Here, Let Me Think That Through For You

You know how sometimes people ask a silly question that’s easily checkable with a Google search? Or make a factual claim that is easily correctable with such a search? “Here, let me Google that for you” is a wonderful snarky response to this behavior. Sometimes it’s a technologically incompetent person who really doesn’t know how to find the information they want, and sometimes it’s a technology native who just forgets that he has all the world’s knowledge at his fingertips. Either way, it’s fun to remind people that there’s an easy solution to their problem.

A different version of this problem is the following. Someone blurts out whatever arguments come to mind without really thinking about them, usually done in rapid-fire succession. It may be someone who has very poor impulse control and no filter. Or it might be someone who is losing an argument dead-to-rights and is flailing in panic. Whatever the reason, some people are prone to producing this kind of blather. These interactions can be interesting, as this sort of free-association might by luck hit upon a nugget of truth. And a thoughtful person can benefit from thinking through someone else’s bad ad hoc arguments and dissecting them. It’s like a game of “’Why?’ Boy”, in which a child keeps asking “Why?” to each response in the iteration and you have go deep down to the foundations of your knowledge. You learn something from this process, even if the ‘Why?’ boy doesn’t.

Mostly, though, this is a huge distracting waste of time. I wish that people would think through their arguments a little more clearly before sharing. Don’t tax the patience of your correspondent with half-baked ideas. I realize there is some ambiguity as to whether this is happening or not. (After all, isn’t there another person on the other side of the argument who thinks you’re doing the same thing?) But there are some tell-tale clues. If you present someone with novel, relevant information that they haven’t digested and they have an immediate response, they are blathering. If someone makes a claim of a statistical nature but can’t offer any numbers (or even a hint of numeracy), they are blathering. If someone hasn’t thought through your argument but blurts out the first objection that pops to mind, they are blathering. Beware of people who are too quick on the draw with the “post” button.

Examples help illustrate a point, so I’ll offer some recent ones. The purpose of this isn't to revisit the argument or embarrass anyone, just to focus on something specific. The other day I got several very absurd responses from someone who clearly wasn’t thinking. It was a discussion of health policy and how to evaluate whether it’s working or not. This person asserted several things that made no sense. His claim was that it would take a generation to evaluate the effectiveness of a health law. On the contrary, if you’re looking for the effects of a recent policy change, you look *closer* to the time of the policy change. A longer timeline allows more confounding factors to dominate the trend. Another bizarre claim was that it’s hard to evaluate policy that’s applied to millions of people. True enough, but the *size of the sample* is not the issue. A larger sample makes small effects *easier* to see, not harder. I said exactly this, and my interlocutor disagreed directly with this statement. No, it’s difficult to analyze policy because of *confounding factors*, i.e. all that other stuff that’s going on at the same time. A true ceteris paribus comparison isn’t possible. Perhaps this person actually had this in mind and did a bad job of explaining his argument, but then this would have been contrary to his claim that we’d have a better estimate of the benefits if we waited a generation to see the results (a generation being a long enough timeline for a lot of confounders to build up). This person seemed to have gotten some basic principles of statistics and social science backwards. (A larger sample size is better, and an effect that is *closer* in time to the policy change is easier to attribute to the policy.)

I presented some information about randomized controlled trials in which lots of people get free healthcare while a control group doesn’t, and the “free medicine” group doesn’t appear to get any healthier. (See the RAND healthcare experiment and the Oregon Medicaid experiment if you want to know more about this.) This evidence bears directly on the policy question. What’s the sense in giving away a lot of extra medicine if there isn’t an appreciable health benefit? I don’t know if he was familiar with this body of scholarship. He didn’t say. He simply asserted that the health effects would be real but too small to measure.  I cannot prove but strongly suspect that this was a case of someone rejecting out of hand *extremely* relevant information for the item under discussion.

Now, “The health effects are real but too small to measure” is a statistical claim. It’s not just something you can blurt out. The exact size of “Too small to measure” can be determined using statistics. (Actuaries like myself use something called "credibility theory" to determine how much trust to put into a dataset, depending on the sample size and strength of signal vs. noise.) It’s possible to calculate how small an effect would be “too small to measure” and perhaps show that it’s absurdly costly to save a single life. Would anyone want a health policy that cost, for example, a trillion dollars per life-year saved? A million dollars even? I wouldn’t, and most people don’t objectively value their lives that much, as determined by willingness to purchase safety features or take dangerous jobs (to list two ways that economist try to "value" a human life). Something that should have been an explicit calculation was instead just asserted. I could have just as easily asserted that the health law had *negative* health effects that were too small to measure, and we’d have been on equal footing. As it happens, I *did* open up an Excel workbook with some mortality-by-age data in it. Very crudely, I convinced myself that you’d need to see a few percentage points change in mortality for very young people, about a 1% change in mortality for 50-year-olds, and less than a 1% change in mortality for 60+ year-olds, to see a statistically significant mortality effect. That should be a starting point for anyone offering a “the benefits are too small to measure” kind of argument. My “analysis” was totally back-of-the-envelope and is probably wrong for various reasons; someone else might get another answer for what “too small to measure” means. My point is that you have to do some work, you have to use figures (real or at least made-up-but-plausible), and you have to use some math when you’re making a statistical argument. It’s inconsiderate to just assert something and make someone else do the legwork for you. “Here, let me think that through for you.”

If my interlocutor is reading this, my apologies for using you as a foil, but this isn’t about you. I’m not trying to be gossipy. The point I’m making has nothing to do with any one person or any particular argument. It just helps to have an example at hand, and this one serves nicely. I have probably wasted way too many hours of my life thinking through bad arguments left on my FB page. A healthier habit might be to simply ignore distracting blather and only communicate with people who state their arguments clearly. It bothers me to think that a bad argument can just be allowed to stand. Someone might see it and think it's superficially plausible. Unfortunately this puts me in a position of sometimes arguing with thoughtless people and reasoning through their arguments for them, and I always feel a little bit cheated when I get sucked into one of these discussions. As a clever friend of mine put it, the thoughtless commenter has a huge cost advantage over the thoughtful one. 

Healthcare Policy Changing. Falsifiable Predictions Wanted.

When the ACA first passed, I remember posing the following challenge: “Ok, ACA supporters, you got your law. Now what’s going to happen? How will the benefits manifest themselves? Make a prediction that we can check. In a few years you should recant if you’re way off. I may take a bet against your prediction, if it’s well-specified enough.”

It would have been interesting to hear people’s actual responses to my question, assuming anyone had thought about it. I was fishing for something like, “Mortality rates will fall for poor minorities by X deaths per 100k population,” or “Diabetes rates will decline by Y%, high blood pressure by Z%, among the currently uninsured population.” The point here is that a healthcare law should somehow affect health, and if it doesn’t noticeably do so it has failed as public policy. It’s fake medicine. The people who were singing the praises of Obamacare should have been willing to make some kind of falsifiable prediction the moment it passed. I took their unwillingness to do so to imply a lack of serious thinking. I never got a meaningful response, nor did I see any prominent pundits or bloggers independently come up with the same idea.

(By the way, it’s not enough for someone to point out that an improving trend has been at work since the law was passed. It could be a pre-existing trend that can’t possibly be attributed to the law. In fact, life expectancy has risen since 2000 so you have to be really careful about attributing any improvements in health to a bill passed in 2010. One has to show that any pre-existing trend has improved above and beyond the trend-line.)

One might have answered something like, “The world is really complicated, and a lot of things affect the overall health of any population. I’m unwilling to make such a prediction.” This would have been an interesting admission. If the major supporters of the ACA thought that the benefits are too small to measure, or so small they would be swamped by noise, they should have at least said so. Some of us think it's unwise to waste massive resources in pursuit of benefits that are speculative or invisible. 

