s mainly things like rationality, naturalism and empiricism that are more or less inherent to science. It’s not even the case that all cognitive scientists believe in materialism (though it probably should be); there are still dualists out there.

Moreover, when it comes to values, most scientists believe in liberalism. This is especially true if we use Harari’s broad sense (on which mainline conservatives and libertarians are ‘liberal’ because they believe in liberty and human rights), but even in the narrow sense of center-left. We are by no means converging on a paradigm where human life has no value because it’s all just data processing; maybe some scientists believe that, but definitely not most of us. If scientists ran the world, I can’t promise everything would be better, but I can tell you that Bush and Trump would never have been elected and we’d have a much better climate policy in place by now.

I do share many of Harari’s fears of the rise of artificial intelligence. The world is clearly not ready for the massive economic disruption that AI is going to cause all too soon. We still define a person’s worth by their employment, and think of ourselves primarily as collection of skills; but AI is going to make many of those skills obsolete, and may make many of us unemployable. It would behoove us to think in advance about who we truly are and what we truly want before that day comes. I used to think that creative intellectual professions would be relatively secure; ChatGPT and Midjourney changed my mind. Even writers and artists may not be safe much longer.

Harari is so good at sympathetically explaining other views he takes it to a fault. At times it is actually difficult to know whether he himself believes something and wants you to, or if he is just steelmanning someone else’s worldview. There’s a whole section on ‘evolutionary humanism’ where he details a worldview that is at best Nietschean and at worst Nazi, but he makes it sound so seductive. I don’t think it’s what he believes, in part because he has similarly good things to say about liberalism and socialism—but it’s honestly hard to tell.

The weakest part of the book is when Harari talks about free will. Like most people, he just doesn’t get compatibilism. He spends a whole chapter talking about how science ‘proves we have no free will’, and it’s just the same old tired arguments hard determinists have always made.

He talks about how we can make choices based on our desires, but we can’t choose our desires; well of course we can’t! What would that even mean? If you could choose your desires, what would you choose them based on, if not your desires? Your desire-desires? Well, then, can you choose your desire-desires? What about your desire-desire-desires?

What even is this ultimate uncaused freedom that libertarian free will is supposed to consist in? No one seems capable of even defining it. (I’d say Kant got the closest: He defined it as the capacity to act based upon what ought rather than what is. But of course what we believe about ‘ought’ is fundamentally stored in our brains as a particular state, a way things are—so in the end, it’s an ‘is’ we act on after all.)

Maybe before you lament that something doesn’t exist, you should at least be able to describe that thing as a coherent concept? Woe is me, that 2 plus 2 is not equal to 5!

It is true that as our technology advances, manipulating other people’s desires will become more and more feasible. Harari overstates the case on so-called robo-rats; they aren’t really mind-controlled, it’s more like they are rewarded and punished. The rat chooses to go left because she knows you’ll make her feel good if she does; she’s still freely choosing to go left. (Dangling a carrot in front of a horse is fundamentally the same thing—and frankly, paying a wage isn’t all that different.) The day may yet come where stronger forms of control become feasible, and woe betide us when it does. Yet this is no threat to the concept of free will; we already knew that coercion was possible, and mind control is simply a more precise form of coercion.

Harari reports on a lot of interesting findings in neuroscience, which are important for people to know about, but they do not actually show that free will is an illusion. What they do show is that free will is thornier than most people imagine. Our desires are not fully unified; we are often ‘of two minds’ in a surprisingly literal sense. We are often tempted by things we know are wrong. We often aren’t sure what we really want. Every individual is in fact quite divisible; we literally contain multitudes.

We do need a richer account of moral responsibility that can deal with the fact that human beings often feel multiple conflicting desires simultaneously, and often experience events differently than we later go on to remember them. But at the end of the day, human consciousness is mostly unified, our choices are mostly rational, and our basic account of moral responsibility is mostly valid.

I think for now we should perhaps be less worried about what may come in the distant future, what sort of godlike powers our descendants may have—and more worried about what we are doing with the godlike powers we already have. We have the power to feed the world; why aren’t we? We have the power to save millions from disease; why don’t we? I don’t see many people blindly following this ‘Dataism’, but I do see an awful lot blinding following a 19th-century vision of capitalism.

And perhaps if we straighten ourselves out, the future will be in better hands.

What behavioral economics needs

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

Apr 16 JDN 2460049

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Implications of stochastic overload

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

Apr 2 JDN 2460037

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The role of innate activation in stochastic overload

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

Mar 26 JDN 2460030

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

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

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

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

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

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

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

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

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

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

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

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

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

In this range, the probability of avoiding overload is:

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

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

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

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

Multiplying these two together:

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

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

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

So let’s consider some other distributions.

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

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

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

Y = (1/λ+s) – (1/ λ + 1)e^(-λ(1-s))

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

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

λ = 0.5:

λ = 1:

λ = 2:

λ = 4:

λ = 8:

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

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

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

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

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

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

Y = I(1-s, α+1, β) + I(1-s, α, β)

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

The innate activation probability looks like this:

And the result comes out like this:

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

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

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

The stochastic overload model

2023-03-122023-03-07 pnrj cognitive science, mathematical models, personal, science and academia behavior, incentives, model, nervous system, neuroscience, neuroticism, stochastic overload, stress, Yerkes-Dodson

The stochastic overload model

Mar 12 JDN 2460016

The next few posts are going to be a bit different, a bit more advanced and technical than usual. This is because, for the first time in several months at least, I am actually working on what could be reasonably considered something like theoretical research.

I am writing it up in the form of blog posts, because actually writing a paper is still too stressful for me right now. This also forces me to articulate my ideas in a clearer and more readable way, rather than dive directly into a morass of equations. It also means that even if I do never actually get around to finishing a paper, the idea is out there, and maybe someone else could make use of it (and hopefully give me some of the credit).

I’ve written previously about the Yerkes-Dodson effect: On cognitively-demanding tasks, increased stress increases performance, but only to a point, after which it begins decreasing it again. The effect is well-documented, but the mechanism is poorly understood.

I am currently on the wrong side of the Yerkes-Dodson curve, which is why I’m too stressed to write this as a formal paper right now. But that also gave me some ideas about how it may work.

I have come up with a simple but powerful mathematical model that may provide a mechanism for the Yerkes-Dodson effect.

This model is clearly well within the realm of a behavioral economic model, but it is also closely tied to neuroscience and cognitive science.

I call it the stochastic overload model.

First, a metaphor: Consider an engine, which can run faster or slower. If you increase its RPMs, it will output more power, and provide more torque—but only up to a certain point. Eventually it hits a threshold where it will break down, or even break apart. In real engines, we often include safety systems that force the engine to shut down as it approaches such a threshold.

I believe that human brains function on a similar principle. Stress increases arousal, which activates a variety of processes via the sympathetic nervous system. This activation improves performance on both physical and cognitive tasks. But it has a downside; especially on cognitively demanding tasks which required sustained effort, I hypothesize that too much sympathetic activation can result in a kind of system overload, where your brain can no longer handle the stress and processes are forced to shut down.

This shutdown could be brief—a few seconds, or even a fraction of a second—or it could be prolonged—hours or days. That might depend on just how severe the stress is, or how much of your brain it requires, or how prolonged it is. For purposes of the model, this isn’t vital. It’s probably easiest to imagine it being a relatively brief, localized shutdown of a particular neural pathway. Then, your performance in a task is summed up over many such pathways over a longer period of time, and by the law of large numbers your overall performance is essentially the average performance of all your brain systems.

That’s the “overload” part of the model. Now for the “stochastic” part.

Let’s say that, in the absence of stress, your brain has a certain innate level of sympathetic activation, which varies over time in an essentially chaotic, unpredictable—stochastic—sort of way. It is never really completely deactivated, and may even have some chance of randomly overloading itself even without outside input. (Actually, a potential role in the model for the personality trait neuroticism is an innate tendency toward higher levels of sympathetic activation in the absence of outside stress.)

Let’s say that this innate activation is x, which follows some kind of known random distribution F(x).

For simplicity, let’s also say that added stress s adds linearly to your level of sympathetic activation, so your overall level of activation is x + s.

For simplicity, let’s say that activation ranges between 0 and 1, where 0 is no activation at all and 1 is the maximum possible activation and triggers overload.

I’m assuming that if a pathway shuts down from overload, it doesn’t contribute at all to performance on the task. (You can assume it’s only reduced performance, but this adds complexity without any qualitative change.)

Since sympathetic activation improves performance, but can result in overload, your overall expected performance in a given task can be computed as the product of two terms:

[expected value of x + s, provided overload does not occur] * [probability overload does not occur]

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

The first term can be thought of as the incentive effect: Higher stress promotes more activation and thus better performance.

The second term can be thought of as the overload effect: Higher stress also increases the risk that activation will exceed the threshold and force shutdown.

This equation actually turns out to have a remarkably elegant form as an integral (and here’s where I get especially technical and mathematical):

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

The integral subsumes both the incentive effect and the overload effect into one term; you can also think of the +s in the integrand as the incentive effect and the 1-s in the limit of integration as the overload effect.

For the uninitated, this is probably just Greek. So let me show you some pictures to help with your intuition. These are all freehand sketches, so let me apologize in advance for my limited drawing skills. Think of this as like Arthur Laffer’s famous cocktail napkin.

Suppose that, in the absence of outside stress, your innate activation follows a distribution like this (this could be a normal or logit PDF; as I’ll talk about next week, logit is far more tractable):

As I start adding stress, this shifts the distribution upward, toward increased activation:

Initially, this will improve average performance.

But at some point, increased stress actually becomes harmful, as it increases the probability of overload.

