Month: August 2025

Grief, a rationalist perspective

PNRJ core principles, ethics, personal , , , , , , , , , , , , ,

Aug 31 JDN 2460919

This post goes live on the 8th anniversary of my father’s death. Thus it seems an appropriate time to write about grief—indeed, it’s somewhat difficult for me to think about much else.

Far too often, the only perspectives on grief we hear are religious ones. Often, these take the form of consolation: “He’s in a better place now.” “You’ll see him again someday.”

Rationalism doesn’t offer such consolations. Technically one can be an atheist and still believe in an afterlife; but rationalism is stronger than mere atheism. It requires that we believe in scientific facts, and the permanent end of consciousness at death is a scientific fact. We know from direct experiments and observations in neuroscience that a destroyed brain cannot think, feel, see, hear, or remember—when your brain shuts down, whatever you are now will be gone.

It is the Basic Fact of Cognitive Science: There is no soul but the brain.

Moreover, I think, deep down, we all know that death is the end. Even religious people grieve. Their words may say that their loved one is in a better place, but their tears tell a different story.

Maybe it’s an evolutionary instinct, programmed deep into our minds like an ancestral memory, a voice that screams in our minds, insistent on being heard:

“Death is bad!”

If there is one crucial instinct a lifeform needs in order to survive, surely it is something like that one: The preference for life over death. In order to live in a hostile world, you have to want to live.

There are some people who don’t want to live, people who become suicidal. Sometimes even the person we are grieving was someone who chose to take their own life. Generally this is because they believe that their life from then on would be defined only by suffering. Usually, I would say they are wrong about that; but in some cases, maybe they are right, and choosing death is rational. Most of the time, life is worth living, even when we can’t see that.

But aside from such extreme circumstances, most of us feel most of the time that death is one of the worst things that could happen to us or our loved ones. And it makes sense that we feel that way. It is right to feel that way. It is rational to feel that way.

This is why grief hurts so much.

This is why you are not okay.

If the afterlife were real—or even plausible—then grief would not hurt so much. A loved one dying would be like a loved one traveling away to somewhere nice; bittersweet perhaps, maybe even sad—but not devastating the way that grief is. You don’t hold a funeral for someone who just booked a one-way trip to Hawaii, even if you know they aren’t ever coming back.

Religion tries to be consoling, but it typically fails. Because that voice in our heads is still there, repeating endlessly: “Death is bad!” “Death is bad!” “Death is bad!”

But what if religion does give people some comfort in such a difficult time? What if supposing something as nonsensical as Heaven numbs the pain for a little while?

In my view, you’d be better off using drugs. Drugs have side effects and can be addictive, but at least they don’t require you to fundamentally abandon your ontology. Mainstream religion isn’t simply false; it’s absurd. It’s one of the falsest things anyone has ever believed about anything. It’s obviously false. It’s ridiculous. It has never deserved any of the respect and reverence it so often receives.

And in a great many cases, religion is evil. Religion teaches people to be obedient to authoritarians, and to oppress those who are different. Some of the greatest atrocities in history were committed in the name of religion, and some of the worst oppression going on today is done in the name of religion.

Rationalists should give religion no quarter. It is better for someone to find solace in alcohol or cannabis than for them to find solace in religion.

And maybe, in the end, it’s better if they don’t find solace at all.

Grief is good. Grief is healthy. Grief is what we should feel when something as terrible as death happens. That voice screaming “Death is bad!” is right, and we should listen to it.

No, what we need is to not be paralyzed by grief, destroyed by grief. We need to withstand our grief, get through it. We must learn to be strong enough to bear what seems unbearable, not console ourselves with lies.

If you are a responsible adult, then when something terrible happens to you, you don’t pretend it isn’t real. You don’t conjure up a fantasy world in which everything is fine. You face your terrors. You learn to survive them. You make yourself strong enough to carry on. The death of a loved one is a terrible thing; you shouldn’t pretend otherwise. But it doesn’t have to destroy you. You can grow, and heal, and move on.

Moreover, grief has a noble purpose. From our grief we must find motivation to challenge death, to fight death wherever we find it. Those we have already lost are gone; it’s too late for them. But it’s not too late for the rest of us. We can keep fighting.

And through economic development and medical science, we do keep fighting.

In fact, little by little, we are winning the war on death.

Death has already lost its hold upon our children. For most of human history, nearly a third of children died before the age of 5. Now less than 1% do, in rich countries, and even in the poorest countries, it’s typically under 10%. With a little more development—development that is already happening in many places—we can soon bring everyone in the world to the high standard of the First World. We have basically won the war on infant and child mortality.

And death is losing its hold on the rest of us, too. Life expectancy at adulthood is also increasing, and more and more people are living into their nineties and even their hundreds.

It’s true, there still aren’t many people living to be 120 (and some researchers believe it will be a long time before this changes). But living to be 85 instead of 65 is already an extra 20 years of life—and these can be happy, healthy years too, not years of pain and suffering. They say that 60 is the new 50; physiologically, we are so much healthier than our ancestors that it’s as if we were ten years younger.

My sincere hope is that our grief for those we have lost and fear of losing those we still have will drive us forward to even greater progress in combating death. I believe that one day we will finally be able to slow, halt, perhaps even reverse aging itself, rendering us effectively immortal.

Religion promises us immortality, but it isn’t real.

Science offers us the possibility of immortality that’s real.

It won’t be easy to get there. It won’t happen any time soon. In all likelihood, we won’t live to see it ourselves. But one day, our descendants may achieve the grandest goal of all: Finally conquering death.

And even long before that glorious day, our lives are already being made longer and healthier by science. We are pushing death back, step by step, day by day. We are fighting, and we are winning.

Moreover, we as individuals are not powerless in this fight: you can fight death a little harder yourself, by becoming an organ donor, or by donating to organizations that fight global poverty or advance medical science. Let your grief drive you to help others, so that they don’t have to grieve as you do.

And if you need consolation from your grief, let it come from this truth: Death is rarer now today than it was yesterday, and will be rarer still tomorrow. We can’t bring back who we have lost, but we can keep ourselves from losing more so soon.

Solving the student debt problem

PNRJ education, inequality, market failure, politics, public policy, science and academia , , , , , , , ,

Aug 24 JDN 2460912

A lot of people speak about student debt as a “crisis”, which makes it sound like the problem is urgent and will have severe consequences if we don’t soon intervene. I don’t think that’s right. While it’s miserable to be unable to pay your student loans, student loans don’t seem to be driving people to bankruptcy or homelessness the way that medical bills do.

Instead I think what we have here is a long-term problem, something that’s been building for a long time and will slowly but surely continue getting worse if we don’t change course. (I guess you can still call it a “crisis” if you want; climate change is also like this, and arguably a crisis.)

But there is a problem here: Student loan balances are rising much faster than other kinds of debt, and the burden falls the worst on Black women and students who went to for-profit schools. A big part of the problem seems to be predatory schools that charge high prices and make big promises but offer poor results.

Making all this worse is the fact that some of the most important income-based repayment plans were overturned by a federal court, forcing everyone who was on them into forebearance. Income-based repayment was a big reason why student loans actually weren’t as bad a burden as their high loan balances might suggest; unlike a personal loan or a mortgage, if you didn’t have enough income to repay your student loans at the full amount, you could get on a plan that would let you make smaller payments, and if you paid on that plan for long enough—even if it didn’t add up to the full balance—your loans would be forgiven.

Now the forebearance is ending for a lot of borrowers, and so they are going into default; and most of that loan forgiveness has been ruled illegal. (Supposedly this is because Congress didn’t approve it. I’ll believe that was the reason when the courts overrule Trump’s tariffs, which clearly have just as thin a legal justification and will cause far more harm to us and the rest of the world.)

In theory, student loans don’t really seem like a bad idea.

College is expensive, because it requires highly-trained professors, who demand high salaries. (The tuition money also goes other places, of course….)

College is valuable, because it provides you with knowledge and skills that can improve your life and also increase your long-term earnings. It’s a big difference: Median salary for someone with a college degree is about $60k, while median salary for someone with only a high school diploma is about $34k.

Most people don’t have enough liquidity to pay for college.

So, we provide loans, so that people can pay for college, and then when they make more money after graduating, they can pay the loans back.

That’s the theory, anyway.

The problem is that average or even median salaries obscure a lot of variation. Some college graduates become doctors, lawyers, or stockbrokers and make huge salaries. Others can’t find jobs at all. In the absence of income-based repayment plans, all students have to pay back their loans in full, regardless of their actual income after graduation.

There is inherent risk in trying to build a career. Our loan system—especially with the recent changes—puts most of this risk on the student. We treat it as their fault they can’t get a good job, and then punish them with loans they can’t afford to repay.

In fact, right now the job market is pretty badfor recent graduates—while usually unemployment for recent college grads is lower than that of the general population, since about 2018 it has actually been higher. (It’s no longer sky-high like it was during COVID; 4.8% is not bad in the scheme of things.)

Actually the job market may even be worse than it looks, because new hires are actually the lowest rate they’ve been since 2020. Our relatively low unemployment currently seems to reflect a lack of layoffs, not a healthy churn of people entering and leaving jobs. People seem to be locked into their jobs, and if they do leave them, finding another is quite difficult.

What I think we need is a system that makes the government take on more of the risk, instead of the students.

There are lots of ways to do this. Actually, the income-based repayment systems we used to have weren’t too bad.

But there is actually a way to do it without student loans at all. College could be free, paid for by taxes.


Now, I know what you’re thinking: Isn’t this unfair to people who didn’t go to college? Why should they have to pay?

Who said they were paying?

There could simply be a portion of the income tax that you only pay if you have a bachelor’s degree. Then you would only pay this tax if you both graduated from college and make a lot of money.

I don’t think this would create a strong incentive not to get a bachelor’s degree; the benefits of doing so remain quite large, even if your taxes were a bit higher as a result.

It might create incentives to major in subjects that aren’t as closely linked to higher earnings—liberal arts instead of engineering, medicine, law, or business. But this I see as fundamentally a public good: The world needs people with liberal arts education. If the market fails to provide for them, the government should step in.

This plan is not as progressive as Elizabeth Warren’s proposal to use wealth taxes to fund free college; but it might be more politically feasible. The argument that people who didn’t go to college shouldn’t have to pay for people who did actually seems reasonable to me; but this system would ensure that in fact they don’t.

The transfer of wealth here would be from people who went to college and make a lot of money to people who went to college and don’t make a lot of money. It would be the government bearing some of the financial risk of taking on a career in an uncertain world.

Conflict without shared reality

PNRJ core principles, current events, ethics, inequality, politics, tribal paradigm , , , , , , , , , , , , , , , , , , , , , , , ,

Aug 17 JDN 2460905

Donald Trump has federalized the police in Washington D.C. and deployed the National Guard. He claims he is doing this in response to a public safety emergency and crime that is “out of control”.

Crime rates in Washington, D.C. are declining and overall at their lowest level in 30 years. Its violent crime rate has not been this low since the 1960s.

By any objective standard, there is no emergency here. Crime in D.C. is not by any means out of control.

Indeed, across the United States, homicide rates are as low as they have been in 60 years.

But we do not live in a world where politics is based on objective truth.

We live in a world where the public perception of reality itself is shaped by the political narrative.

One of the first things that authoritarians do to control these narratives is try to make their followers distrust objective sources. I watch in disgust as not simply the Babylon Bee (which is a right-wing satire site that tries really hard to be funny but never quite manages it) but even the Atlantic (a mainstream news outlet generally considered credible) feeds—in multiple articles—into this dangerous lie that crime is increasing and the official statistics are somehow misleading us about that.

Of course the Atlantic‘s take is much more nuanced; but quite frankly, now is not the time for nuance. A fascist is trying to take over our government, and he needs to be resisted at every turn by every means possible. You need to be calling him out on every single lie he makes—yes, every single one, I know there are a lot of them, and that’s kind of the point—rather than trying to find alternative framings on which maybe part of what he said could somehow be construed as reasonable from a certain point of view. Every time you make Trump sound more reasonable than he is—and mainstream news outlets have done this literally hundreds of times—you are pushing America closer to fascism.

I really don’t know what to do here.

It is impossible to resolve conflicts when they are not based on shared reality.

No policy can solve a crime wave that doesn’t exist. No trade agreement can stop unfair trading practices that aren’t happening. Nothing can stop vaccines from causing autism that they already don’t cause. There is no way to fix problems when those problems are completely imaginary.

I used to think that political conflict was about different values which had to be balanced against one another: Liberty versus security, efficiency versus equality, justice versus mercy. I thought that we all agreed on the basic facts and even most of the values, and were just disagreeing about how to weigh certain values over others.

Maybe I was simply naive; maybe it’s never been like that. But it certainly isn’t right now. We aren’t disagreeing about what should be done; we are disagreeing about what is happening in front of our eyes. We don’t simply have different priorities or even different values; it’s like we are living in different worlds.

I have read, e.g. by Jonathan Haidt, that conservatives largely understand what liberals want, but liberals don’t really understand what conservatives want. (I would like to take one of the tests they use in these experiments, see how I actually do; but I’ve never been able to find one.)

Haidt’s particular argument seems to be that liberals don’t “understand” the “moral dimensions” of loyalty, authority, and sanctity, because we only “understand” harm and fairness as the basis of morality. But just because someone says something is morally relevant, that doesn’t mean it is morally relevant! And indeed, based on more or less the entirety of ethical philosophy, I can say that harm and fairness are morality, and the others simply aren’t. They are distortions of morality, they are inherently evil, and we are right to oppose them at every turn. Loyalty, authority, and sanctity are what fed Nazi Germany and the Spanish Inquisition.

This claim that liberals don’t understand conservatives has always seemed very odd to me: I feel like I have a pretty clear idea what conservatives want, it’s just that what they want is terrible: Kick out the immigrants, take money from the poor and give it to the rich, and put rich straight Christian White men back in charge of everything. (I mean, really, if that’s not what they want, why do they keep voting for people who do it? Revealed preferences, people!)

Or, more sympathetically: They want to go back to a nostalgia-tinted vision of the 1950s and 1960s in which it felt like things were going well for our country—because they were blissfully ignorant of all the violence and injustice in the world. No, thank you, Black people and queer people do not want to go back to how we were treated in the 1950s—when segregation was legal and Alan Turing was chemically castrated. (And they also don’t seem to grasp that among the things that did make some things go relatively well in that period were unions, antitrust law and progressive taxes, which conservatives now fight against at every turn.)

But I think maybe part of what’s actually happening here is that a lot of conservatives actually “want” things that literally don’t make sense, because they rest upon assumptions about the world that simply aren’t true.

They want to end “out of control” crime that is the lowest it’s been in decades.

They want to stop schools from teaching things that they already aren’t teaching.

They want the immigrants to stop bringing drugs and crime that they aren’t bringing.

They want LGBT people to stop converting their children, which we already don’t and couldn’t. (And then they want to do their own conversions in the other direction—which also don’t work, but cause tremendous harm.)

They want liberal professors to stop indoctrinating their students in ways we already aren’t and can’t. (If we could indoctrinate our students, don’t you think we’d at least make them read the syllabus?)

They want to cut government spending by eliminating “waste” and “fraud” that are trivial amounts, without cutting the things that are actually expensive, like Social Security, Medicare, and the military. They think we can balance the budget without cutting these things or raising taxes—which is just literally mathematically impossible.

They want to close off trade to bring back jobs that were sent offshore—but those jobs weren’t sent offshore, they were replaced by robots. (US manufacturing output is near its highest ever, even though manufacturing employment is half what it once was.)


And meanwhile, there’s a bunch of real problems that aren’t getting addressed: Soaring inequality, a dysfunctional healthcare system, climate change, the economic upheaval of AI—and they either don’t care about those, aren’t paying attention to them, or don’t even believe they exist.

It feels a bit like this:

You walk into a room and someone points a gun at you, shouting “Drop the weapon!” but you’re not carrying a weapon. And you show your hands, and try to explain that you don’t have a weapon, but they just keep shouting “Drop the weapon!” over and over again. Someone else has already convinced them that you have a weapon, and they expect you to drop that weapon, and nothing you say can change their mind about this.

What exactly should you do in that situation?

How do you avoid getting shot?

Do you drop something else and say it’s the weapon (make some kind of minor concession that looks vaguely like what they asked for)? Do you try to convince them that you have a right to the weapon (accept their false premise but try to negotiate around it)? Do you just run away (leave the country?)? Do you double down and try even harder to convince them that you really, truly, have no weapon?

I’m not saying that everyone on the left has a completely accurate picture of reality; there are clearly a lot of misconceptions on this side of the aisle as well. But at least among the mainstream center left, there seems to be a respect for objective statistics and a generally accurate perception of how the world works—the “reality-based community”. Sometimes liberals make mistakes, have bad ideas, or even tell lies; but I don’t hear a lot of liberals trying to fix problems that don’t exist or asking for the government budget to be changed in ways that violate basic arithmetic.

I really don’t know what do here, though.

How do you change people’s minds when they won’t even agree on the basic facts?

On foxes and hedgehogs, part II

PNRJ heuristics and biases, politics, statistics , , , , , , , , , , , , ,

Aug 3 JDN 2460891

In last week’s post I described Philip E. Tetlock’s experiment showing that “foxes” (people who are open-minded and willing to consider alternative views) make more accurate predictions than “hedgehogs” (people who are dogmatic and conform strictly to a single ideology).

As I explained at the end of the post, he, uh, hedges on this point quite a bit, coming up with various ways that the hedgehogs might be able to redeem themselves, but still concluding that in most circumstances, the foxes seem to be more accurate.

Here are my thoughts on this:

I think he went too easy on the hedgehogs.

I consider myself very much a fox, and I honestly would never assign a probability of 0% or 100% to any physically possible event. Honestly I consider it a flaw in Tetlock’s design that he included those as options but didn’t include probabilities I would assign, like 1%, 0.1%, or 0.01%.

He only let people assign probabilities in 10% increments. So I guess if you thought something was 3% likely, you’re supposed to round to 0%? That still feels terrible. I’d probably still write 10%. There weren’t any questions like “Aliens from the Andromeda Galaxy arrive to conquer our planet, thus rendering all previous political conflicts moot”, but man, had there been, I’d still be tempted to not put 0%. I guess I would put 0% for that though? Because in 99.999999% of cases, I’d get it right—it wouldn’t happen—and I’d get more points. But man, even single-digit percentages? I’d mash the 10% button. I am pretty much allergic to overconfidence.

In fact, I think in my mind I basically try to use a logarithmic score, which unlike a Brier score, severely (technically, infinitely) punishes you for saying that something impossible happened or something inevitable didn’t. Like, really, if you’re doing it right, that should never, ever happen to you. If you assert that something has 0% probability and it happens, you have just conclusively disproven your worldview. (Admittedly it’s possible you could fix it with small changes—but a full discussion of that would get us philosophically too far afield. “outside the scope of this paper”.)

So honestly I think he was too lenient on overconfidence by using a Brier score, which does penalize this kind of catastrophic overconfidence, but only by a moderate amount. If you say that something has a 0% chance and then it happens, you get a Brier score of -1. But if you say that something has a 50% chance and then it happens (which it would, you know, 50% of the time), you’d get a Brier score of -0.25. So even absurd overconfidence isn’t really penalized that badly.

Compare this to a logarithmic rule: Say 0% and it happens, and you get negative infinity. You lose. You fail. Go home. Your worldview is bad and you should feel bad. This should never happen to you if you have a coherent worldview (modulo the fact that he didn’t let you say 0.01%).

So if I had designed this experiment, I would have given finer-grained options at the extremes, and then brought the hammer down on anybody who actually asserted a 0% chance of an event that actually occurred. (There’s no need for the finer-grained options elsewhere; over millennia of history, the difference between 0% and 0.1% is whether it won’t happen or it will—quite relevant for, say, full-scale nuclear war—while the difference between 40% and 42.1% is whether it’ll happen every 2 to 3 years or… every 2 to 3 years.)

But okay, let’s say we stick with the Brier score, because infinity is scary.

  1. About the adjustments:
    1. The “value adjustments” are just absolute nonsense. Those would be reasons to adjust your policy response, via your utility function—they are not a reason to adjust your probability. Yes, a nuclear terrorist attack would be a really big deal if it happened and we should definitely be taking steps to prevent that; but that doesn’t change the fact that the probability of one happening is something like 0.1% per year and none have ever happened. Predicting things that don’t happen is bad forecasting, even if the things you are predicting would be very important if they happened.
    2. The “difficulty adjustments” are sort of like applying a different scoring rule, so that I’m more okay with; but that wasn’t enough to make the hedgehogs look better than the foxes.
    3. The “fuzzy set” adjustments could be legitimate, but only under particular circumstances. Being “almost right” is only valid if you clearly showed that the result was anomalous because of some other unlikely event, and—because the timeframe was clearly specified in the questions—“might still happen” should still get fewer points than accurately predicting that it hasn’t happened yet. Moreover, it was very clear that people only ever applied these sort of changes when they got things wrong; they rarely if ever said things like “Oh, wow, I said that would happen and it did, but for completely different reasons that I didn’t expect—I was almost wrong there.” (Crazy example, but if the Soviet Union had been taken over by aliens, “the Soviet Union will fall” would be correct—but I don’t think you could really attribute that to good political prediction.)
  2. The second exercise shows that even the foxes are not great Bayesians, and that some manipulations can make people even more inaccurate than before; but the hedgehogs also perform worse and also make some of the same crazy mistakes and still perform worse overall than the foxes, even in that experiment.
  3. I guess he’d call me a “hardline neopositivist”? Because I think that your experiment asking people to predict things should require people to, um, actually predict things? The task was not to get the predictions wrong but be able to come up with clever excuses for why they were wrong that don’t challenge their worldview. The task was to not get the predictions wrong. Apparently this very basic level of scientific objectivity is now considered “hardline neopositivism”.

I guess we can reasonably acknowledge that making policy is about more than just prediction, and indeed maybe being consistent and decisive is advantageous in a game-theoretic sense (in much the same way that the way to win a game of Chicken is to very visibly throw away your steering wheel). So you could still make a case for why hedgehogs are good decision-makers or good leaders.

But I really don’t see how you weasel out of the fact that hedgehogs are really bad predictors. If I were running a corporation, or a government department, or an intelligence agency, I would want accurate predictions. I would not be interested in clever excuses or rich narratives. Maybe as leaders one must assemble such narratives in order to motivate people; so be it, there’s a division of labor there. Maybe I’d have a separate team of narrative-constructing hedgehogs to help me with PR or something. But the people who are actually analyzing the data should be people who are good at making accurate predictions, full stop.

And in fact, I don’t think hedgehogs are good decision-makers or good leaders. I think they are good politicians. I think they are good at getting people to follow them and believe what they say. But I do not think they are actually good at making the decisions that would be the best for society.

Indeed, I think this is a very serious problem.

I think we systematically elect people to higher office—and hire them for jobs, and approve them for tenure, and so on—because they express confidence rather than competence. We pick the people who believe in themselves the most, who (by regression to the mean if nothing else) are almost certainly the people who are most over-confident in themselves.

Given that confidence is easier to measure than competence in most areas, it might still make sense to choose confident people if confidence were really positively correlated with competence, but I’m not convinced that it is. I think part of what Tetlock is showing us is that the kind of cognitive style that yields high confidence—a hedgehog—simply is not the kind of cognitive style that yields accurate beliefs—a fox. People who are really good at their jobs are constantly questioning themselves, always open to new ideas and new evidence; but that also means that they hedge their bets, say “on the other hand” a lot, and often suffer from Impostor Syndrome. (Honestly, testing someone for Impostor Syndrome might be a better measure of competence than a traditional job interview! Then again, Goodhart’s Law.)

Indeed, I even see this effect within academic science; the best scientists I know are foxes through and through, but they’re never the ones getting published in top journals and invited to give keynote speeches at conferences. The “big names” are always hedgehog blowhards with some pet theory they developed in the 1980s that has failed to replicate but somehow still won’t die.

Moreover, I would guess that trustworthiness is actually pretty strongly inversely correlated to confidence—“con artist” is short for “confidence artist”, after all.

Then again, I tried to find rigorous research comparing openness (roughly speaking “fox-ness”) or humility to honesty, and it was surprisingly hard to find. Actually maybe the latter is just considered an obvious consensus in the literature, because there is a widely-used construct called honesty-humility. (In which case, yeah, my thinking on trustworthiness and confidence is an accepted fact among professional psychologists—but then, why don’t more people know that?)

But that still doesn’t tell me if there is any correlation between honesty-humility and openness.

I did find these studies showing that both honesty-humility and openness are both positively correlated with well-being, both positively correlated with cooperation in experimental games, and both positively correlated with being left-wing; but that doesn’t actually prove they are positively correlated with each other. I guess it provides weak evidence in that direction, but only weak evidence. It’s entirely possible for A to be positively correlated with both B and C but B and C are uncorrelated or negatively correlated. (Living in Chicago is positively correlated with being a White Sox fan and positively correlated with being a Cubs fan, but being a White Sox fan is not positively correlated with being a Cubs fan!)

I also found studies showing that higher openness predicts less right-wing authoritarianism and higher honesty predicts less social conformity; but that wasn’t the question either.

Here’s a factor analysis specifically arguing for designing measures of honesty-humility so that they don’t correlate with other personality traits, so it can be seen as its own independent personality trait. There are some uncomfortable degrees of freedom in designing new personality metrics, which may make this sort of thing possible; and then by construction honesty-humility and openness would be uncorrelated, because any shared components were parceled out to one trait or the other.

So, I guess I can’t really confirm my suspicion here; maybe people who think like hedgehogs aren’t any less honest, or are even more honest, than people who think like foxes. But I’d still bet otherwise. My own life experience has been that foxes are honest and humble while hedgehogs are deceitful and arrogant.

Indeed, I believe that in systematically choosing confident hedgehogs as leaders, the world economy loses tens of trillions of dollars a year in inefficiencies. In fact, I think that we could probably end world hunger if we only ever put leaders in charge who were both competent and trustworthy.

Of course, in some sense that’s a pipe dream; we’re never going to get all good leaders, just as we’ll never get zero death or zero crime.

But based on how otherwise-similar countries have taken wildly different trajectories based on differences in leadership, I suspect that even relatively small changes in that direction could have quite large impacts on a society’s outcomes: South Korea isn’t perfect at picking its leaders; but surely it’s better than North Korea, and indeed that seems like one of the primary things that differentiates the two countries. Botswana is not a utopian paradise, but it’s a much nicer place to live than Nigeria, and a lot of the difference seems to come down to who is in charge, or who has been in charge for the last few decades.

And I could put in a jab here about the current state of the United States, but I’ll resist. If you read my blog, you already know my opinions on this matter.

On foxes and hedgehogs, part I

PNRJ cognitive science, core principles, ethics, experimental economics, mathematical models, politics, public policy, statistics , , , , , , , , , , ,

Aug 3 JDN 2460891

Today I finally got around to reading Expert Political Judgment by Philip E. Tetlock, more or less in a single sitting because I’ve been sick the last week with some pretty tight limits on what activities I can do. (It’s mostly been reading, watching TV, or playing video games that don’t require intense focus.)

It’s really an excellent book, and I now both understand why it came so highly recommended to me, and now pass on that recommendation to you: Read it.

The central thesis of the book really boils down to three propositions:

  1. Human beings, even experts, are very bad at predicting political outcomes.
  2. Some people, who use an open-minded strategy (called “foxes”), perform substantially better than other people, who use a more dogmatic strategy (called “hedgehogs”).
  3. When rewarding predictors with money, power, fame, prestige, and status, human beings systematically favor (over)confident “hedgehogs” over (correctly) humble “foxes”.

I decided I didn’t want to make this post about current events, but I think you’ll probably agree with me when I say:

That explains a lot.

How did Tetlock determine this?

Well, he studies the issue several different ways, but the core experiment that drives his account is actually a rather simple one:

  1. He gathered a large group of subject-matter experts: Economists, political scientists, historians, and area-studies professors.
  2. He came up with a large set of questions about politics, economics, and similar topics, which could all be formulated as a set of probabilities: “How likely is this to get better/get worse/stay the same?” (For example, this was in the 1980s, so he asked about the fate of the Soviet Union: “By 1990, will they become democratic, remain as they are, or collapse and fragment?”)
  3. Each respondent answered a subset of the questions, some about their own particular field, some about another, more distant field; they assigned probabilities on an 11-point scale, from 0% to 100% in increments of 10%.
  4. A few years later, he compared the predictions to the actual results, scoring them using a Brier score, which penalizes you for assigning high probability to things that didn’t happen or low probability to things that did happen.
  5. He compared the resulting scores between people with different backgrounds, on different topics, with different thinking styles, and a variety of other variables. He also benchmarked them using some automated algorithms like “always say 33%” and “always give ‘stay the same’ 100%”.

I’ll show you the key results of that analysis momentarily, but to help it make more sense to you, let me elaborate a bit more on the “foxes” and “hedgehogs”. The notion is was first popularized by Isaiah Berlin in an essay called, simply, The Hedgehog and the Fox.

“The fox knows many things, but the hedgehog knows one very big thing.”

That is, someone who reasons as a “fox” combines ideas from many different sources and perspective, and tries to weigh them all together into some sort of synthesis that then yields a final answer. This process is messy and complicated, and rarely yields high confidence about anything.

Whereas, someone who reasons as a “hedgehog” has a comprehensive theory of the world, an ideology, that provides clear answers to almost any possible question, with the surely minor, insubstantial flaw that those answers are not particularly likely to be correct.

He also considered “hedge-foxes” (people who are mostly fox but also a little bit hedgehog) and “fox-hogs” (people who are mostly hedgehog but also a little bit fox).

Tetlock has decomposed the scores into two components: calibration and discrimination. (Both very overloaded words, but they are standard in the literature.)

Calibration is how well your stated probabilities matched up with the actual probabilities; that is, if you predicted 10% probability on 20 different events, you have very good calibration if precisely 2 of those events occurred, and very poor calibration if 18 of those events occurred.

Discrimination more or less describes how useful your predictions are, what information they contain above and beyond the simple base rate. If you just assign equal probability to all events, you probably will have reasonably good calibration, but you’ll have zero discrimination; whereas if you somehow managed to assign 100% to everything that happened and 0% to everything that didn’t, your discrimination would be perfect (and we would have to find out how you cheated, or else declare you clairvoyant).

For both measures, higher is better. The ideal for each is 100%, but it’s virtually impossible to get 100% discrimination and actually not that hard to get 100% calibration if you just use the base rates for everything.


There is a bit of a tradeoff between these two: It’s not too hard to get reasonably good calibration if you just never go out on a limb, but then your predictions aren’t as useful; we could have mostly just guessed them from the base rates.

On the graph, you’ll see downward-sloping lines that are meant to represent this tradeoff: Two prediction methods that would yield the same overall score but different levels of calibration and discrimination will be on the same line. In a sense, two points on the same line are equally good methods that prioritize usefulness over accuracy differently.

All right, let’s see the graph at last:

The pattern is quite clear: The more foxy you are, the better you do, and the more hedgehoggy you are, the worse you do.

I’d also like to point out the other two regions here: “Mindless competition” and “Formal models”.

The former includes really simple algorithms like “always return 33%” or “always give ‘stay the same’ 100%”. These perform shockingly well. The most sophisticated of these, “case-specific extrapolation” (35 and 36 on the graph, which basically assumes that each country will continue doing what it’s been doing) actually performs as well if not better than even the foxes.

And what’s that at the upper-right corner, absolutely dominating the graph? That’s “Formal models”. This describes basically taking all the variables you can find and shoving them into a gigantic logit model, and then outputting the result. It’s computationally intensive and requires a lot of data (hence why he didn’t feel like it deserved to be called “mindless”), but it’s really not very complicated, and it’s the best prediction method, in every way, by far.

This has made me feel quite vindicated about a weird nerd thing I do: When I have a big decision to make (especially a financial decision), I create a spreadsheet and assemble a linear utility model to determine which choice will maximize my utility, under different parameterizations based on my past experiences. Whichever result seems to win the most robustly, I choose. This is fundamentally similar to the “formal models” prediction method, where the thing I’m trying to predict is my own happiness. (It’s a bit less formal, actually, since I don’t have detailed happiness data to feed into the regression.) And it has worked for me, astonishingly well. It definitely beats going by my own gut. I highly recommend it.

What does this mean?

Well first of all, it means humans suck at predicting things. At least for this data set, even our experts don’t perform substantially better than mindless models like “always assume the base rate”.

Nor do experts perform much better in their own fields than in other fields; they do all perform better than undergrads or random people (who somehow perform worse than the “mindless” models)

But Tetlock also investigates further, trying to better understand this “fox/hedgehog” distinction and why it yields different performance. He really bends over backwards to try to redeem the hedgehogs, in the following ways:

  1. He allows them to make post-hoc corrections to their scores, based on “value adjustments” (assigning higher probability to events that would be really important) and “difficulty adjustments” (assigning higher scores to questions where the three outcomes were close to equally probable) and “fuzzy sets” (giving some leeway on things that almost happened or things that might still happen later).
  2. He demonstrates a different, related experiment, in which certain manipulations can cause foxes to perform a lot worse than they normally would, and even yield really crazy results like probabilities that add up to 200%.
  3. He has a whole chapter that is a Socratic dialogue (seriously!) between four voices: A “hardline neopositivist”, a “moderate neopositivist”, a “reasonable relativist”, and an “unrelenting relativist”; and all but the “hardline neopositivist” agree that there is some legitimate place for the sort of post hoc corrections that the hedgehogs make to keep themselves from looking so bad.

This post is already getting a bit long, so that will conclude part I. Stay tuned for part II, next week!