A most excellent post, that's something I've been thinking about recently too, and I've come to the conclusion that in many cases it's perfectly okay to be close-minded, or to reject an argument without having a good counter-argument. I hadn't made the link with why atheists and skeptics should probably mellow out when making fun of religious people.
I think *everybody* should study crackpots (or at least, everybody who cares about ideas); so that everybody gets a better idea of how it feels to be convinced by bullshit. That would probably increase the crackpots' audience, but on the other hand might make people less likely to turn crackpot.
You could probably make interesting exercises by mixing crackpot arguments and mainstream-but-old arguments (so that they may not use the latest vocabulary), and have a CFAR exercise about distinguishing them.
I don't think the simulation argument is *wrong* as much as irrelevant - as for Boltzmann brains, even if it's true my decisions should be the same, so I don't see why I should care. Sure, on one level it's kind of interesting to know that I might be being simulated, but it's not as if it mattered much.
I agree. I was thinking of following this up by posting links to some of the most reasonable-sounding and convincing crackpots who have short, accessible persuasive arguments online. Steven from Black Belt Bayesian linked to this
a while back, which is a decent example of the sort of thing I'd be looking for. You have any suggestions?
As for the exercise, I kind of intended my hermeneutics game
to work kind of like this, in making it clear how convincing an argument even smart people could come up with for even randomly chosen positions in a short amount of time.
Your story about Velikovsky is pretty much exactly the same as my father's story about reading "Chariots of the Gods".
Logically valid arguments are only sound if the premises are true. Most crackpot arguments are indeed pretty close to valid, but they're not sound because they have a false premise.
Von Daniken is a special case in that AFAIK he actually did
completely make up some data (eg he talked about caves with certain artifacts that were just totally imaginary).
Most of the good crackpots I have read avoid that, and are just very good at interpreting real data to fit their theories. Dealing with data-fabricators seems to require a totally new level of paranoia, although luckily convincing ones seem to be rare.
I never found anything by von Daniken at all convincing, and his theme park
was kind of a disappointment.
I was confused to notice you assign female gender to the average high school dropout. Normally people default to male gender unless talking about a population dominated by women; I websearched for "high school dropout rates by gender" and the first hit
suggests the gender ratio is pretty even. Have you had a different experience?
(Oh -- maybe high school dropouts visiting hospitals are mostly female?)
I assume he was just hewing to the trend of using the female gender pronoun in a gender-neutral sense, and did not mean anything in particular by it.
Everything in this post strikes me as basically correct. The one awful thing I would add is that when most people adopt epistemic learned helplessness, they don't believe it's possible for *anyone* to do better. In particular they don't believe it's possible for you to do better, and that you're stupid for trying, and that if you think you can do better you're claiming social status above theirs, and so on. They have given up on Reason itself, not on their own use of it, and if you try they will smile down upon you superiorly - or for those of a kinder nature, take you aside and give you worried advice about how that whole Reason stuff doesn't actually work. The novice goes astray and says "The Art failed me", the master goes astray and says "I failed my Art".
My father's response would be, basically, that yes, you *can* Do Better, but only if you go to the effort to become an expert in the domain you're trying to form an opinion on - which, on many topics, would take years of study. Being able to present an argument that a smart layperson would find convincing isn't very good Bayesian evidence; being able to present an argument that a fellow expert would find convincing is both a much harder task and is much stronger evidence in favor of the argument's conclusion.
(Also, as far as I can tell, "become an expert yourself" is a bar that you, Eliezer, appear to have met in your own field(s), despite your lack of formal credentials.)
Yeah. I've basically decided my argument-evaluator is likely quite stupid unless and until its results show definite good results of some sort, even aesthetic. Until then it's just being played by other people's superior simulations of me. Many of the stupidest things I've ever done have basically been because I was convinced of something that I later realised was utter tosh.
My first thought upon reading this was the LW post on "Reason as memetic immune disorder (http://lesswrong.com/lw/18b/reason_as_memetic_immune_disorder/)."
Edited at 2013-01-06 01:54 am (UTC)
2013-01-06 04:00 pm (UTC)
Well, I'm quite glad that you came around to sanity.
A brief remark on the "Even the smartest people I know have a commendable tendency not to take certain ideas seriously. Bostrom's simulation argument, the anthropic doomsday argument, Pascal's Mugging - I've never heard anyone give a coherent argument against any of these, but I've also never met anyone who fully accepts them and lives life according to their implications."
That's because those arguments truly are of bullshit-grade reliability.
E.g. in the simulation argument, you make some very fishy assumptions - such as an assumption that probability of your existence is equal among all copies of 'something like you'. It would be highly likely to be wrong via a mere lack of reason why that would be so - but there's more - you should already start smelling the overpowering stench of bullshit because your conclusion depends on arbitrary and fuzzy choice.
That is far more than sufficient argument to dismiss persuasiveness of simulation argument entirely.
But some people have poor understanding of what is required for dismissal, in the far mode. E.g. they require a persuasive argument in favour of some other set of assumptions. That puts bullshit at too much advantage.
The doomsday argument is even worse in this regard.
The problem with this is that often totally valid conclusions are explained by bullshitting, and due to the social vetting process, people tend to be exposed to a bunch of true conclusions supported by bullshit.
|From: Dmytry Lavrov|
2013-01-07 11:21 am (UTC)
I wonder if you'd call not driving while intoxicated 'learned helplessness'
Taking ideas seriously while being ignorant and/or stupid is like driving while intoxicated. Nothing to be glad about. It is a bit difficult to ingrain into people - in their own minds, the drunks are sober...
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2013-02-18 08:38 pm (UTC)
I had something of a similar idea quite recently, but I couldn't explain it so well and I didn't have a very good name for it.
I'd been convinced one way, and then the other way, and then back again by really good, solid arguments, much like yourself. There has to be a way of dealing with that situation that doesn't involve a mental roller coaster.
2013-02-21 05:34 pm (UTC)
About the arguments...
In computer science, we have something called the pigeon principle. Essentially, it says that you cannot fit N+1 things in N slots, but it has far reaching implications. A more common one is the idea of compression... you cannot create a program that compresses every possible 1000 bit file to a smaller size, because there are 2^1000 possible 1000 bit files, and less then that number of smaller files, so some of those larger files HAVE to map to the same smaller file. Now that I have provided a basic example, one problem with the simulation argument is that it implies that a simulation is possible within a simulation. However, to simulate a computation, you need, at minimum, the same amount of memory as the computation itself, as well as some extra to hold the program that run the simulation. An infinite chain of simulations is therefore impossible by induction... each embedded simulation has to use less space then the one before it. Of course, this argument makes assumptions, like the fact that some more advanced computational model does not exist, the maximum speed of light limiting the scope of a simulation to a finite space, etc, but there is no particularly good reason to belive these axioms are flawed.
The anthropoic doomsday argument is a great example of a total failure of understanding of statistics. The metric chosen is arbitrary. You could pick "number of total years lived by humans" or "age of the universe" or "age of the earth" or whatever else, and get different results, to prove by contradiction that the method is false, even without knowing the detailed reasons why.
The standard argument against pascal's mugging is simply that if the probability of X -> Y is unknown, and so is the probability of (not X) -> Y, then whatever model you use, they probably cancel out. In other words, perhaps this guy will kill 3^^^^3 people if I DO give him $5... since I have no evidence that either is possible. Additionally, in this particular case (but not in the case of pascal's wager neccessarily), any consequence outside of the observable will have the same cancelation properties. And they are nicely recursive as well... if you come up with a model that does have different outcome probabilities for unknown events, then I can come up with an equivalent one. Which one is more likely accurate? etc etc. Really, what it boils down to is that untestable models are useless, and models which are not based on existing evidence are useless.
To actually get to the point of the article though... while engineering indeed supports some degree of black and white perception, the view from math and the sciences seems different. In math, everything is understood to be at most a useful abstraction, built completely arbitrarily on a set of axioms. There are alternative axioms and alternative maths, and the truths proclaimed are always conditional. On the scientific end, there are some (understood) problems with implementation, but the basic ideas of the scientific revolution, that our goal is to create models and then test them, is very difficult to find flaws in. It is built to be an alternative method to finding truth then arguments, which is clearly less useful. No, you should not accept an argument because it is well reasoned, nor should you accept it if it matches your preconceptions, and you should not reject it for those reasons either. You simply measure how useful it is at predicting outcomes compared to other models, and use it as a tool for prediction if it is more effective at matching the data then alternative models.
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