One might have given another sort of answer, something like, “The law would work if implemented, but stupid Republican states will fail to implement it and stupid Republicans in a future Congress will hamstring the law.” I can imagine someone being tempted to issue this hedge, but again this is something that needs to be stated ahead of time. The person giving this kind of answer is predicting failure, and failure based on something endogenous to the system. If the failure of public policy is that predictable, we should oppose such policy. Your political initiative might have worked if it weren’t for that incorrigible opposition party, just as your lunar program might have worked were it not for stupid gravity!

Today it looks like some kind of repeal is imminent, and doomsayers are predicting some kind of blood-bath as people lose their insurance coverage. I seriously doubt it, but I am willing to hear from anyone capable of discussing this at room temperature. What *precisely* do you think will happen?

My own view is that health policy doesn’t matter all that much in terms of getting actual health outcomes, although bad policy can certainly be very expensive and saddle us with enormous burdens. The social science is pretty clear on this point. An individual’s “insured status” has little correlation with health after you’ve made the appropriate demographic adjustments; it simply isn’t true that insuring someone grants them “access to healthcare” in a way that makes them healthier. (Or more precisely, a population of such people won’t get healthier; any one such someone might get healthier or sicker. But it’s the population effect, not some individual’s health outcome, that tells you something about causation.) I have a long reading list for anyone who doubts this. Start with Cut Medicine In Half by Robin Hanson, and do go on to read the entire discussion. (Cato Unbound, where the essay was hosted, is a forum. In this particular one there are three other health policy experts who, while they never actually say Hanson is wrong, take issue with his claims.) Read In Excellent Health by Scott Atlas; here is a podcast of him discussing the book on Econtalk. Also check out Overtreated by Shannon Brownlee, Catastrophic Care by David Goldhill, Crisis of Abundance by Arnold Kling, Affordable Excellence: The Singapore Healthcare Story by William A Haseltine, Priceless by John Goodman, and I’m sure I’m forgetting a few others. Also listen to any episode of Russ Robert’s Econtalk that talks about healthcare, especially anything with Arnold Kling or Robin Hanson. Most people balk at the claim that people don’t get healthier when you give them a bunch of free medicine, but I’m on very solid ground here. Go review the literature a little if you're skeptical. I'll wait. Given all this, the obsessive fixation on getting healthcare to “the poor” is misguided. This is mostly not a fight about access to healthcare, but rather about who pays for what and how. 

My own prediction would be something like: Policy tweaks of the "pass/repeal the ACA" kind won't noticeably affect health outcomes (although much larger restructurings might). Policy tweaks that cause patients to face a larger proportion of the bill would result in cost savings with no measurable effect on health outcomes. I could try to be more specific if a specific proposal is on the table. But the people who are predicting disaster from the repeal of a seven-year-old healthcare bill look positively daffy. In terms of evidence-based policy, they don't have a leg to stand on. 

Pet Peeves of a Data Analyst

[Edit: I wrote this a few days ago then reread just before sharing. It comes off as more sneering and angry than I would like, though I was mostly in a good mood when I wrote it. I'm sharing anyway, recognizing that it could probably be written in a way that's more understanding to the peeve-inducers. I tried, when I could, to be constructive rather than abusive.]

I work with large datasets for a living, doing all manner of statistical analysis. My work ranges from simple summary statistics to model building and feature engineering on big datasets. I thought I’d list a few pet peeves of mine.

1)      Fake Precision on Noisy Data. Someone always pipes in with “Did you adjust/control for X? (Which happens to be a hobby horse of mine?)” Sometimes this is welcome, but sometimes the dataset is too small and therefore not credible enough for refined adjustments to matter. Sometimes the “analysis” is basically fitting a line through some random dots. If you had infinite data, the dots would *not* be random, but would be a noticeable pattern of some sort (linear or otherwise). But you have to work with the data you actually have. If your data are too sparse to see the real pattern, any “adjustment” you make, even if there’s some good theoretical reason for making it, isn’t going to matter. It just means you’re fitting a line though *this* set of random dots rather than *that* set. I’ve also seen maddening arguments about whether some set of points should be fitted with a line or a curve or some other sort of grouping, when in reality the data points are too noisy to make any such determination.

2)      Last Minute Demands on a Big Dataset. It’s often said that data modeling is 90% data gathering/cleaning and 10% model building. So it’s a huge headache when someone has a bright idea for a last minute insertion. Sometimes this is the fault of the modelers, but usually it’s wishy washy management deciding at the very last second that something they just thought of just now is very, very important. “Wait, will we be including social media history in our analysis of auto accident frequency? I didn’t see it in the list of variables. Let’s add it!” The data modeling people sigh at these kinds of requests, because it usually means a few days of additional data gathering and a delay in a (perhaps already determined) modeling schedule. Data modelers should keep lines of communication open and set some kind of “no further adjustments” date so that this doesn’t happen. But it probably will anyway.

3)      “Well, I did this at my former company…” Sometimes a person will move from a large company with millions of customers and a huge dataset to a small or medium-sized company. They often bring with them unrealistic expectations. With larger datasets it is easier to see very weak patterns that might be invisible on a smaller dataset. The pattern may be real, but you won’t see it in your data. At my company, you have to assemble several years' worth of customer data, in countrywide aggregates, to see real patterns. A much larger company might have 20% of the adult population as its customers, and can thus see real patterns in a single quarter’s worth of data in a single state or region. If a manager gets used to the latter, they will have unreasonable expectation if they moved to work at a smaller company. Sometimes this might be a matter not of data size but of expertise. If your previous company had three data science PhDs, a team of actuaries, and a bunch of SQL experts, but your new company has a bunch of Excel jockeys who barely know how to run an Excel regression, you won’t be able to implement all your awesome ideas at the new company. At the very least, it may take some time to develop the appropriate skill set.

4)      Ignoring the modeling results and expecting there to be no consequences. Data models sometimes give us surprising results. That’s why we do them. If we knew all the patterns ahead of time, there would be no need for the modeling. But I sometimes get requests such as: “I’m going to ignore a piece of your modeling results. Re-run your model so that I’m still right.” For example, a model might tell you that people above age 60 are at increased risk of an auto accident, but a manager objects that this is their target market. They may ask you to re-run the model without including age as a predictive variable, hoping that the new model will offset their (possibly unwise) business decision. (This is a completely made up example that is vaguely similar to something that might actually happen.) There may be some rare instances where this is appropriate, but managers need to understand when they are throwing away predictive power with their business decisions. If you throw a predictive variable out of your model, you are throwing away predictive power and you can’t get it back. Worse, the model will try to find the influence of that variable elsewhere, so you may end up with a model that is altogether weaker. To take an example, suppose I have a multivariable regression including driver age and other variables on auto accident frequency; if I throw driver age out of my model, the other variables that correlate with driver age will adjust to try to pick up the lost signal. It’s probably better to keep driver age in the model and consider it a “control variable” that won’t count against the driver (because you won't charge the customer based on their age or something). At any rate, it’s delusional to think that you can throw away predictive information and somehow totally offset this decision.

5)      Hobby horsing (again). As in, “Hey, I did X once and it was important and really made a big difference. Did you do X?” This overlaps with 3), obviously. Just because your brilliant insight saved the day once doesn’t mean it’s going to matter every time. It’s extremely annoying when people try to shoehorn their awesome idea into every single project.

6)      Finding a bullshit reason to discredit something. Often someone will object to an analysis the conclusion of which they dislike. A much more toxic dynamic is when someone dislikes a specific *person* and looks for stupid reasons to discredit their work. This is incredibly demoralizing to the data people and managers need to be very aware of when they are doing it. To guard against this, a data modeler should anticipate such objections and be ready to answer them, even going so far as to prepare for a specific person known to be an incredulous hard-ass.

7)      Someone gets mad at you for finding something inconvenient. I understand when someone responds incredulously to incredible results. Sometimes the data modeler really did goof. But sometimes the incredulity persists after all the objections are answered. “Yes, I adjusted for this. Yes, I controlled for that. Yes, I filtered for those.” After all this, perhaps you *still* conclude that your target market doesn’t deserve that huge discount you’ve been giving them, or your giant marketing initiative didn’t work. Accept the results and move on. I once had a boss who simply could not accept inconvenient results. He would come up with bullshit adjustments or filters or something hoping to get the results he wanted. It felt like I was being punished for bringing him bad news. Don’t be that guy.

8)      The regulatory state. I work in the insurance business as a research actuary. I am often in charge of crafting responses to regulators, sometimes filling out standard filing forms. Every single state (except Wyoming) requires that you file your rate plan with the state department of insurance (DOI), and every state DOI reserves the right to object to any filing. There is often a painful back-and-forth where regulators ask annoying questions and the insurance company tries to answer them. Sometimes the objections are based on the violation of a specific statute, and sometimes the reasons for objecting are far more capricious. They often have no statutory authority for their objections, or authority that comes from a lame catch-all (such as a vague law saying that rates must be “actuarially sound”, “not unfairly discriminatory”, etc.). This is a major pain for data modelers. I’m not making a libertarian stand here; if the state outlaws race-based discrimination and asks for reasonable proof that a model is not engaging in any such discrimination, I don’t object to that. My major gripe is that many of these departments are decades behind the latest modeling methods. Their standardized form questions often betray their ignorance.* A standard filing form from one state (see footnote below for additional detail) was littered with questions that looked like they were copied from a standard textbook on traditional linear models (very old school) but which are irrelevant to generalized linear models (glms, very commonly used in my industry). It’s like the actuaries at that department went to a predictive modeling seminar for a day or two and came back thinking they were experts on the topic. Then they copied some wording from the session handouts and turned it into an official state document. The non-standard questions we get from state DOIs are no better. Almost every filing is met with an “objection letter,” in which a DOI employee asks questions specific to the filing. One such question was (and I swear to you I am paraphrasing only slightly here): “What is a multivariate model?” Anyone remotely knowledgeable would have known that the term "multivariate" was a reference to glms, which almost every insurance company uses. These non-practitioners (I nearly said non-experts, but that would be a woeful understatement) have pathetically little knowledge of what they are actually regulating. I suspect this is the same in other industries. The latest, most cutting-edge methods must be justified, but must comply with decades-old language written for a different purpose. Such cutting edge tech must be explained to laymen who have the final decision. Once again I’m not here to critique government regulation in general. I just think it’s not too much to ask that government employees understand the thing they are regulating. And if a government agency cannot afford to keep such expertise on retainer, they need to relax or repeal those regulations.  This single factor is a huge barrier to innovation. If we can’t use a model unless it can be explained to an ignorant non-practitioner, that severely restricts what we can do. Another problem is that regulators sometimes demand a very specific statistical test when in practice something might be more of a judgment call. Data modeling is a process that requires a great deal of human judgment. What variables should I include? How should I group things? What kind of curve should I fit? Regulators often demand an unreasonable sort of rigor in the model-building process where all of this judgment is stripped away and replaced with an unalterable decision tree. Such a process will often lead to models with nonsensical results that any reasonable person can spot and fix, but the regulators interpret any insertion of human judgment as an attempt to be devious.

This list is by no means exhaustive. Obviously it could be longer, or shorter, but these were the peeves that occurred to me on one evening. 

* A questionnaire on generalized linear models (glms) in one state ask about tests for “homoscedasticity”, which means that the variance does not vary with the expected mean. (On a residuals plot, your residuals will spread out more on one side of the graph, rather than having a roughly constant standard deviation across the range.) This is a topic in traditional linear models, but the power of a glm is that you can relax the “constant variance” assumption. (You can make your error term gamma, poisson, tweedie, etc, rather than the traditional Gaussian that results in a constant expected variance.) Apologies if these technical details are confusing to the uninitiated, but it is really basic stuff as far as glms go. And every company is using these now.

Libertarians and Social Justice: A Sticking Point

Libertarians and the social justice left quite often come to the same policy conclusions. So why are they so often at each other’s throats?*

To take an example, most libertarians and most people who identify with the social justice left believe in gay marriage. If we were transported back to an era where interracial marriage was an issue, you’d see the same sort of agreement on policy. I think the problem is that both tribes arrive at pro-gay-marriage position from different kinds of arguments. Libertarians believe in equality under the law and an untrammeled right to free association. “Gay Marriage” isn’t a specific item; it’s a subset of a larger right of adults to freely associate with one another. The social justice left arrives at their pro-gay-marriage position through a different sort of argument, one that certainly doesn’t embrace a general right of free association. I'd probably fail to exactly articulate this line of reasoning, but it surely has something to do with certain classes of victims being historically oppressed and deserving a special consideration to counterbalance this history. Nevertheless, we have agreement on a large collection of policies, so there *should* be a viable coalition here.

I think this is the rub. Libertarians want the social justice folks to explain what principle underlies "the right to gay marriage," because such a principle entail many other rights, some of which the left is hostile to. Social justice folks bristle at this, because the struggle for gay marriage is an important fight by an oppressed minority. They might react with, "How dare you bring up something so trite as property rights? How dare you compare the right of a couple to marry to the right of a business owner to discriminate against unwanted patrons?" Both tribes are fishing for some sign that the other can be trusted. If only the social justice warrior would affirm a much more general right of free association and free transaction between consenting adults, the libertarian could trust him not to support a bunch of illiberal policies, justified with back-fit ad hoc reasoning. If only the libertarian would signal his unwavering support for this oppressed minority group, the social justice warrior could trust him not to betray the cause when high-minded principles get in the way of the next fight.

To take another example, the left often couches its criminal justice policy positions in terms of disparate impacts to minorities. To a libertarian, an injustice is an injustice regardless of how disparate the impact is. If you tell me a million people are unduly or unlawfully harassed by police, with some fraction of those unjustly arrested and a few dozen beaten or shot, I shouldn't care any more or less when you tell me the races of the victims. To the social justice warrior the root cause is racism or some other kind of bigotry, so they see it as obtuse or evasive when someone fails to clearly proclaim that this is the problem. In this view, fix the bigotry and the bad policies will go away. To the libertarian, things like the war on drugs and stop-and-frisk are unjust in and of themselves. If you got rid of this underlying policy the racial disparity in their application would obviously go away. In this view, it's obtuse to talk about the racial impact of an unjust policing when the policy fix is so obvious, and it's demeaning to imply that a non-minority victim's suffering at the hands of police should count less.

I don't know what it would take to get a functioning political coalition of libertarians and social justice types. If you read my blog, it's fairly obvious I come down on the libertarian side of this split. I don't want to give a self-flattering answer like "The solution is for lefties to become libertarians." Nor do I want to offer a lame split-the-difference compromise. My main purpose in this post is to articulate the reason why an otherwise obvious alliance doesn't form. I don't think it's a fatal disagreement, though, because all political coalitions contain subgroups that distrust or hate each other while sometimes arriving at specific policy agreements. Perhaps my explanation above proves too much. In that case, what's really going on here?

*A mild disclaimer. I don't mean to imply that there aren't any social justice libertarians, nor do I mean to imply that no social justice leftists have libertarian leanings. Clearly there's some overlap between both groups. I'm tying to highlight an area of discord, so forgive me if my discussion above treated two overlapping classes as purer than they really are. 

Good Comments

What is a “good” comment anyway?

I wrote a post a while ago listing all the bad commenting habits that I dislike the most. Around the same time, a blogger who I know on Facebook said (writing on FB, not on his blog) that he’s been disappointed with the quality of comments on his blog. I think this is probably a common problem. A select few blogs seem to garner very good comments. Steven Landburg’s blog The Big Questions has the very highest quality comments section of any blog I read. Scott Alexander’s blog Slate Star Codex also has a great comments section. I don’t go to Less Wrong very often, but it’s about as high-quality as Slate Star Codex. Econlog is also near the top of the list, which I believe is due to some thorough (sometimes overzealous) moderating. Marginal Revolution would benefit from more active moderating, but it does manage to attract a lot of good comments (if you can perform the task of filtering out the bad ones yourself). But even in these stand-out comment sections, the average comment is still pretty bad and probably not worth reading, let alone responding to. There are always people veering a little too far off topic, or behaving rudely to the other commenters, or making bad arguments, or carelessly misreading the original post.

