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Closed Limelike Curves
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Thank You, Old Man Jevons, For My Job
Old Man Jevons Can’t Save You Now (Part 2/2)
“Always go with your first intuition on multiple choice” reflects advice that’s specifically good for students who are anxious because they’re taking a test. The student will generally select the correct answer (or at least the one that’s most likely to be correct). If they’re somewhat uncertain about it, they’ll then start to feel anxious; this anxiety will build over time, resulting in a more and more pessimistic assessment of how likely they are to be correct, resulting in even more anxiety. This continues until the student is either sufficiently pessimistic to think that the original answer was not the best or else changes it simply to relieve the stress. This happens even though no new information has been received, implying said change is unlikely to be correlated with correctness and more likely simply reflects a failure of human psychology.
In short, a test is an especially bad test case (pun fully intended) for this because the amount of bias being introduced increases over time with anxiety, rather than decreasing.
Arguing Absolute Velocities
From my own experience reading the literature, I propose the following: The Bimodal Market Hypothesis.
The BMH states that the market is either a terrifying beast of infinite power that has priced in the number of grandchildren you will have into McDonald’s stocks, or is so stupid it kinda makes you want to cry. Evidence for this includes:
1. The Dot-Com bubble, the fact that Bitcoin has a price exceeding $0 despite there currently existing cryptocurrencies that are strictly better in every way, and that time the market proved to be incapable of doing addition. Maybe it’s when tech is involved?
2. But also, the Peso problem, where an apparent decades-long anomaly in the markets turned out to exist because the market was accurately estimating the probability that the Mexican government would be unable to maintain its peg.
Situation 1 occurs when idiots outnumber financial experts, and overwhelm the ability of “Smart money” to accurately price a stock in the face of a hype wave. Everyone reasonable avoids these stocks like the plague; shorting them exposes you to the possibility of bankruptcy in the case that the madness fails to subside in time. Situation 2 happens when financial experts poring over spreadsheets determine the value of a stock by rereading every statement until it’s become their favorite work of literature before making a decision.
Policy proposal: Make it illegal to trade stocks if you haven’t read A Random Walk Down Wall Street, don’t know what a PE ratio is, if you do know what a candlestick graph is, or if you think that the reason why stocks go up is something like “Everyone thinks they will.”
I agree, in that a very small improvement on a cryptocurrency shouldn’t actually result in it outcompeting everything else. The thing is that Bitcoin has at this point been completely pummeled by other cryptocurrencies like Ethereum or stablecoins in terms of usefulness, to the point where it seems clear to me that Bitcoin mostly exists and will continue to exist only for speculative purposes.
(Also: The opposite of the virtue of lightness usually goes by the name of conservatism bias in the cognitive science literature.)
This post seems both interesting and like a way to get a very unrepresentative sample.
This is fair and an important caveat. