logic

What is it with EA and AI?

pnrj cognitive science, computer science, ethics, futurism, inequality, public policy, science and academia , , , , , , , , , ,

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?

On the Overton Window

pnrj core principles, ethics, heuristics and biases, politics, tribal paradigm , , , , , , , , , ,

Jul 24 JDN 2459786

As you are no doubt aware, a lot of people on the Internet like to loudly proclaim support for really crazy, extreme ideas. Some of these people actually believe in those ideas, and if you challenge them, will do their best to defend them. Those people are wrong at the level of substantive policy, but there’s nothing wrong with their general approach: If you really think that anarchism or communism is a good thing, it only makes sense that you’d try to convince other people. You might have a hard time of it (in part because you are clearly wrong), but it makes sense that you’d try.

But there is another class of people who argue for crazy, extreme ideas. When pressed, they will admit they don’t really believe in abolishing the police or collectivizing all wealth, but they believe in something else that’s sort of vaguely in that direction, and they think that advocating for the extreme idea will make people more likely to accept what they actually want.

They often refer to this as “shifting the Overton Window”. As Matt Yglesias explained quite well a year ago, this is not actually what Overton was talking about.

But, in principle, it could still be a thing that works. There is a cognitive bias known as anchoring which is often used in marketing: If I only offered a $5 bottle of wine and a $20 bottle of wine, you might think the $20 bottle is too expensive. But if I also include a $50 bottle, that makes you adjust your perceptions of what constitutes a “reasonable” price for wine, and may make you more likely to buy the $20 bottle after all.

It could be, therefore, that an extreme policy demand makes people more willing to accept moderate views, as a sort of compromise. Maybe demanding the abolition of police is a way of making other kinds of police reform seem more reasonable. Maybe showing pictures of Marx and chanting “eat the rich” could make people more willing to accept higher capital gains taxes. Maybe declaring that we are on the verge of apocalyptic climate disaster will make people more willing to accept tighter regulations on carbon emissions and subsidies for solar energy.

Then again—does it actually seem to do that? I see very little evidence that it does. All those demands for police abolition haven’t changed the fact that defunding the police is unpopular. Raising taxes on the rich is popular, but it has been for awhile now (and never was with, well, the rich). And decades of constantly shouting about imminent climate catastrophe is really starting to look like crying wolf.

To see why this strategy seems to be failing, I think it’s helpful to consider how it feels from the other side. Take a look at some issues where someone else is trying to get you to accept a particular view, and consider whether someone advocating a more extreme view would make you more likely to compromise.

Your particular opinions may vary, but here are some examples that would apply to me, and, I suspect, many of you.

If someone says they want tighter border security, I’m skeptical—it’s pretty tight already. But in and of itself, this would not be such a crazy idea. Certainly I agree that it is possible to have too little border security, and so maybe that turns out to be the state we’re in.

But then, suppose that same person, or someone closely allied to them, starts demanding the immediate deportation of everyone who was not born in the United States, even those who immigrated legally and are naturalized or here on green cards. This is a crazy, extreme idea that’s further in the same direction, so on this anchoring theory, it should make me more willing to accept the idea of tighter border security. And yet, I can say with some confidence that it has no such effect.

Indeed, if anything I think it would make me less likely to accept tighter border security, in proportion to how closely aligned those two arguments are. If they are coming from the same person, or the same political party, it would cause me to suspect that the crazy, extreme policy is the true objective, and the milder, compromise policy is just a means toward that end. It also suggests certain beliefs and attitudes about immigration in general—xenophobia, racism, ultranationalism—that I oppose even more strongly. If you’re talking about deporting all immigrants, you make me suspect that your reasons for wanting tighter border security are not good ones.

Let’s try another example. Suppose someone wants to cut taxes on upper income brackets. In our current state, I think that would be a bad idea. But there was a time not so long ago when I would have agreed with it: Even I have to admit that a top bracket of 94% (as we had in 1943) sounds a little ridiculous, and is surely on the wrong side of the Laffer curve. So the basic idea of cutting top tax rates is not inherently crazy or ridiculous.

Now, suppose that same idea came from the same person, or the same party, or the same political movement, as one that was arguing for the total abolition of all taxation. This is a crazy, extreme idea; it would amount to either total anarcho-capitalism with no government at all, or some sort of bizarre system where the government is funded entirely through voluntary contributions. I think it’s pretty obvious that such a system would be terrible, if not outright impossible; and anyone whose understanding of political economy is sufficiently poor that they would fail to see this is someone whose overall judgment on questions of policy I must consider dubious. Once again, the presence of the extreme view does nothing to make me want to consider the moderate view, and may even make me less willing to do so.

Perhaps I am an unusually rational person, not so greatly affected by anchoring biases? Perhaps. But whereas I do feel briefly tempted by to buy the $20 wine bottle by the effect of the $50 wine bottle, and must correct myself with knowledge I have about anchoring bias, the presentation of an extreme political view never even makes me feel any temptation to accept some kind of compromise with it. Learning that someone supports something crazy or ridiculous—or is willing to say they do, even if deep down they don’t—makes me automatically lower my assessment of their overall credibility. If anything, I think I am tempted to overreact in that direction, and have to remind myself of the Stopped Clock Principle: reversed stupidity is not intelligence, and someone can have both bad ideas and good ones.

Moreover, the empirical data, while sketchy, doesn’t seem to support this either; where the Overton Window (in the originally intended sense) has shifted, as on LGBT rights, it was because people convincingly argued that the “extreme” position was in fact an entirely reasonable and correct view. There was a time not so long ago that same-sex marriage was deemed unthinkable, and the “moderate” view was merely decriminalizing sodomy; but we demanded, and got, same-sex marriage, not as a strategy to compromise on decriminalizing sodomy, but because we actually wanted same-sex marriage and had good arguments for it. I highly doubt we would have been any more successful if we had demanded something ridiculous and extreme, like banning opposite-sex marriage.

The resulting conclusion seems obvious and banal: Only argue for things you actually believe in.

Yet, somehow, that seems to be a controversial view these days.

The sausage of statistics being made

pnrj education, statistics , , , , , , , , , , , , , , , , ,

 

Nov 11 JDN 2458434

“Laws, like sausages, cease to inspire respect in proportion as we know how they are made.”

~ John Godfrey Saxe, not Otto von Bismark

Statistics are a bit like laws and sausages. There are a lot of things in statistical practice that don’t align with statistical theory. The most obvious examples are the fact that many results in statistics are asymptotic: they only strictly apply for infinitely large samples, and in any finite sample they will be some sort of approximation (we often don’t even know how good an approximation).

But the problem runs deeper than this: The whole idea of a p-value was originally supposed to be used to assess one single hypothesis that is the only one you test in your entire study.

That’s frankly a ludicrous expectation: Why would you write a whole paper just to test one parameter?

This is why I don’t actually think this so-called multiple comparisons problem is a problem with researchers doing too many hypothesis tests; I think it’s a problem with statisticians being fundamentally unreasonable about what statistics is useful for. We have to do multiple comparisons, so you should be telling us how to do it correctly.

Statisticians have this beautiful pure mathematics that generates all these lovely asymptotic results… and then they stop, as if they were done. But we aren’t dealing with infinite or even “sufficiently large” samples; we need to know what happens when your sample is 100, not when your sample is 10^29. We can’t assume that our variables are independently identically distributed; we don’t know their distribution, and we’re pretty sure they’re going to be somewhat dependent.

Even in an experimental context where we can randomly and independently assign some treatments, we can’t do that with lots of variables that are likely to matter, like age, gender, nationality, or field of study. And applied econometricians are in an even tighter bind; they often can’t randomize anything. They have to rely upon “instrumental variables” that they hope are “close enough to randomized” relative to whatever they want to study.

In practice what we tend to do is… fudge it. We use the formal statistical methods, and then we step back and apply a series of informal norms to see if the result actually makes sense to us. This is why almost no psychologists were actually convinced by Daryl Bem’s precognition experiments, despite his standard experimental methodology and perfect p < 0.05 results; he couldn’t pass any of the informal tests, particularly the most basic one of not violating any known fundamental laws of physics. We knew he had somehow cherry-picked the data, even before looking at it; nothing else was possible.

This is actually part of where the “hierarchy of sciences” notion is useful: One of the norms is that you’re not allowed to break the rules of the sciences above you, but you can break the rules of the sciences below you. So psychology has to obey physics, but physics doesn’t have to obey psychology. I think this is also part of why there’s so much enmity between economists and anthropologists; really we should be on the same level, cognizant of each other’s rules, but economists want to be above anthropologists so we can ignore culture, and anthropologists want to be above economists so they can ignore incentives.

Another informal norm is the “robustness check”, in which the researcher runs a dozen different regressions approaching the same basic question from different angles. “What if we control for this? What if we interact those two variables? What if we use a different instrument?” In terms of statistical theory, this doesn’t actually make a lot of sense; the probability distributions f(y|x) of y conditional on x and f(y|x, z) of y conditional on x and z are not the same thing, and wouldn’t in general be closely tied, depending on the distribution f(x|z) of x conditional on z. But in practice, most real-world phenomena are going to continue to show up even as you run a bunch of different regressions, and so we can be more confident that something is a real phenomenon insofar as that happens. If an effect drops out when you switch out a couple of control variables, it may have been a statistical artifact. But if it keeps appearing no matter what you do to try to make it go away, then it’s probably a real thing.

Because of the powerful career incentives toward publication and the strange obsession among journals with a p-value less than 0.05, another norm has emerged: Don’t actually trust p-values that are close to 0.05. The vast majority of the time, a p-value of 0.047 was the result of publication bias. Now if you see a p-value of 0.001, maybe then you can trust it—but you’re still relying on a lot of assumptions even then. I’ve seen some researchers argue that because of this, we should tighten our standards for publication to something like p < 0.01, but that’s missing the point; what we need to do is stop publishing based on p-values. If you tighten the threshold, you’re just going to get more rejected papers and then the few papers that do get published will now have even smaller p-values that are still utterly meaningless.

These informal norms protect us from the worst outcomes of bad research. But they are almost certainly not optimal. It’s all very vague and informal, and different researchers will often disagree vehemently over whether a given interpretation is valid. What we need are formal methods for solving these problems, so that we can have the objectivity and replicability that formal methods provide. Right now, our existing formal tools simply are not up to that task.

There are some things we may never be able to formalize: If we had a formal algorithm for coming up with good ideas, the AIs would already rule the world, and this would be either Terminator or The Culture depending on whether we designed the AIs correctly. But I think we should at least be able to formalize the basic question of “Is this statement likely to be true?” that is the fundamental motivation behind statistical hypothesis testing.

I think the answer is likely to be in a broad sense Bayesian, but Bayesians still have a lot of work left to do in order to give us really flexible, reliable statistical methods we can actually apply to the messy world of real data. In particular, tell us how to choose priors please! Prior selection is a fundamental make-or-break problem in Bayesian inference that has nonetheless been greatly neglected by most Bayesian statisticians. So, what do we do? We fall back on informal norms: Try maximum likelihood, which is like using a very flat prior. Try a normally-distributed prior. See if you can construct a prior from past data. If all those give the same thing, that’s a “robustness check” (see previous informal norm).

Informal norms are also inherently harder to teach and learn. I’ve seen a lot of other grad students flail wildly at statistics, not because they don’t know what a p-value means (though maybe that’s also sometimes true), but because they don’t really quite grok the informal underpinnings of good statistical inference. This can be very hard to explain to someone: They feel like they followed all the rules correctly, but you are saying their results are wrong, and now you can’t explain why.

In fact, some of the informal norms that are in wide use are clearly detrimental. In economics, norms have emerged that certain types of models are better simply because they are “more standard”, such as the dynamic stochastic general equilibrium models that can basically be fit to everything and have never actually usefully predicted anything. In fact, the best ones just predict what we already knew from Keynesian models. But without a formal norm for testing the validity of models, it’s been “DSGE or GTFO”. At present, it is considered “nonstandard” (read: “bad”) not to assume that your agents are either a single unitary “representative agent” or a continuum of infinitely-many agents—modeling the actual fact of finitely-many agents is just not done. Yet it’s hard for me to imagine any formal criterion that wouldn’t at least give you some points for correctly including the fact that there is more than one but less than infinity people in the world (obviously your model could still be bad in other ways).

I don’t know what these new statistical methods would look like. Maybe it’s as simple as formally justifying some of the norms we already use; maybe it’s as complicated as taking a fundamentally new approach to statistical inference. But we have to start somewhere.

Argumentum ab scientia is not argumentum baculo: The difference between authority and expertise

pnrj core principles, critique of neoclassical economics, heuristics and biases, public policy , , , , , , , , , , , , ,

May 7, JDN 2457881

Americans are, on the whole, suspicious of authority. This is a very good thing; it shields us against authoritarianism. But it comes with a major downside, which is a tendency to forget the distinction between authority and expertise.

Argument from authority is an informal fallacy, argumentum baculo. The fact that something was said by the Pope, or the President, or the General Secretary of the UN, doesn’t make it true. (Aside: You’re probably more familiar with the phrase argumentum ad baculum, which is terrible Latin. That would mean “argument toward a stick”, when clearly the intended meaning was “argument by means of a stick”, which is argumentum baculo.)

But argument from expertise, argumentum ab scientia, is something quite different. The world is much too complicated for any one person to know everything about everything, so we have no choice but to specialize our knowledge, each of us becoming an expert in only a few things. So if you are not an expert in a subject, when someone who is an expert in that subject tells you something about that subject, you should probably believe them.

You should especially be prepared to believe them when the entire community of experts is in consensus or near-consensus on a topic. The scientific consensus on climate change is absolutely overwhelming. Is this a reason to believe in climate change? You’re damn right it is. Unless you have years of education and experience in understanding climate models and atmospheric data, you have no basis for challenging the expert consensus on this issue.

This confusion has created a deep current of anti-intellectualism in our culture, as Isaac Asimov famously recognized:

There is a cult of ignorance in the United States, and there always has been. The strain of anti-intellectualism has been a constant thread winding its way through our political and cultural life, nurtured by the false notion that democracy means that “my ignorance is just as good as your knowledge.”

This is also important to understand if you have heterodox views on any scientific topic. The fact that the whole field disagrees with you does not prove that you are wrong—but it does make it quite likely that you are wrong. Cranks often want to compare themselves to Galileo or Einstein, but here’s the thing: Galileo and Einstein didn’t act like cranks. They didn’t expect the scientific community to respect their ideas before they had gathered compelling evidence in their favor.

When behavioral economists found that neoclassical models of human behavior didn’t stand up to scrutiny, did they shout from the rooftops that economics is all a lie? No, they published their research in peer-reviewed journals, and talked with economists about the implications of their results. There may have been times when they felt ignored or disrespected by the mainstream, but they pressed on, because the data was on their side. And ultimately, the mainstream gave in: Daniel Kahneman won the Nobel Prize in Economics.

Experts are not always right, that is true. But they are usually right, and if you think they are wrong you’d better have a good reason to think so. The best reasons are the sort that come about when you yourself have spent the time and effort to become an expert, able to challenge the consensus on its own terms.

Admittedly, that is a very difficult thing to do—and more difficult than it should be. I have seen firsthand how difficult and painful the slow grind toward a PhD can be, and how many obstacles will get thrown in your way, ranging from nepotism and interdepartmental politics, to discrimination against women and minorities, to mismatches of interest between students and faculty, all the way to illness, mental health problems, and the slings and arrows of outrageous fortune in general. If you have particularly heterodox ideas, you may face particularly harsh barriers, and sometimes it behooves you to hold your tongue and toe the lie awhile.

But this is no excuse not to gain expertise. Even if academia itself is not available to you, we live in an age of unprecedented availability of information—it’s not called the Information Age for nothing. A sufficiently talented and dedicated autodidact can challenge the mainstream, if their ideas are truly good enough. (Perhaps the best example of this is the mathematician savant Srinivasa Ramanujan. But he’s… something else. I think he is about as far from the average genius as the average genius is from the average person.) No, that won’t be easy either. But if you are really serious about advancing human understanding rather than just rooting for your political team (read: tribe), you should be prepared to either take up the academic route or attack it as an autodidact from the outside.

In fact, most scientific fields are actually quite good about admitting what they don’t know. A total consensus that turns out to be wrong is actually a very rare phenomenon; much more common is a clash of multiple competing paradigms where one ultimately wins out, or they end up replaced by a totally new paradigm or some sort of synthesis. In almost all cases, the new paradigm wins not because it becomes fashionable or the ancien regime dies out (as Planck cynically claimed) but because overwhelming evidence is observed in its favor, often in the form of explaining some phenomenon that was previously impossible to understand. If your heterodox theory doesn’t do that, then it probably won’t win, because it doesn’t deserve to.

(Right now you might think of challenging me: Does my heterodox theory do that? Does the tribal paradigm explain things that either total selfishness or total altruism cannot? I think it’s pretty obvious that it does. I mean, you are familiar with a little thing called “racism”, aren’t you? There is no explanation for racism in neoclassical economics; to understand it at all you have to just impose it as an arbitrary term on the utility function. But at that point, why not throw in whatever you please? Maybe some people enjoy bashing their heads against walls, and other people take great pleasure in the taste of arsenic. Why would this particular self- (not to mention other-) destroying behavior be universal to all human societies?)

In practice, I think most people who challenge the mainstream consensus aren’t genuinely interested in finding out the truth—certainly not enough to actually go through the work of doing it. It’s a pattern you can see in a wide range of fringe views: Anti-vaxxers, 9/11 truthers, climate denialists, they all think the same way. The mainstream disagrees with my preconceived ideology, therefore the mainstream is some kind of global conspiracy to deceive us. The overwhelming evidence that vaccination is safe and (wildly) cost-effective, 9/11 was indeed perpetrated by Al Qaeda and neither planned nor anticipated by anyone in the US government , and the global climate is being changed by human greenhouse gas emissions—these things simply don’t matter to them, because it was never really about the truth. They knew the answer before they asked the question. Because their identity is wrapped up in that political ideology, they know it couldn’t possibly be otherwise, and no amount of evidence will change their mind.

How do we reach such people? That, I don’t know. I wish I did. But I can say this much: We can stop taking them seriously when they say that the overwhelming scientific consensus against them is just another “appeal to authority”. It’s not. It never was. It’s an argument from expertise—there are people who know this a lot better than you, and they think you’re wrong, so you’re probably wrong.

The many varieties of argument “men”

pnrj core principles, ethics, heuristics and biases , , , , , ,

JDN 2457552

After several long, intense, and very likely controversial posts in a row, I decided to take a break with a post that is short and fun.

You have probably already heard of a “strawman” argument, but I think there are many more “materials” an argument can be made of which would be useful terms to have, so I have proposed a taxonomy of similar argument “men”. Perhaps this will help others in the future to more precisely characterize where arguments have gone wrong and how they should have gone differently.

For examples of each, I’m using a hypothetical argument about the gold standard, based on the actual arguments I refute in my previous post on the subject.

This is an argument actually given by a proponent of the gold standard, upon which my “men” shall be built:

1) A gold standard is key to achieving a period of sustained, 4% real economic growth.

The U.S. dollar was created as a defined weight of gold and silver in 1792. As detailed in the booklet, The 21st Century Gold Standard (available free at http://agoldenage.com), I co-authored with fellow Forbes.com columnist Ralph Benko, a dollar as good as gold endured until 1971 with the relatively brief exceptions of the War of 1812, the Civil War and Reconstruction, and 1933, the year President Franklin Roosevelt suspended dollar/gold convertibility until January 31, 1934 when the dollar/gold link was re-established at $35 an ounce, a 40% devaluation from the prior $20.67 an ounce. Over that entire 179 years, the U.S. economy grew at a 3.9% average annual rate, including all of the panics, wars, industrialization and a myriad other events. During the post World War II Bretton Woods gold standard, the U.S. economy also grew on average 4% a year.

By contrast, during the 40-years since going off gold, U.S. economic growth has averaged an anemic 2.8% a year. The only 40-year periods in which the economic growth was slower were those ending in the Great Depression, from 1930 to 1940.

2) A gold standard reduces the risk of recessions and financial crises.

Critics of the gold standard point out, correctly, that it would prohibit the Federal Reserve from manipulating interest rates and the value of the dollar in hopes of stimulating demand. In fact, the idea that a paper dollar would lead to a more stable economy was one of the key selling points for abandoning the gold standard in 1971.

However, this power has done far more harm than good. Under the paper dollar, recessions have become more severe and financial crises more frequent. During the post World War II gold standard, unemployment averaged less than 5% and never rose above 7% during a calendar year. Since going off gold, unemployment has averaged more than 6%, and has been above 8% now for nearly 3.5 years.

And now, the argument men:

Fallacious (Bad) Argument Men

These argument “men” are harmful and irrational; they are to be avoided, and destroyed wherever they are found. Maybe in some very extreme circumstances they would be justifiable—but only in circumstances where it is justifiable to be dishonest and manipulative. You can use a strawman argument to convince a terrorist to let the hostages go; you can’t use one to convince your uncle not to vote Republican.

Strawman: The familiar fallacy in which instead of trying to address someone else’s argument, you make up your own fake version of that argument which is easier to defeat. The image is of making an effigy of your opponent out of straw and beating on the effigy to avoid confronting the actual opponent.

You can’t possibly think that going to the gold standard would make the financial system perfect! There will still be corrupt bankers, a banking oligopoly, and an unpredictable future. The gold standard would do nothing to remove these deep flaws in the system.

Hitman: An even worse form of the strawman, in which you misrepresent not only your opponent’s argument, but your opponent themselves, using your distortion of their view as an excuse for personal attacks against their character.

Oh, you would favor the gold standard, wouldn’t you? A rich, middle-aged White man, presumably straight and nominally Christian? You have all the privileges in life, so you don’t care if you take away the protections that less-fortunate people depend upon. You don’t care if other people become unemployed, so long as you don’t have to bear inflation reducing the real value of your precious capital assets.

Conman: An argument for your own view which you don’t actually believe, but expect to be easier to explain or more persuasive to this particular audience than the true reasons for your beliefs.

Back when we were on the gold standard, it was the era of “Robber Barons”. Poverty was rampant. If we go back to that system, it will just mean handing over all the hard-earned money of working people to billionaire capitalists.