So I got to thinking: You’re posting controversial ideas on the internet and inviting anonymous readers to comment? What could possibly go right? I thought it would be helpful to write a companion piece to my Bad Comments list. These are the ways that a comment thread can actually go right.

1)      Correcting a material fact in someone’s argument. Be careful about this one. The correction should be an unambiguous *correction*. If there are dueling studies or conflicting data sources or something, it’s not helpful to say, “You’re wrong, replace your facts with my facts and you’ll be right.” Also, tame the urge to think that facts somehow speak for themselves. All facts require some kind of interpretation in the form of an argument. But if someone is unambiguously mistaken on a point of fact, they might actually appreciate and thank you for the correction. It sounds crazy, but I’ve seen it happen. 

2)      Filling in some important but neglected background. This is similar to 1), but far more ambiguous. If someone, say, quotes the wrong number for annual gun deaths in the US, it’s trivial to look that up and correct it. It’s slightly more of a judgment call to say, “…but really we’re talking about homicides and accidents, not suicides. Also, the recent trends are relevant here…” Sometimes people get their facts right but the context wrong, so if you can fill in the relevant context that can be useful for the other denizens of the comments section. Judgment is obviously necessary here. Someone might take “filling in context” as a carte blanc to expound upon an alternative worldview for several paragraphs. Don’t be that guy.

3)      Pointing out a logical flaw in someone’s argument. By this I mean literally, pedantically correcting a logical error and nothing more. Like, “Whoops, you misused modus ponens. Your argument appears to be ‘If A then B. B. Therefore A.’” Don’t add any snark or berate the author. These things happen. You do them, too. Just make a quick correction and move on. You might even politely suggest that the conclusion is still true, just for reasons other than the argument offered. Try, when you can, to *directly* address the argument given. Many comments ignore the argument offered while supplying some orthogonal argument for why the conclusion can’t be right. Whenever possible, directly address the argument offered. (Here is a good example of what I'm talking about.)

4)      Identify the locus of your disagreement and fixate on that. I have seen far too many comment threads escalate into full-blown flame wars without anyone even specifying what they were claiming. The very good comment threads are the ones in which the commenters stay focused on a particular point of disagreement.

5)      Praise should be specific. No “F***-yeah!”s or “F***-ing A man!” I don’t think anyone wants a bunch of cheerleaders in their comments section. Excessive fist-pumping and high-fiving in the comments section can deter skeptical commenters, exactly the kind that the blogger needs to hear from. Say specifically what you liked and what you thought was a new or useful idea. Try to give the author good fodder for another post. If possible, suggest ways to make a strong post even stronger. If a post has you thinking "F*** yeah! Preach it!" then you should be actively looking for something wrong with it, because it's possible your emotions have gotten the better of you. 

6)      “You might want to read this…” If you have similar interests, it might be useful to suggest reading materials. Books, articles, movies, etc. Of course a quick article is more likely to actually be read than a book. Your recommendation might be appreciated. The standard is going to be much higher for suggesting literature critical or contrary to someone’s worldview. If I’m an anarcho-capitalist who reads a lot of economic arguments for anarcho-capitalism (David Friedman, Murray Rothbard, Pete Leeson), I may want to know if there’s an obscure author I have missed (Bruce Benson for example). On the other hand, such a person won’t voraciously read works written by socialists hoping to stumble on a rare nugget of truth. In the second case, if you can identify the best argued, best articulated short-ish work for a skeptic to read, your knowledge may be appreciated.

There are so many ways for comments to go wrong and so few ways for them to go right. Basically, if you’re going to leave an approving comment do so without “piling on” or excessive fist-pumping. And if you’re leaving a critical comment stay on topic, state your criticism clearly and succinctly. Don’t be a dick about it, don’t veer off topic, and don’t impugn the motives of the person you are “correcting.” I have wasted countless hours arguing with people who exhibit poor comment hygiene. It hasn’t all been a complete waste and I did learn some important lessons from some of these exchanges. But many of them could have and should have been completely avoided.