And eventually, the probability of overload becomes so high that performance becomes worse than it was with no stress at all:

The result is that overall performance, as a function of stress, looks like an inverted U-shaped curve—the Yerkes-Dodson curve:

The precise shape of this curve depends on the distribution that we use for the innate activation, which I will save for next week’s post.

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

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

Feb 26 JDN 2460002

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

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

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

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

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

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

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

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

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

Good enough is perfect. Perfect is bad.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Good enough is perfect; perfect is bad.

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

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

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

Good enough is perfect; perfect is bad.

The mythology mindset

2023-02-052023-01-29 pnrj book reviews, cognitive science, core principles, tribal paradigm belief, Daniel Dennett, mythology, psychology, reason, religion, Steven Pinker

Feb 5 JDN 2459981

I recently finished reading Steven Pinker’s latest book Rationality. It’s refreshing, well-written, enjoyable, and basically correct with some small but notable errors that seem sloppy—but then you could have guessed all that from the fact that it was written by Steven Pinker.

What really makes the book interesting is an insight Pinker presents near the end, regarding the difference between the “reality mindset” and the “mythology mindset”.

It’s a pretty simple notion, but a surprisingly powerful one.

In the reality mindset, a belief is a model of how the world actually functions. It must be linked to the available evidence and integrated into a coherent framework of other beliefs. You can logically infer from how some parts work to how other parts must work. You can predict the outcomes of various actions. You live your daily life in the reality mindset; you couldn’t function otherwise.

In the mythology mindset, a belief is a narrative that fulfills some moral, emotional, or social function. It’s almost certainly untrue or even incoherent, but that doesn’t matter. The important thing is that it sends the right messages. It has the right moral overtones. It shows you’re a member of the right tribe.

The idea is similar to Dennett’s “belief in belief”, which I’ve written about before; but I think this characterization may actually be a better one, not least because people would be more willing to use it as a self-description. If you tell someone “You don’t really believe in God, you believe in believing in God”, they will object vociferously (which is, admittedly, what the theory would predict). But if you tell them, “Your belief in God is a form of the mythology mindset”, I think they are at least less likely to immediately reject your claim out of hand. “You believe in God a different way than you believe in cyanide” isn’t as obviously threatening to their identity.

A similar notion came up in a Psychology of Religion course I took, in which the professor discussed “anomalous beliefs” linked to various world religions. He picked on a bunch of obscure religions, often held by various small tribes. He asked for more examples from the class. Knowing he was nominally Catholic and not wanting to let mainstream religion off the hook, I presented my example: “This bread and wine are the body and blood of Christ.” To his credit, he immediately acknowledged it as a very good example.

It’s also not quite the same thing as saying that religion is a “metaphor”; that’s not a good answer for a lot of reasons, but perhaps chief among them is that people don’t say they believe metaphors. If I say something metaphorical and then you ask me, “Hang on; is that really true?” I will immediately acknowledge that it is not, in fact, literally true. Love is a rose with all its sweetness and all its thorns—but no, love isn’t really a rose. And when it comes to religious belief, saying that you think it’s a metaphor is basically a roundabout way of saying you’re an atheist.

From all these different directions, we seem to be converging on a single deeper insight: when people say they believe something, quite often, they clearly mean something very different by “believe” than what I would ordinarily mean.

I’m tempted even to say that they don’t really believe it—but in common usage, the word “belief” is used at least as often to refer to the mythology mindset as the reality mindset. (In fact, it sounds less weird to say “I believe in transsubstantiation” than to say “I believe in gravity”.) So if they don’t really believe it, then they at least mythologically believe it.

Both mindsets seem to come very naturally to human beings, in particular contexts. And not just modern people, either. Humans have always been like this.

Ask that psychology professor about Jesus, and he’ll tell you a tall tale of life, death, and resurrection by a demigod. But ask him about the Stroop effect, and he’ll provide a detailed explanation of rigorous experimental protocol. He believes something about God; but he knows something about psychology.

Ask a hunter-gatherer how the world began, and he’ll surely spin you a similarly tall tale about some combination of gods and spirits and whatever else, and it will all be peculiarly particular to his own tribe and no other. But ask him how to gut a fish, and he’ll explain every detail with meticulous accuracy, with almost the same rigor as that scientific experiment. He believes something about the sky-god; but he knows something about fish.

To be a rationalist, then, is to aspire to live your whole life in the reality mindset. To seek to know rather than believe.

This isn’t about certainty. A rationalist can be uncertain about many things—in fact, it’s rationalists of all people who are most willing to admit and quantify their uncertainty.

This is about whether you allow your beliefs to float free as bare, almost meaningless assertions that you profess to show you are a member of the tribe, or you make them pay rent, directly linked to other beliefs and your own experience.

As long as I can remember, I have always aspired to do this. But not everyone does. In fact, I dare say most people don’t. And that raises a very important question: Should they? Is it better to live the rationalist way?