Vaporman: Not even an argument, just a forceful assertion of your view that takes the place or shape of an argument.

The gold standard is madness! It makes no sense at all! How can you even think of going back to such a ridiculous monetary system?

Honest (Acceptable) Argument Men

These argument “men” are perfectly acceptable, and should be the normal expectation in honest discourse.

Woodman: The actual argument your opponent made, addressed and refuted honestly using sound evidence.

There is very little evidence that going back to the gold standard would in any way improve the stability of the currency or the financial system. While long-run inflation was very low under the gold standard, this fact obscures the volatility of inflation, which was extremely high; bouts of inflation were followed by bouts of deflation, swinging the value of the dollar up or down as much as 15% in a single year. Nor is there any evidence that the gold standard prevented financial crises, as dozens of financial crises occurred under the gold standard, if anything more often than they have since the full-fiat monetary system established in 1971.

Bananaman: An actual argument your opponent made that you honestly refute, which nonetheless is so ridiculous that it seems like a strawman, even though it isn’t. Named in “honor” of Ray Comfort’s Banana Argument. Of course, some bananas are squishier than others, and the only one I could find here was at least relatively woody–though still recognizable as a banana:

You said “A gold standard is key to achieving a period of sustained, 4% real economic growth.” based on several distorted, misunderstood, or outright false historical examples. The 4% annual growth in total GDP during the early part of the United States was due primarily to population growth, not a rise in real standard of living, while the rapid growth during WW2 was obviously due to the enormous and unprecedented surge in government spending (and by the way, we weren’t even really on the gold standard during that period). In a blatant No True Scotsman fallacy, you specifically exclude the Great Depression from the “true gold standard” so that you don’t have to admit that the gold standard contributed significantly to the severity of the depression.

Middleman: An argument that synthesizes your view and your opponent’s view, in an attempt to find a compromise position that may be acceptable, if not preferred, by all.

Unlike the classical gold standard, the Bretton Woods gold standard in place from 1945 to 1971 was not obviously disastrous. If you want to go back to a system of international exchange rates fixed by gold similar to Bretton Woods, I would consider that a reasonable position to take.

Virtuous (Good) Argument Men

These argument “men” go above and beyond the call of duty; rather than simply seek to win arguments honestly, they actively seek the truth behind the veil of opposing arguments. These cannot be expected in all circumstances, but they are to be aspired to, and commended when found.

Ironman: Your opponent’s actual argument, but improved, with some of its flaws shored up. The same basic thinking as your opponent, but done more carefully, filling in the proper gaps.

The gold standard might not reduce short-run inflation, but it would reduce long–run inflation, making our currency more stable over long periods of time. We would be able to track long-term price trends in goods such as housing and technology much more easily, and people would have an easier time psychologically grasping the real prices of goods as they change during their lifetime. No longer would we hear people complain, “How can you want a minimum wage of $15? As a teenager in 1955, I got paid $3 an hour and I was happy with that!” when that $3 in 1955, adjusted for inflation, is $26.78 in today’s money.

Steelman: Not the argument your opponent made, but the one they should have made. The best possible argument you are aware of that would militate in favor of their view, the one that sometimes gives you pause about your own opinions, the real and tangible downside of what you believe in.

Tying currency to gold or any other commodity may not be very useful directly, but it could serve one potentially vital function, which is as a commitment mechanism to prevent the central bank from manipulating the currency to enrich themselves or special interests. It may not be the optimal commitment mechanism, but it is a psychologically appealing one for many people, and is also relatively easy to define and keep track of. It is also not subject to as much manipulation as something like nominal GDP targeting or a Taylor Rule, which could be fudged by corrupt statisticians. And while it might cause moderate volatility, it can also protect against the most extreme forms of volatility such as hyperinflation. In countries with very corrupt governments, a gold standard might actually be a good idea, if you could actually enforce it, because it would at least limit the damage that can be done by corrupt central bank officials. Had such a system been in place in Zimbabwe in the 1990s, the hyperinflation might have been prevented. The US is not nearly as corrupt as Zimbabwe, so we probably do not need a gold standard; but it may be wise to recommend the use of gold standards or similar fixed-exchange currencies in Third World countries so that corrupt leaders cannot abuse the monetary system to gain at the expense of their people.

What does correlation have to do with causation?

pnrj core principles, statistics , , , , , , , , , , , ,

JDN 2457345

I’ve been thinking of expanding the topics of this blog into some basic statistics and econometrics. It has been said that there are “Lies, damn lies, and statistics”; but in fact it’s almost the opposite—there are truths, whole truths, and statistics. Almost everything in the world that we know—not merely guess, or suppose, or intuit, or believe, but actually know, with a quantifiable level of certainty—is done by means of statistics. All sciences are based on them, from physics (when they say the Higgs discovery is a “5-sigma event”, that’s a statistic) to psychology, ecology to economics. Far from being something we cannot trust, they are in a sense the only thing we can trust.

The reason it sometimes feels like we cannot trust statistics is that most people do not understand statistics very well; this creates opportunities for both accidental confusion and willful distortion. My hope is therefore to provide you with some of the basic statistical knowledge you need to combat the worst distortions and correct the worst confusions.

I wasn’t quite sure where to start on this quest, but I suppose I have to start somewhere. I figured I may as well start with an adage about statistics that I hear commonly abused: “Correlation does not imply causation.”

Taken at its original meaning, this is definitely true. Unfortunately, it can be easily abused or misunderstood.

In its original meaning, the formal sense of the word “imply” meaning logical implication, to “imply” something is an extremely strong statement. It means that you logically entail that result, that if the antecedent is true, the consequent must be true, on pain of logical contradiction. Logical implication is for most practical purposes synonymous with mathematical proof. (Unfortunately, it’s not quite synonymous, because of things like Gödel’s incompleteness theorems and Löb’s theorem.)

And indeed, correlation does not logically entail causation; it’s quite possible to have correlations without any causal connection whatsoever, simply by chance. One of my former professors liked to brag that from 1990 to 2010 whether or not she ate breakfast had a statistically significant positive correlation with that day’s closing price for the Dow Jones Industrial Average.

How is this possible? Did my professor actually somehow influence the stock market by eating breakfast? Of course not; if she could do that, she’d be a billionaire by now. And obviously the Dow’s price at 17:00 couldn’t influence whether she ate breakfast at 09:00. Could there be some common cause driving both of them, like the weather? I guess it’s possible; maybe in good weather she gets up earlier and people are in better moods so they buy more stocks. But the most likely reason for this correlation is much simpler than that: She tried a whole bunch of different combinations until she found two things that correlated. At the usual significance level of 0.05, on average you need to try about 20 combinations of totally unrelated things before two of them will show up as correlated. (My guess is she used a number of different stock indexes and varied the starting and ending year. That’s a way to generate a surprisingly large number of degrees of freedom without it seeming like you’re doing anything particularly nefarious.)

But how do we know they aren’t actually causally related? Well, I suppose we don’t. Especially if the universe is ultimately deterministic and nonlocal (as I’ve become increasingly convinced by the results of recent quantum experiments), any two data sets could be causally related somehow. But the point is they don’t have to be; you can pick any randomly-generated datasets, pair them up in 20 different ways, and odds are, one of those ways will show a statistically significant correlation.

All of that is true, and important to understand. Finding a correlation between eating grapefruit and getting breast cancer, or between liking bitter foods and being a psychopath, does not necessarily mean that there is any real causal link between the two. If we can replicate these results in a bunch of other studies, that would suggest that the link is real; but typically, such findings cannot be replicated. There is something deeply wrong with the way science journalists operate; they like to publish the new and exciting findings, which 9 times out of 10 turn out to be completely wrong. They never want to talk about the really important and fascinating things that we know are true because we’ve been confirming them over hundreds of different experiments, because that’s “old news”. The journalistic desire to be new and first fundamentally contradicts the scientific requirement of being replicated and confirmed.

So, yes, it’s quite possible to have a correlation that tells you absolutely nothing about causation.

But this is exceptional. In most cases, correlation actually tells you quite a bit about causation.

And this is why I don’t like the adage; “imply” has a very different meaning in common speech, meaning merely to suggest or evoke. Almost everything you say implies all sorts of things in this broader sense, some more strongly than others, even though it may logically entail none of them.

Correlation does in fact suggest causation. Like any suggestion, it can be overridden. If we know that 20 different combinations were tried until one finally yielded a correlation, we have reason to distrust that correlation. If we find a correlation between A and B but there is no logical way they can be connected, we infer that it is simply an odd coincidence.

But when we encounter any given correlation, there are three other scenarios which are far more likely than mere coincidence: A causes B, B causes A, or some other factor C causes A and B. These are also not mutually exclusive; they can all be true to some extent, and in many cases are.

A great deal of work in science, and particularly in economics, is based upon using correlation to infer causation, and has to be—because there is simply no alternative means of approaching the problem.

Yes, sometimes you can do randomized controlled experiments, and some really important new findings in behavioral economics and development economics have been made this way. Indeed, much of the work that I hope to do over the course of my career is based on randomized controlled experiments, because they truly are the foundation of scientific knowledge. But sometimes, that’s just not an option.

Let’s consider an example: In my master’s thesis I found a strong correlation between the level of corruption in a country (as estimated by the World Bank) and the proportion of that country’s income which goes to the top 0.01% of the population. Countries that have higher levels of corruption also tend to have a larger proportion of income that accrues to the top 0.01%. That correlation is a fact; it’s there. There’s no denying it. But where does it come from? That’s the real question.

Could it be pure coincidence? Well, maybe; but when it keeps showing up in several different models with different variables included, that becomes unlikely. A single p < 0.05 will happen about 1 in 20 times by chance; but five in a row should happen less than 1 in 1 million times (assuming they’re independent, which, to be fair, they usually aren’t).

Could it be some artifact of the measurement methods? It’s possible. In particular, I was concerned about the possibility of Halo Effect, in which people tend to assume that something which is better (or worse) in one way is automatically better (or worse) in other ways as well. People might think of their country as more corrupt simply because it has higher inequality, even if there is no real connection. But it would have taken a very large halo bias to explain this effect.

So, does corruption cause income inequality? It’s not hard to see how that might happen: More corrupt individuals could bribe leaders or exploit loopholes to make themselves extremely rich, and thereby increase inequality.

Does inequality cause corruption? This also makes some sense, since it’s a lot easier to bribe leaders and manipulate regulations when you have a lot of money to work with in the first place.

Does something else cause both corruption and inequality? Also quite plausible. Maybe some general cultural factors are involved, or certain economic policies lead to both corruption and inequality. I did try to control for such things, but I obviously couldn’t include all possible variables.

So, which way does the causation run? Unfortunately, I don’t know. I tried some clever statistical techniques to try to figure this out; in particular, I looked at which tends to come first—the corruption or the inequality—and whether they could be used to predict each other, a method called Granger causality. Those results were inconclusive, however. I could neither verify nor exclude a causal connection in either direction. But is there a causal connection? I think so. It’s too robust to just be coincidence. I simply don’t know whether A causes B, B causes A, or C causes A and B.

Imagine trying to do this same study as a randomized controlled experiment. Are we supposed to create two societies and flip a coin to decide which one we make more corrupt? Or which one we give more income inequality? Perhaps you could do some sort of experiment with a proxy for corruption (cheating on a test or something like that), and then have unequal payoffs in the experiment—but that is very far removed from how corruption actually works in the real world, and worse, it’s prohibitively expensive to make really life-altering income inequality within an experimental context. Sure, we can give one participant $1 and the other $1,000; but we can’t give one participant $10,000 and the other $10 million, and it’s the latter that we’re really talking about when we deal with real-world income inequality. I’m not opposed to doing such an experiment, but it can only tell us so much. At some point you need to actually test the validity of your theory in the real world, and for that we need to use statistical correlations.

Or think about macroeconomics; how exactly are you supposed to test a theory of the business cycle experimentally? I guess theoretically you could subject an entire country to a new monetary policy selected at random, but the consequences of being put into the wrong experimental group would be disastrous. Moreover, nobody is going to accept a random monetary policy democratically, so you’d have to introduce it against the will of the population, by some sort of tyranny or at least technocracy. Even if this is theoretically possible, it’s mind-bogglingly unethical.

Now, you might be thinking: But we do change real-world policies, right? Couldn’t we use those changes as a sort of “experiment”? Yes, absolutely; that’s called a quasi-experiment or a natural experiment. They are tremendously useful. But since they are not truly randomized, they aren’t quite experiments. Ultimately, everything you get out of a quasi-experiment is based on statistical correlations.

Thus, abuse of the adage “Correlation does not imply causation” can lead to ignoring whole subfields of science, because there is no realistic way of running experiments in those subfields. Sometimes, statistics are all we have to work with.

This is why I like to say it a little differently:

Correlation does not prove causation. But correlation definitely can suggest causation.

Love is rational

pnrj cognitive science , , , , , , , , , , , , , , , , , , ,

JDN 2457066 PST 15:29.

Since I am writing this the weekend of Valentine’s Day (actually by the time it is published it will be Valentine’s Day) and sitting across from my boyfriend, it seems particularly appropriate that today’s topic should be love. As I am writing it is in fact Darwin Day, so it is fitting that evolution will be a major topic as well.

Usually we cognitive economists are the ones reminding neoclassical economists that human beings are not always rational. Today however I must correct a misconception in the opposite direction: Love is rational, or at least it can be, should be, and typically is.

Lately I’ve been reading The Logic of Life which actually makes much the same point, about love and many other things. I had expected it to be a dogmatic defense of economic rationality—published in 2008 no less, which would make it the scream of a dying paradigm as it carries us all down with it—but I was in fact quite pleasantly surprised. The book takes a nuanced position on rationality very similar to my own, and actually incorporates many of the insights from neuroeconomics and cognitive economics. I think Harford would basically agree with me that human beings are 90% rational (but woe betide the other 10%).

We have this romantic (Romantic?) notion in our society that love is not rational, it is “beyond” rationality somehow. “Love is blind”, they say; and this is often used as a smug reply to the notion that rationality is the proper guide to live our lives.

The argument would seem to follow: “Love is not rational, love is good, therefore rationality is not always good.”

But then… the argument would follow? What do you mean, follow? Follow logically? Follow rationally? Something is clearly wrong if we’ve constructed a rational argument intended to show that we should not live our lives by rational arguments.

And the problem of course is the premise that love is not rational. Whatever made you say that?

It’s true that love is not directly volitional, not in the way that it is volitional to move your arm upward or close your eyes or type the sentence “Jackdaws ate my big sphinx of quartz.” You don’t exactly choose to love someone, weighing the pros and cons and making a decision the way you might choose which job offer to take or which university to attend.

But then, you don’t really choose which university you like either, now do you? You choose which to attend. But your enjoyment of that university is not a voluntary act. And similarly you do in fact choose whom to date, whom to marry. And you might well consider the pros and cons of such decisions. So the difference is not as large as it might at first seem.

More importantly, to say that our lives should be rational is not the same as saying they should be volitional. You simply can’t live your life as completely volitional, no matter how hard you try. You simply don’t have the cognitive resources to maintain constant awareness of every breath, every heartbeat. Yet there is nothing irrational about breathing or heartbeats—indeed they are necessary for survival and thus a precondition of anything rational you might ever do.

Indeed, in many ways it is our subconscious that is the most intelligent part of us. It is not as flexible as our conscious mind—that is why our conscious mind is there—but the human subconscious is unmatched in its efficiency and reliability among literally all known computational systems in the known universe. Walk across a room and it will solve reverse kinematics in real time. Throw a ball and it will solve three-dimensional nonlinear differential equations as well. Look at a familiar face and it will immediately identify it among a set of hundreds of faces with near-perfect accuracy regardless of the angle, lighting conditions, or even hairstyle. To see that I am not exaggerating the immense difficulty of these tasks, look at how difficult it is to make robots that can walk on two legs or throw balls. Face recognition is so difficult that it is still an unsolved problem with an extensive body of ongoing research.

And love, of course, is the subconscious system that has been most directly optimized by natural selection. Our very survival has depended upon it for millions of years. Indeed, it’s amazing how often it does seem to fail given those tight optimization constraints; I think this is for two reasons. First, natural selection optimizes for inclusive fitness, which is not the same thing as optimizing for happiness—what’s good for your genes may not be good for you per se. Many of the ways that love hurts us seem to be based around behaviors that probably did on average spread more genes on the African savannah. Second, the task of selecting an optimal partner is so mind-bogglingly complex that even the most powerful computational system in the known universe still can only do it so well. Imagine trying to construct a formal decision model that would tell you whom you should marry—all the variables you’d need to consider, the cost of sampling each of those variables sufficiently, the proper weightings on all the different terms in the utility function. Perhaps the wonder is that love is as rational as it is.

Indeed, love is evidence-based—and when it isn’t, this is cause for concern. The evidence is most often presented in small ways over long periods of time—a glance, a kiss, a gift, a meeting canceled to stay home and comfort you. Some ways are larger—a career move postponed to keep the family together, a beautiful wedding, a new house. We aren’t formally calculating the Bayesian probability at each new piece of evidence—though our subconscious brains might be, and whatever they’re doing the results aren’t far off from that mathematical optimum.

The notion that you will never “truly know” if others love you is no more epistemically valid or interesting than the notion that you will never “truly know” if your shirt is grue instead of green or if you are a brain in a vat. Perhaps we’ve been wrong about gravity all these years, and on April 27, 2016 it will suddenly reverse direction! No, it won’t, and I’m prepared to literally bet the whole world on that (frankly I’m not sure I have a choice). To be fair, the proposition that your spouse of twenty years or your mother loves you is perhaps not that certain—but it’s pretty darn certain. Perhaps the proper comparison is the level of certainty that climate change is caused by human beings, or even less, the level of certainty that your car will not suddenly veer off the road and kill you. The latter is something that actually happens—but we all drive every day assuming it won’t. By the time you marry someone, you can and should be that certain that they love you.

Love without evidence is bad love. The sort of unrequited love that builds in secret based upon fleeing glimpses, hours of obsessive fantasy, and little or no interaction with its subject isn’t romantic—it’s creepy and psychologically unhealthy. The extreme of that sort of love is what drove John Hinckley Jr. to shoot Ronald Reagan in order to impress Jodie Foster.

I don’t mean to make you feel guilty if you have experienced such a love—most of us have at one point or another—but it disgusts me how much our society tries to elevate that sort of love as the “true love” to which we should all aspire. We encourage people—particularly teenagers—to conceal their feelings for a long time and then release them in one grand surprise gesture of affection, which is just about the opposite of what you should actually be doing. (Look at Love Actually, which is just about the opposite of what its title says.) I think a great deal of strife in our society would be eliminated if we taught our children how to build relationships gradually over time instead of constantly presenting them with absurd caricatures of love that no one can—or should—follow.

I am pleased to see that our cultural norms on that point seem to be changing. A corporation as absurdly powerful as Disney is both an influence upon and a barometer of our social norms, and the trope in the most recent Disney films (like Frozen and Maleficent) is that true love is not the fiery passion of love at first sight, but the deep bond between family members that builds over time. This is a much healthier concept of love, though I wouldn’t exclude romantic love entirely. Romantic love can be true love, but only by building over time through a similar process.

Perhaps there is another reason people are uncomfortable with the idea that love is rational; by definition, rational behaviors respond to incentives. And since we tend to conceive of incentives as a purely selfish endeavor, this would seem to imply that love is selfish, which seems somewhere between painfully cynical and outright oxymoronic.

But while love certainly does carry many benefits for its users—being in love will literally make you live longer, by quite a lot, an effect size comparable to quitting smoking or exercising twice a week—it also carries many benefits for its recipients as well. Love is in fact the primary means by which evolution has shaped us toward altruism; it is the love for our family and our tribe that makes us willing to sacrifice so much for them. Not all incentives are selfish; indeed, an incentive is really just something that motivates you to action. If you could truly convince me that a given action I took would have even a reasonable chance of ending world hunger, I would do almost anything to achieve it; I can scarcely imagine a greater incentive, even though I would be harmed and the benefits would incur to people I have never met.

Love evolved because it advanced the fitness of our genes, of course. And this bothers many people; it seems to make our altruism ultimately just a different form of selfishness I guess, selfishness for our genes instead of ourselves. But this is a genetic fallacy, isn’t it? Yes, evolution by natural selection is a violent process, full of death and cruelty and suffering (as Darwin said, red in tooth and claw); but that doesn’t mean that its outcome—namely ourselves—is so irredeemable. We are, in fact, altruistic, regardless of where that altruism came from. The fact that it advanced our genes can actually be comforting in a way, because it reminds us that the universe is nonzero-sum and benefiting others does not have to mean harming ourselves.

One question I like to ask when people suggest that some scientific fact undermines our moral status in this way is: “Well, what would you prefer?” If the causal determinism of neural synapses undermines our free will, then what should we have been made of? Magical fairy dust? If we were, fairy dust would be a real phenomenon, and it would obey laws of nature, and you’d just say that the causal determinism of magical fairy dust undermines free will all over again. If the fact that our altruistic emotions evolved by natural selection to advance our inclusive fitness makes us not truly altruistic, then where should have altruism come from? A divine creator who made us to love one another? But then we’re just following our programming! You can always make this sort of argument, which either means that live is necessarily empty of meaning, that no possible universe could ever assuage our ennui—or, what I believe, that life’s meaning does not come from such ultimate causes. It is not what you are made of or where you come from that defines what you are. We are best defined by what we do.

It seems to depend how you look at it: Romantics are made of stardust and the fabric of the cosmos, while cynics are made of the nuclear waste expelled in the planet-destroying explosions of dying balls of fire. Romantics are the cousins of all living things in one grand family, while cynics are apex predators evolved from millions of years of rape and murder. Both of these views are in some sense correct—but I think the real mistake is in thinking that they are incompatible. Human beings are both those things, and more; we are capable of both great compassion and great cruelty—and also great indifference. It is a mistake to think that only the dark sides—or for that matter only the light sides—of us are truly real.

Love is rational; love responds to incentives; love is an evolutionary adaptation. Love binds us together; love makes us better; love leads us to sacrifice for one another.

Love is, above all, what makes us not infinite identical psychopaths.