science

What does correlation have to do with causation?

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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.

Why being a scientist means confronting your own ignorance

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I read an essay today arguing that scientists should be stupid. Or more precisely, ignorant. Or even more precisely, they should recognize their ignorance when all others ignore and turn away.

What does it feel like to be wrong?

It doesn’t feel like anything. Most people are wrong most of the time without realizing it. (Explained brilliantly in this TED talk.)

What does it feel like to be proven wrong, to find out you were confused or ignorant?

It hurts, a great deal. And most people flinch away from this. They would rather continue being wrong than experience the feeling of being proven wrong.

But being proven wrong is the only way to become less wrong. Being proven ignorant is the only way to truly attain knowledge.

I once heard someone characterize the scientific temperament as “being comfortable not knowing”. No, no, no! Just the opposite, in fact. The unscientific temperament is being comfortable not knowing, being fine with your infinite ignorance as long as you can go about your day. The scientific temperament is being so deeply  uncomfortable not knowing that it overrides the discomfort everyone feels when their beliefs are proven wrong. It is to have a drive to actually know—not to think you know, not to feel as if you know, not to assume you know and never think about it, but to actually know—that is so strong it pushes you through all the pain and doubt and confusion of actually trying to find out.

An analogy I like to use is The Armor of Truth. Suppose you were presented with a piece of armor, The Armor of Truth, which is claimed to be indestructible. You will have the chance to wear this armor into battle; if it is indeed indestructible, you will be invincible and will surely prevail. But what if it isn’t? What if it has some weakness you aren’t aware of? Then it could fail and you could die.

How would you go about determining whether The Armor of Truth is really what it is claimed to be? Would you test it with things you expect it to survive? Would you brush it with feathers, pour glasses of water on it, poke it with your finger? Would you seek to confirm your belief in its indestructibility? (As confirmation bias would have you do?) No, you would test it with things you expect to destroy it; you’d hit it with everything you have. You’re fire machine guns at it, drop bombs on it, pour acid on it, place it in a nuclear testing site. You’d do everything you possibly could to falsify your belief in the armor’s indestructibility. And only when you failed, only after you had tried everything you could think of to destroy the armor and it remained undented and unscratched, would you begin to believe that it is truly indestructible. (Popper was exaggerating when he said all science is based on falsification; but he was not exaggerating very much.)

Science is The Armor of Truth, and we wear it into battle—but now the analogy begins to break down, for our beliefs are within us, they are part of us. We’d like to be able to point the machineguns at armor far away from us, but instead it is as if we are forced to wear the armor as the guns are fired. When a break in the armor is found and a bullet passes through—a belief we dearly held is proven false—it hurts us, and we wish we could find another way to test it. But we can’t; and if we fail to test it now, it will only endanger us later—confront a false belief with reality enough and it will eventually fail. A scientist is someone who accepts this and wears the armor bravely as the test guns blaze.

Being a scientist means confronting your own ignorance: Not accepting it, but also not ignoring it; confronting it. Facing it down. Conquering it. Destroying it.

What’s wrong with academic publishing?

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JDN 2457257 EDT 14:23.

I just finished expanding my master’s thesis into a research paper that is, I hope, suitable for publication in an economics journal. As part of this process I’ve been looking into the process of submitting articles for publication in academic journals… and I’ve found has been disgusting and horrifying. It is astonishingly bad, and my biggest question is why researchers put up with it.

Thus, the subject of this post is what’s wrong with the system—and what we might do instead.

Before I get into it, let me say that I don’t actually disagree with “publish or perish” in principle—as SMBC points out, it’s a lot like “do your job or get fired”. Researchers should publish in peer-reviewed journals; that’s a big part of what doing research means. The problem is how most peer-reviewed journals are currently operated.

First of all, in case you didn’t know, most scientific journals are owned by for-profit corporations. The largest corporation Elsevier, owns The Lancet and all of ScienceDirect, and has net income of over 1 billion Euros a year. Then there’s Springer and Wiley-Blackwell; between the three of them, these publishers account for over 40% of all scientific publications. These for-profit publishers retain the full copyright to most of the papers they publish, and tightly control access with paywalls; the cost to get through these paywalls is generally thousands of dollars a year for individuals and millions of dollars a year for universities. Their monopoly power is so great it “makes Rupert Murdoch look like a socialist.”

For-profit journals do often offer an “open-access” option in which you basically buy back your own copyright, but the price is high—the most common I’ve seen are $1800 or $3000 per paper—and very few researchers do this, for obvious financial reasons. In fact I think for a full-time tenured faculty researcher it’s probably worth it, given the alternatives. (Then again, full-time tenured faculty are becoming an endangered species lately; what might be worth it in the long run can still be very difficult for a cash-strapped adjunct to afford.) Open-access means people can actually read your paper and potentially cite your paper. Closed-access means it may languish in obscurity.

And of course it isn’t just about the benefits for the individual researcher. The scientific community as a whole depends upon the free flow of information; the reason we publish in the first place is that we want people to read papers, discuss them, replicate them, challenge them. Publication isn’t the finish line; it’s at best a checkpoint. Actually one thing that does seem to be wrong with “publish or perish” is that there is so much pressure for publication that we publish too many pointless papers and nobody has time to read the genuinely important ones.

These prices might be justifiable if the for-profit corporations actually did anything. But in fact they are basically just aggregators. They don’t do the peer-review, they farm it out to other academic researchers. They don’t even pay those other researchers; they just expect them to do it. (And they do! Like I said, why do they put up with this?) They don’t pay the authors who have their work published (on the contrary, they often charge submission fees—about $100 seems to be typical—simply to look at them). It’s been called “the world’s worst restaurant”, where you pay to get in, bring your own ingredients and recipes, cook your own food, serve other people’s food while they serve yours, and then have to pay again if you actually want to be allowed to eat.

They pay for the printing of paper copies of the journal, which basically no one reads; and they pay for the electronic servers that host the digital copies that everyone actually reads. They also provide some basic copyediting services (copyediting APA style is a job people advertise on Craigslist—so you can guess how much they must be paying).

And even supposing that they actually provided some valuable and expensive service, the fact would remain that we are making for-profit corporations the gatekeepers of the scientific community. Entities that exist only to make money for their owners are given direct control over the future of human knowledge. If you look at Cracked’s “reasons why we can’t trust science anymore”, all of them have to do with the for-profit publishing system. p-hacking might still happen in a better system, but publishers that really had the best interests of science in mind would be more motivated to fight it than publishers that are simply trying to raise revenue by getting people to buy access to their papers.

Then there’s the fact that most journals do not allow authors to submit to multiple journals at once, yet take 30 to 90 days to respond and only publish a fraction of what is submitted—it’s almost impossible to find good figures on acceptance rates (which is itself a major problem!), but the highest figures I’ve seen are 30% acceptance, a more typical figure seems to be 10%, and some top journals go as low as 3%. In the worst-case scenario you are locked into a journal for 90 days with only a 3% chance of it actually publishing your work. At that rate publishing an article could take years.

Is open-access the solution? Yes… well, part of it, anyway.

There are a large number of open-access journals, some of which do not charge submission fees, but very few of them are prestigious, and many are outright predatory. Predatory journals charge exorbitant fees, often after accepting papers for publication; many do little or no real peer review. There are almost seven hundred known predatory open-access journals; over one hundred have even been caught publishing hoax papers. These predatory journals are corrupting the process of science.

There are a few reputable open-access journals, such as BMC Biology and PLOSOne. Though not actually a journal, ArXiv serves a similar role. These will be part of the solution, most definitely. Yet even legitimate open-access journals often charge each author over $1000 to publish an article. There is a small but significant positive correlation between publication fees and journal impact factor.

We need to found more open-access journals which are funded by either governments or universities, so that neither author nor reader ever pays a cent. Science is a public good and should be funded as such. Even if copyright makes sense for other forms of content (I’m not so sure about that), it most certainly does not make sense for scientific knowledge, which by its very nature is only doing its job if it is shared with the world.

These journals should be specifically structured to be method-sensitive but results-blind. (It’s a very good thing that medical trials are usually registered before they are completed, so that publication is assured even if the results are negative—the same should be done with other sciences. Unfortunately, even in medicine there is significant publication bias.) If you could sum up the scientific method in one phrase, it might just be that: Method-sensitive but results-blind. If you think you know what you’re going to find beforehand, you may not be doing science. If you are certain what you’re going to find beforehand, you’re definitely not doing science.

The process should still be highly selective, but it should be possible—indeed, expected—to submit to multiple journals at once. If journals want to start paying their authors to entice them to publish in that journal rather than take another offer, that’s fine with me. Researchers are the ones who produce the content; if anyone is getting paid for it, it should be us.

This is not some wild and fanciful idea; it’s already the way that book publishing works. Very few literary agents or book publishers would ever have the audacity to say you can’t submit your work elsewhere; those that try are rapidly outcompeted as authors stop submitting to them. It’s fundamentally unreasonable to expect anyone to hang all their hopes on a particular buyer months in advance—and that is what you are, publishers, you are buyers. You are not sellers, you did not create this content.

But new journals face a fundamental problem: Good researchers will naturally want to publish in journals that are prestigious—that is, journals that are already prestigious. When all of the prestige is in journals that are closed-access and owned by for-profit companies, the best research goes there, and the prestige becomes self-reinforcing. Journals are prestigious because they are prestigious; welcome to tautology club.

Somehow we need to get good researchers to start boycotting for-profit journals and start investing in high-quality open-access journals. If Elsevier and Springer can’t get good researchers to submit to them, they’ll change their ways or wither and die. Research should be funded and published by governments and nonprofit institutions, not by for-profit corporations.

This may in fact highlight a much deeper problem in academia, the very concept of “prestige”. I have no doubt that Harvard is a good university, better university than most; but is it actually the best as it is in most people’s minds? Might Stanford or UC Berkeley be better, or University College London, or even the University of Michigan? How would we tell? Are the students better? Even if they are, might that just be because all the better students went to the schools that had better reputations? Controlling for the quality of the student, more prestigious universities are almost uncorrelated with better outcomes. Those who get accepted to Ivies but attend other schools do just as well in life as those who actually attend Ivies. (Good news for me, getting into Columbia but going to Michigan.) Yet once a university acquires such a high reputation, it can be very difficult for it to lose that reputation, and even more difficult for others to catch up.

Prestige is inherently zero-sum; for me to get more prestige you must lose some. For one university or research journal to rise in rankings, another must fall. Aside from simply feeding on other prestige, the prestige of a university is largely based upon the students it rejects—its “selectivity” score. What does it say about our society that we value educational institutions based upon the number of people they exclude?

Zero-sum ranking is always easier to do than nonzero-sum absolute scoring. Actually that’s a mathematical theorem, and one of the few good arguments against range voting (still not nearly good enough, in my opinion); if you have a list of scores you can always turn them into ranks (potentially with ties); but from a list of ranks there is no way to turn them back into scores.

Yet ultimately it is absolute scores that must drive humanity’s progress. If life were simply a matter of ranking, then progress would be by definition impossible. No matter what we do, there will always be top-ranked and bottom-ranked people.

There is simply no way mathematically for more than 1% of human beings to be in the top 1% of the income distribution. (If you’re curious where exactly that lies today, I highly recommend this interactive chart by the New York Times.) But we could raise the standard of living for the majority of people to a level that only the top 1% once had—and in fact, within the First World we have already done this. We could in fact raise the standard of living for everyone in the First World to a level that only the top 1%—or less—had as recently as the 16th century, by the simple change of implementing a basic income.

There is no way for more than 0.14% of people to have an IQ above 145, because IQ is defined to have a mean of 100 and a standard deviation of 15, regardless of how intelligent people are. People could get dramatically smarter over time—and in fact have—and yet it would still be the case that by definition, only 0.14% can be above 145.

Similarly, there is no way for much more than 1% of people to go to the top 1% of colleges. There is no way for more than 1% of people to be in the highest 1% of their class. But we could increase the number of college degrees (which we have); we could dramatically increase literacy rates (which we have).

We need to find a way to think of science in the same way. I wouldn’t suggest simply using number of papers published or even number of drugs invented; both of those are skyrocketing, but I can’t say that most of the increase is actually meaningful. I don’t have a good idea of what an absolute scale for scientific quality would look like, even at an aggregate level; and it is likely to be much harder still to make one that applies on an individual level.

But I think that ultimately this is the only way, the only escape from the darkness of cutthroat competition. We must stop thinking in terms of zero-sum rankings and start thinking in terms of nonzero-sum absolute scales.

Beware the false balance

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JDN 2457046 PST 13:47.

I am now back in Long Beach, hence the return to Pacific Time. Today’s post is a little less economic than most, though it’s certainly still within the purview of social science and public policy. It concerns a question that many academic researchers and in general reasonable, thoughtful people have to deal with: How do we remain unbiased and nonpartisan?

This would not be so difficult if the world were as the most devoted “centrists” would have you believe, and it were actually the case that both sides have their good points and bad points, and both sides have their scandals, and both sides make mistakes or even lie, so you should never take the side of the Democrats or the Republicans but always present both views equally.

Sadly, this is not at all the world in which we live. While Democrats are far from perfect—they are human beings after all, not to mention politicians—Republicans have become completely detached from reality. As Stephen Colbert has said, “Reality has a liberal bias.” You know it’s bad when our detractors call us the reality-based community. Treating both sides as equal isn’t being unbiased—it’s committing a balance fallacy.

Don’t believe me? Here is a list of objective, scientific facts that the Republican Party (and particularly its craziest subset, the Tea Party) has officially taken political stances against:

  1. Global warming is a real problem, and largely caused by human activity. (The Republican majority in the Senate voted down a resolution acknowledging this.)
  2. Human beings share a common ancestor with chimpanzees. (48% of Republicans think that we were created in our present form.)
  3. Animals evolve over time due to natural selection. (Only 43% of Republicans believe this.)
  4. The Earth is approximately 4.5 billion years old. (Marco Rubio said he thinks maybe the Earth was made in seven days a few thousand years ago.)
  5. Hydraulic fracturing can trigger earthquakes.(Republican in Congress are trying to nullify local regulations on fracking because they insist it is so safe we don’t even need to keep track.)
  6. Income inequality in the United States is the worst it has been in decades and continues to rise. (Mitt Romney said that the concern about income inequality is just “envy”.)
  7. Progressive taxation reduces inequality without adversely affecting economic growth. (Here’s a Republican former New York Senator saying that the President “should be ashamed” for raising taxes on—you guessed it—”job creators”.)
  8. Moderate increases in the minimum wage do not yield significant losses in employment. (Republicans consistently vote against even small increases in the minimum wage, and Democrats consistently vote in favor.)
  9. The United States government has no reason to ever default on its debt. (John Boehner, now Speaker of the House, once said that “America is broke” and if we don’t stop spending we’ll never be able to pay the national debt.)
  10. Human embryos are not in any way sentient, and fetuses are not sentient until at least 17 weeks of gestation, probably more like 30 weeks. (Yet if I am to read it in a way that would make moral sense, “Life begins at conception”—which several Republicans explicitly endorsed at the National Right to Life Convention—would have to imply that even zygotes are sentient beings. If you really just meant “alive”, then that would equally well apply to plants or even bacteria. Sentience is the morally relevant category.)

And that’s not even counting the Republican Party’s association with Christianity and all of the objectively wrong scientific claims that necessarily entails—like the existence of an afterlife and the intervention of supernatural forces. Most Democrats also self-identify as Christian, though rarely with quite the same fervor (the last major Democrat I can think of who was a devout Christian was Jimmy Carter), probably because most Americans self-identify as Christian and are hesitant to elect an atheist President (despite the fact that 93% of the National Academy of Sciences is comprised of atheists and the higher your IQ the more likely you are to be an atheist; we wouldn’t want to elect someone who agrees with smart people, now would we?).

It’s true, there are some other crazy ideas out there with a left-wing slant, like the anti-vaccination movement that has wrought epidemic measles upon us, the anti-GMO crowd that rejects basic scientific facts about genetics, and the 9/11 “truth” movement that refuses to believe that Al Qaeda actually caused the attacks. There are in fact far-left Marxists out there who want to tear down the whole capitalist system by glorious revolution and replace it with… er… something (they’re never quite clear on that last point). But none of these things are the official positions of standing members of Congress.

The craziest belief by a standing Democrat I can think of is Dennis Kucinich’s belief that he saw an alien spacecraft. And to be perfectly honest, alien spacecraft are about a thousand times more plausible than Christianity in general, let alone Creationism. There almost certainly are alien spacecraft somewhere in the universe—just most likely so far away we’ll need FTL to encounter them. Moreover, this is not Kucinich’s official position as a member of Congress and it’s not something he has ever made policy based upon.

Indeed, if you’re willing to include the craziest individuals with no real political power who identify with a particular side of the political spectrum, then we should include on the right-wing side people like the Bundy militia in Nevada, neo-Nazis in Detroit, and the dozens of KKK chapters across the US. Not to mention this pastor who wants to murder all gay people in the world (because he truly believes what Leviticus 20:13 actually and clearly says).

If you get to include Marxists on the left, then we get to include Nazis on the right. Or, we could be reasonable and say that only the official positions of elected officials or mainstream pundits actually count, in which case Democrats have views that are basically accurate and reasonable while the majority of Republicans have views that are still completely objectively wrong.

There’s no balance here. For every Democrat who is wrong, there is a Republicans who is totally delusional. For every Democrat who distorts the truth, there is a Republican who blatantly lies about basic facts. Not to mention that for every Democrat who has had an ill-advised illicit affair there is a Republican who has committed war crimes.

Actually war crimes are something a fair number of Democrats have done as well, but the difference still stands out in high relief: Barack Obama has ordered double-tap drone strikes that are in violation of the Geneva Convention, but George W. Bush orchestrated a worldwide mass torture campaign and launched pointless wars that slaughtered hundreds of thousands of people. Bill Clinton ordered some questionable CIA operations, but George H.W. Bush was the director of the CIA.

I wish we had two parties that were equally reasonable. I wish there were two—or three, or four—proposals on the table in each discussion, all of which had merits and flaws worth considering. Maybe if we somehow manage to get the Green Party a significant seat in power, or the Social Democrat party, we can actually achieve that goal. But that is not where we are right now. Right now, we have the Democrats, who have some good ideas and some bad ideas; and then we have the Republicans, who are completely out of their minds.

There is an important concept in political science called the Overton window; it is the range of political ideas that are considered “reasonable” or “mainstream” within a society. Things near the middle of the Overton window are considered sensible, even “nonpartisan” ideas, while things near the edges are “partisan” or “political”, and things near but outside the window are seen as “extreme” and “radical”. Things far outside the window are seen as “absurd” or even “unthinkable”.

Right now, our Overton window is in the wrong place. Things like Paul Ryan’s plan to privatize Social Security and Medicare are seen as reasonable when they should be considered extreme. Progressive income taxes of the kind we had in the 1960s are seen as extreme when they should be considered reasonable. Cutting WIC and SNAP with nothing to replace them and letting people literally starve to death are considered at most partisan, when they should be outright unthinkable. Opposition to basic scientific facts like climate change and evolution is considered a mainstream political position—when in terms of empirical evidence Creationism should be more intellectually embarrassing than being a 9/11 truther or thinking you saw an alien spacecraft. And perhaps worst of all, military tactics like double-tap strikes that are literally war crimes are considered “liberal”, while the “conservative” position involves torture, worldwide surveillance and carpet bombing—if not outright full-scale nuclear devastation.

I want to restore reasonable conversation to our political system, I really do. But that really isn’t possible when half the politicians are totally delusional. We have but one choice: We must vote them out.

I say this particularly to people who say “Why bother? Both parties are the same.” No, they are not the same. They are deeply, deeply different, for all the reasons I just outlined above. And if you can’t bring yourself to vote for a Democrat, at least vote for someone! A Green, or a Social Democrat, or even a Libertarian or a Socialist if you must. It is only by the apathy of reasonable people that this insanity can propagate in the first place.