I believe that it is. I suppose I would, temperamentally. But say what you will about the Enlightenment and the scientific revolution, they have clearly revolutionized human civilization and made life much better today than it was for most of human existence. We are peaceful, safe, and well-fed in a way that our not-so-distant ancestors could only dream of, and it’s largely thanks to systems built under the principles of reason and rationality—that is, the reality mindset.

We would never have industrialized agriculture if we still thought in terms of plant spirits and sky gods. We would never have invented vaccines and antibiotics if we still believed disease was caused by curses and witchcraft. We would never have built power grids and the Internet if we still saw energy as a mysterious force permeating the world and not as a measurable, manipulable quantity.

This doesn’t mean that ancient people who saw the world in a mythological way were stupid. In fact, it doesn’t even mean that people today who still think this way are stupid. This is not about some innate, immutable mental capacity. It’s about a technology—or perhaps the technology, the meta-technology that makes all other technology possible. It’s about learning to think the same way about the mysterious and the familiar, using the same kind of reasoning about energy and death and sunlight as we already did about rocks and trees and fish. When encountering something new and mysterious, someone in the mythology mindset quickly concocts a fanciful tale about magical beings that inevitably serves to reinforce their existing beliefs and attitudes, without the slightest shred of evidence for any of it. In their place, someone in the reality mindset looks closer and tries to figure it out.

Still, this gives me some compassion for people with weird, crazy ideas. I can better make sense of how someone living in the modern world could believe that the Earth is 6,000 years old or that the world is ruled by lizard-people. Because they probably don’t really believe it, they just mythologically believe it—and they don’t understand the difference.

Good enough is perfect, perfect is bad

2023-01-082023-01-03 pnrj book reviews, cognitive science, core principles, critique of neoclassical economics, personal ADHD, Bayesian, bounded rationality, DSGE, dynamic stochastic general equilibrium, frequentist, imperfection, Lagrangian, optimization, perfection, rationality

Jan 8 JDN 2459953

Not too long ago, I read the book How to Keep House While Drowning by KC Davis, which I highly recommend. It offers a great deal of useful and practical advice, especially for someone neurodivergent and depressed living through an interminable pandemic (which I am, but honestly, odds are, you may be too). And to say it is a quick and easy read is actually an unfair understatement; it is explicitly designed to be readable in short bursts by people with ADHD, and it has a level of accessibility that most other books don’t even aspire to and I honestly hadn’t realized was possible. (The extreme contrast between this and academic papers is particularly apparent to me.)

One piece of advice that really stuck with me was this: Good enough is perfect.

At first, it sounded like nonsense; no, perfect is perfect, good enough is just good enough. But in fact there is a deep sense in which it is absolutely true.

Indeed, let me make it a bit stronger: Good enough is perfect; perfect is bad.

I doubt Davis thought of it in these terms, but this is a concise, elegant statement of the principles of bounded rationality. Sometimes it can be optimal not to optimize.

Suppose that you are trying to optimize something, but you have limited computational resources in which to do so. This is actually not a lot for you to suppose—it’s literally true of basically everyone basically every moment of every day.

But let’s make it a bit more concrete, and say that you need to find the solution to the following math problem: “What is the product of 2419 times 1137?” (Pretend you don’t have a calculator, as it would trivialize the exercise. I thought about using a problem you couldn’t do with a standard calculator, but I realized that would also make it much weirder and more obscure for my readers.)

Now, suppose that there are some quick, simple ways to get reasonably close to the correct answer, and some slow, difficult ways to actually get the answer precisely.

In this particular problem, the former is to approximate: What’s 2500 times 1000? 2,500,000. So it’s probably about 2,500,000.

Or we could approximate a bit more closely: Say 2400 times 1100, that’s about 100 times 100 times 24 times 11, which is 2 times 12 times 11 (times 10,000), which is 2 times (110 plus 22), which is 2 times 132 (times 10,000), which is 2,640,000.

Or, we could actually go through all the steps to do the full multiplication (remember I’m assuming you have no calculator), multiply, carry the 1s, add all four sums, re-check everything and probably fix it because you messed up somewhere; and then eventually you will get: 2,750,403.

So, our really fast method was only off by about 10%. Our moderately-fast method was only off by 4%. And both of them were a lot faster than getting the exact answer by hand.

Which of these methods you’d actually want to use depends on the context and the tools at hand. If you had a calculator, sure, get the exact answer. Even if you didn’t, but you were balancing the budget for a corporation, I’m pretty sure they’d care about that extra $110,403. (Then again, they might not care about the $403 or at least the $3.) But just as an intellectual exercise, you really didn’t need to do anything; the optimal choice may have been to take my word for it. Or, if you were at all curious, you might be better off choosing the quick approximation rather than the precise answer. Since nothing of any real significance hinged on getting that answer, it may be simply a waste of your time to bother finding it.

This is of course a contrived example. But it’s not so far from many choices we make in real life.

Yes, if you are making a big choice—which job to take, what city to move to, whether to get married, which car or house to buy—you should get a precise answer. In fact, I make spreadsheets with formal utility calculations whenever I make a big choice, and I haven’t regretted it yet. (Did I really make a spreadsheet for getting married? You’re damn right I did; there were a lot of big financial decisions to make there—taxes, insurance, the wedding itself! I didn’t decide whom to marry that way, of course; but we always had the option of staying unmarried.)

But most of the choices we make from day to day are small choices: What should I have for lunch today? Should I vacuum the carpet now? What time should I go to bed? In the aggregate they may all add up to important things—but each one of them really won’t matter that much. If you were to construct a formal model to optimize your decision of everything to do each day, you’d spend your whole day doing nothing but constructing formal models. Perfect is bad.

In fact, even for big decisions, you can’t really get a perfect answer. There are just too many unknowns. Sometimes you can spend more effort gathering additional information—but that’s costly too, and sometimes the information you would most want simply isn’t available. (You can look up the weather in a city, visit it, ask people about it—but you can’t really know what it’s like to live there until you do.) Even those spreadsheet models I use to make big decisions contain error bars and robustness checks, and if, even after investing a lot of effort trying to get precise results, I still find two or more choices just can’t be clearly distinguished to within a good margin of error, I go with my gut. And that seems to have been the best choice for me to make. Good enough is perfect.

I think that being gifted as a child trained me to be dangerously perfectionist as an adult. (Many of you may find this familiar.) When it came to solving math problems, or answering quizzes, perfection really was an attainable goal a lot of the time.

As I got older and progressed further in my education, maybe getting every answer right was no longer feasible; but I still could get the best possible grade, and did, in most of my undergraduate classes and all of my graduate classes. To be clear, I’m not trying to brag here; if anything, I’m a little embarrassed. What it mainly shows is that I had learned the wrong priorities. In fact, one of the main reasons why I didn’t get a 4.0 average in undergrad is that I spent a lot more time back then writing novels and nonfiction books, which to this day I still consider my most important accomplishments and grieve that I’ve not (yet?) been able to get them commercially published. I did my best work when I wasn’t trying to be perfect. Good enough is perfect; perfect is bad.

Now here I am on the other side of the academic system, trying to carve out a career, and suddenly, there is no perfection. When my exam is being graded by someone else, there is a way to get the most points. When I’m the one grading the exams, there is no “correct answer” anymore. There is no one scoring me to see if I did the grading the “right way”—and so, no way to be sure I did it right.

Actually, here at Edinburgh, there are other instructors who moderate grades and often require me to revise them, which feels a bit like “getting it wrong”; but it’s really more like we had different ideas of what the grade curve should look like (not to mention US versus UK grading norms). There is no longer an objectively correct answer the way there is for, say, the derivative of x^3, the capital of France, or the definition of comparative advantage. (Or, one question I got wrong on an undergrad exam because I had zoned out of that lecture to write a book on my laptop: Whether cocaine is a dopamine reuptake inhibitor. It is. And the fact that I still remember that because I got it wrong over a decade ago tells you a lot about me.)

And then when it comes to research, it’s even worse: What even constitutes “good” research, let alone “perfect” research? What would be most scientifically rigorous isn’t what journals would be most likely to publish—and without much bigger grants, I can afford neither. I find myself longing for the research paper that will be so spectacular that top journals have to publish it, removing all risk of rejection and failure—in other words, perfect.

Yet such a paper plainly does not exist. Even if I were to do something that would win me a Nobel or a Fields Medal (this is, shall we say, unlikely), it probably wouldn’t be recognized as such immediately—a typical Nobel isn’t awarded until 20 or 30 years after the work that spawned it, and while Fields Medals are faster, they’re by no means instant or guaranteed. In fact, a lot of ground-breaking, paradigm-shifting research was originally relegated to minor journals because the top journals considered it too radical to publish.

Or I could try to do something trendy—feed into DSGE or GTFO—and try to get published that way. But I know my heart wouldn’t be in it, and so I’d be miserable the whole time. In fact, because it is neither my passion nor my expertise, I probably wouldn’t even do as good a job as someone who really buys into the core assumptions. I already have trouble speaking frequentist sometimes: Are we allowed to say “almost significant” for p = 0.06? Maximizing the likelihood is still kosher, right? Just so long as I don’t impose a prior? But speaking DSGE fluently and sincerely? I’d have an easier time speaking in Latin.

What I know—on some level at least—I ought to be doing is finding the research that I think is most worthwhile, given the resources I have available, and then getting it published wherever I can. Or, in fact, I should probably constrain a little by what I know about journals: I should do the most worthwhile research that is feasible for me and has a serious chance of getting published in a peer-reviewed journal. It’s sad that those two things aren’t the same, but they clearly aren’t. This constraint binds, and its Lagrange multiplier is measured in humanity’s future.

But one thing is very clear: By trying to find the perfect paper, I have floundered and, for the last year and a half, not written any papers at all. The right choice would surely have been to write something.

Because good enough is perfect, and perfect is bad.

What is it with EA and AI?

2023-01-012023-01-03 pnrj cognitive science, computer science, ethics, futurism, inequality, public policy, science and academia AI safety, artificial intelligence, effective altruism, Eliezer Yudkowsky, Facebook, Google, logic, Pascal's Wager, philosophy, poverty, research

Jan 1 JDN 2459946

Surprisingly, most Effective Altruism (EA) leaders don’t seem to think that poverty alleviation should be our top priority. Most of them seem especially concerned about long-term existential risk, such as artificial intelligence (AI) safety and biosecurity. I’m not going to say that these things aren’t important—they certainly are important—but here are a few reasons I’m skeptical that they are really the most important the way that so many EA leaders seem to think.

1. We don’t actually know how to make much progress at them, and there’s only so much we can learn by investing heavily in basic research on them. Whereas, with poverty, the easy, obvious answer turns out empirically to be extremely effective: Give them money.

2. While it’s easy to multiply out huge numbers of potential future people in your calculations of existential risk (and this is precisely what people do when arguing that AI safety should be a top priority), this clearly isn’t actually a good way to make real-world decisions. We simply don’t know enough about the distant future of humanity to be able to make any kind of good judgments about what will or won’t increase their odds of survival. You’re basically just making up numbers. You’re taking tiny probabilities of things you know nothing about and multiplying them by ludicrously huge payoffs; it’s basically the secular rationalist equivalent of Pascal’s Wager.

2. AI and biosecurity are high-tech, futuristic topics, which seem targeted to appeal to the sensibilities of a movement that is still very dominated by intelligent, nerdy, mildly autistic, rich young White men. (Note that I say this as someone who very much fits this stereotype. I’m queer, not extremely rich and not entirely White, but otherwise, yes.) Somehow I suspect that if we asked a lot of poor Black women how important it is to slightly improve our understanding of AI versus giving money to feed children in Africa, we might get a different answer.

3. Poverty eradication is often characterized as a “short term” project, contrasted with AI safety as a “long term” project. This is (ironically) very short-sighted. Eradication of poverty isn’t just about feeding children today. It’s about making a world where those children grow up to be leaders and entrepreneurs and researchers themselves. The positive externalities of economic development are staggering. It is really not much of an exaggeration to say that fascism is a consequence of poverty and unemployment.

4. Currently the main thing that most Effective Altruism organizations say they need most is “talent”; how many millions of person-hours of talent are we leaving on the table by letting children starve or die of malaria?

5. Above all, existential risk can’t really be what’s motivating people here. The obvious solutions to AI safety and biosecurity are not being pursued, because they don’t fit with the vision that intelligent, nerdy, young White men have of how things should be. Namely: Ban them. If you truly believe that the most important thing to do right now is reduce the existential risk of AI and biotechnology, you should support a worldwide ban on research in artificial intelligence and biotechnology. You should want people to take all necessary action to attack and destroy institutions—especially for-profit corporations—that engage in this kind of research, because you believe that they are threatening to destroy the entire world and this is the most important thing, more important than saving people from starvation and disease. I think this is really the knock-down argument; when people say they think that AI safety is the most important thing but they don’t want Google and Facebook to be immediately shut down, they are either confused or lying. Honestly I think maybe Google and Facebook should be immediately shut down for AI safety reasons (as well as privacy and antitrust reasons!), and I don’t think AI safety is yet the most important thing.

Why aren’t people doing that? Because they aren’t actually trying to reduce existential risk. They just think AI and biotechnology are really interesting, fascinating topics and they want to do research on them. And I agree with that, actually—but then they need stop telling people that they’re fighting to save the world, because they obviously aren’t. If the danger were anything like what they say it is, we should be halting all research on these topics immediately, except perhaps for a very select few people who are entrusted with keeping these forbidden secrets and trying to find ways to protect us from them. This may sound radical and extreme, but it is not unprecedented: This is how we handle nuclear weapons, which are universally recognized as a global existential risk. If AI is really as dangerous as nukes, we should be regulating it like nukes. I think that in principle it could be that dangerous, and may be that dangerous someday—but it isn’t yet. And if we don’t want it to get that dangerous, we don’t need more AI researchers, we need more regulations that stop people from doing harmful AI research! If you are doing AI research and it isn’t directly involved specifically in AI safety, you aren’t saving the world—you’re one of the people dragging us closer to the cliff! Anything that could make AI smarter but doesn’t also make it safer is dangerous. And this is clearly true of the vast majority of AI research, and frankly to me seems to also be true of the vast majority of research at AI safety institutes like the Machine Intelligence Research Institute.

Seriously, look through MIRI’s research agenda: It’s mostly incredibly abstract and seems completely beside the point when it comes to preventing AI from taking control of weapons or governments. It’s all about formalizing Bayesian induction. Thanks to you, Skynet can have a formally computable approximation to logical induction! Truly we are saved. Only two of their papers, on “Corrigibility” and “AI Ethics”, actually struck me as at all relevant to making AI safer. The rest is largely abstract mathematics that is almost literally navel-gazing—it’s all about self-reference. Eliezer Yudkowsky finds self-reference fascinating and has somehow convinced an entire community that it’s the most important thing in the world. (I actually find some of it fascinating too, especially the paper on “Functional Decision Theory”, which I think gets at some deep insights into things like why we have emotions. But I don’t see how it’s going to save the world from AI.)

Don’t get me wrong: AI also has enormous potential benefits, and this is a reason we may not want to ban it. But if you really believe that there is a 10% chance that AI will wipe out humanity by 2100, then get out your pitchforks and your EMP generators, because it’s time for the Butlerian Jihad. A 10% chance of destroying all humanity is an utterly unacceptable risk for any conceivable benefit. Better that we consign ourselves to living as we did in the Neolithic than risk something like that. (And a globally-enforced ban on AI isn’t even that; it’s more like “We must live as we did in the 1950s.” How would we survive!?) If you don’t want AI banned, maybe ask yourself whether you really believe the risk is that high—or are human brains just really bad at dealing with small probabilities?

I think what’s really happening here is that we have a bunch of guys (and yes, the EA and especially AI EA-AI community is overwhelmingly male) who are really good at math and want to save the world, and have thus convinced themselves that being really good at math is how you save the world. But it isn’t. The world is much messier than that. In fact, there may not be much that most of us can do to contribute to saving the world; our best options may in fact be to donate money, vote well, and advocate for good causes.

Let me speak Bayesian for a moment: The prior probability that you—yes, you, out of all the billions of people in the world—are uniquely positioned to save it by being so smart is extremely small. It’s far more likely that the world will be saved—or doomed—by people who have power. If you are not the head of state of a large country or the CEO of a major multinational corporation, I’m sorry; you probably just aren’t in a position to save the world from AI.

But you can give some money to GiveWell, so maybe do that instead?

In defense of civility

2022-12-192022-12-12 pnrj cognitive science, core principles, ethics, public policy, tribal paradigm ACAB, argument, be gay do crime, civility, Daniel Dennett, defund the police, LGBT, New Atheism, police reform, reason, reasonable, tone policing, violence

Dec 18 JDN 2459932

Civility is in short supply these days. Perhaps it has always been in short supply; certainly much of the nostalgia for past halcyon days of civility is ill-founded. Wikipedia has an entire article on hundreds of recorded incidents of violence in legislative assemblies, in dozens of countries, dating all the way from to the Roman Senate in 44 BC to Bosnia in 2019. But the Internet seems to bring about its own special kind of incivility, one which exposes nearly everyone to some of the worst vitriol the entire world has to offer. I think it’s worth talking about why this is bad, and perhaps what we might do about it.

For some, the benefits of civility seem so self-evident that they don’t even bear mentioning. For others, the idea of defending civility may come across as tone-deaf or even offensive. I would like to speak to both of those camps today: If you think the benefits of civility are obvious, I assure you, they aren’t to everyone. And if you think that civility is just a tool of the oppressive status quo, I hope I can make you think again.

A lot of the argument against civility seems to be founded in the notion that these issues are important, lives are at stake, and so we shouldn’t waste time and effort being careful how we speak to each other. How dare you concern yourself with the formalities of argumentation when people are dying?

But this is totally wrongheaded. It is precisely because these issues are important that civility is vital. It is precisely because lives are at stake that we must make the right decisions. And shouting and name-calling (let alone actual fistfights or drawn daggers—which have happened!) are not conducive to good decision-making.

If you shout someone down when choosing what restaurant to have dinner at, you have been very rude and people may end up unhappy with their dining experience—but very little of real value has been lost. But if you shout someone down when making national legislation, you may cause the wrong policy to be enacted, and this could lead to the suffering or death of thousands of people.

Think about how court proceedings work. Why are they so rigid and formal, with rules upon rules upon rules? Because the alternative was capricious violence. In the absence of the formal structure of a court system, so-called ‘justice’ was handed out arbitrarily, by whoever was in power, or by mobs of vigilantes. All those seemingly-overcomplicated rules were made in order to resolve various conflicts of interest and hopefully lead toward more fair, consistent results in the justice system. (And don’t get me wrong; they still could stand to be greatly improved!)

Legislatures have complex rules of civility for the same reason: Because the outcome is so important, we need to make sure that the decision process is as reliable as possible. And as flawed as existing legislatures still are, and as silly as it may seem to insist upon addressing ‘the Honorable Representative from the Great State of Vermont’, it’s clearly a better system than simply letting them duke it out with their fists.

A related argument I would like to address is that of ‘tone policing‘. If someone objects, not to the content of what you are saying, but to the tone in which you have delivered it, are they arguing in bad faith?