Another thought occurred to me while writing this. Most of what happens on the internet doesn’t get captured in the comments section. Most of it happens in our minds without leaving a visible trace. If there is a well-written, thoughtful article, most of its readers will simply absorb its content without commentary, perhaps stowing it away in their heads for future reference, or perhaps forgetting about the post itself while still absorbing its lesson. If we’re talking about a blog post, most of the push-back will be from unthoughtful people who simply can’t swallow the conclusion. Maybe my blogger-friend was mistaken to be disappointed by his comments section. The loudest, most impulsive, least reflective individuals are the ones most likely to fire off a comment, so the comments section is a very biased sample of reactions to any given post. (Ever see a comment start with "long time lurker, first time commenter"? Just realize that most of your readers are forever lurkers, but the quality of their thoughts is probably as high as or higher than that of your average commenter.) If you could somehow sample the very private thoughts of your readers, it just might restore your faith in humanity. 

The Animatronic President Show Everybody Wants to See

I want to own a museum that does a robot presidents show. This already exists, but here’s my addition. Animatronic Lincoln gets up when it’s his turn to speak and says, “Hi, boys and girls! I’m Abraham Lincoln, and I've just won the Civil War!” All the other presidents hit the deck. Lincoln looks behind him over one shoulder, then the other, then shrugs. “Anyway, it looks like it’s clear sailing from here, for me AND the blacks. I think I'll celebrate by taking in a show with the Mrs.”

Your imagination can take it from there. Perhaps an animatronic John Wilkes Booth could make an appearance.

Quote from To Serve and Protect by Bruce Benson

The Justice Department recently announced that it would stop using "private" prisons. Scare quotes because nothing that the government pays for is truly private. It reminded me of the following passage:

Some people argue that only the government should have police powers and the power to punish, since such powers in private hands will tend to be abused. In other words, the government must be the monopoly producer of police services, prosecution, and punishment. The fact is, however, that “the government” never actually produces anything. Everything that the government allegedly produces is actually produced by contracting with private entities working under contract. They are not owned by the government. They contract to provide their services because they expect to be better off than they would be in an alternative job. The benefits of the bureaucratic job may take many forms, including any pleasure received from helping to produce what a bureaucrat perceives to be in the public interest, a good living to support a family and/or an attractive life style, job security, perhaps pleasure from being in a position of power and authority, and so on. An individual police officer, then, is a private citizen who has been given a tremendous amount of power and discretion, and he is in a position to abuse the power that he is given. After all, since he is part of an organization with virtual monopoly power over the right to coerce, there is relatively little to constrain is tendencies to abuse his position, Not surprisingly, many types of abuse (corruption, physical abuse, falsification of evidence, etc.) occur in great numbers…

In light of this discussion, the normative view that government must be the only organization with police and punishment powers, for the fear that private entities might abuse such powers, really does not make that much sense. The fact that the idea of government production is a fiction actually implies that it makes more sense to have competitive options in order to constrain the ability of individuals to abuse power… In the context of the present discussion, however, this implies that contracting out must occur at some level.

This is an excerpt from the 2nd chapter of Bruce Benson's "To Serve and Protect." This is an important point. "Privatization" is not a yes or no question; all government services are ultimately supplied by private individuals. The question remains: should you have the option to deny the police your business if they fail to serve you, or should you be unconditionally subjected to their rules (and be forced to pay for their services even though you don't want them)? Should problem officers be difficult to fire (as public employees invariably are), or should the be easy to fire (as private employees typically are)?

There is nothing special about government provision that allows us to specify the outcome and achieve that outcome with any certainty. This is a point that libertarians like myself understand; perhaps we're bad at communicating it. Public provision and private provision are both subject to uncertainty. Either has the potential to harm the consumer. The real question is which system can be expected to perform better.

Anyway, I was sort of scratching my head at all the celebration over the Justice Department's announcement. It will still be the case that the federal government decides that certain people should be detained in prisons, and it's still the case that the federal government will pay certain other people to detain those prisoners. I don't really buy the framing that this is a categorically different way of doing things.