Well, possibly. Certainly, arguments about tone can be used that way. In particular I remember that this was basically the only coherent objection anyone could come up with against the New Atheism movement: “Well, sure, obviously, God isn’t real and religion is ridiculous; but why do you have to be so mean about it!?”

But it’s also quite possible for tone to be itself a problem. If your tone is overly aggressive and you don’t give people a chance to even seriously consider your ideas before you accuse them of being immoral for not agreeing with you—which happens all the time—then your tone really is the problem.

So, how can we tell which is which? I think a good way to reply to what you think might be bad-faith tone policing is this: “What sort of tone do you think would be better?”

I think there are basically three possible responses:

1. They can’t offer one, because there is actually no tone in which they would accept the substance of your argument. In that case, the tone policing really is in bad faith; they don’t want you to be nicer, they want you to shut up. This was clearly the case for New Atheism: As Daniel Dennett aptly remarked, “There’s simply no polite way to tell someone they have dedicated their lives to an illusion.” But sometimes, such things need to be said all the same.

2. They offer an alternative argument you could make, but it isn’t actually expressing your core message. Either they have misunderstood your core message, or they actually disagree with the substance of your argument and should be addressing it on those terms.

3. They offer an alternative way of expressing your core message in a milder, friendlier tone. This means that they are arguing in good faith and actually trying to help you be more persuasive!

I don’t know how common each of these three possibilities is; it could well be that the first one is the most frequent occurrence. That doesn’t change the fact that I have definitely been at the other end of the third one, where I absolutely agree with your core message and want your activism to succeed, but I can see that you’re acting like a jerk and nobody will want to listen to you.

Here, let me give some examples of the type of argument I’m talking about:

1. “Defund the police”: This slogan polls really badly. Probably because most people have genuine concerns about crime and want the police to protect them. Also, as more and more social services (like for mental health and homelessness) get co-opted into policing, this slogan makes it sound like you’re just going to abandon those people. But do we need serious, radical police reform? Absolutely. So how about “Reform the police”, “Put police money back into the community”, or even “Replace the police”?

2. “All Cops Are Bastards”: Speaking of police reform, did I mention we need it? A lot of it? Okay. Now, let me ask you: All cops? Every single one of them? There is not a single one out of the literally millions of police officers on this planet who is a good person? Not one who is fighting to take down police corruption from within? Not a single individual who is trying to fix the system while preserving public safety? Now, clearly, it’s worth pointing out, some cops are bastards—but hey, that even makes a better acronym: SCAB. In fact, it really is largely a few bad apples—the key point here is that you need to finish the aphorism: “A few bad apples spoil the whole barrel.” The number of police who are brutal and corrupt is relatively small, but as long as the other police continue to protect them, the system will be broken. Either you get those bad apples out pronto, or your whole barrel is bad. But demonizing the very people who are in the best position to implement those reforms—good police officers—is not helping.

3. “Be gay, do crime”: I know it’s tongue-in-cheek and ironic. I get that. It’s still a really dumb message. I am absolutely on board with LGBT rights. Even aside from being queer myself, I probably have more queer and trans friends than straight friends at this point. But why in the world would you want to associate us with petty crime? Why are you lumping us in with people who harm others at best out of desperation and at worst out of sheer greed? Even if you are literally an anarchist—which I absolutely am not—you’re really not selling anarchism well if the vision you present of it is a world of unfettered crime! There are dozens of better pro-LGBT slogans out there; pick one. Frankly even “do gay, be crime” is better, because it’s more clearly ironic. (Also, you can take it to mean something like this: Don’t just be gay, do gay—live your fullest gay life. And if you can be crime, that means that the system is fundamentally unjust: You can be criminalized just for who you are. And this is precisely what life is like for millions of LGBT people on this planet.)

A lot of people seem to think that if you aren’t immediately convinced by the most vitriolic, aggressive form of an argument, then you were never going to be convinced anyway and we should just write you off as a potential ally. This isn’t just obviously false; it’s incredibly dangerous.

The whole point of activism is that not everyone already agrees with you. You are trying to change minds. If it were really true that all reasonable, ethical people already agreed with your view, you wouldn’t need to be an activist. The whole point of making political arguments is that people can be reasonable and ethical and still be mistaken about things, and when we work hard to persuade them, we can eventually win them over. In fact, on some things we’ve actually done spectacularly well.

And what about the people who aren’t reasonable and ethical? They surely exist. But fortunately, they aren’t the majority. They don’t rule the whole world. If they did, we’d basically be screwed: If violence is really the only solution, then it’s basically a coin flip whether things get better or worse over time. But in fact, unreasonable people are outnumbered by reasonable people. Most of the things that are wrong with the world are mistakes, errors that can be fixed—not conflicts between irreconcilable factions. Our goal should be to fix those mistakes wherever we can, and that means being patient, compassionate educators—not angry, argumentative bullies.

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In defense of civility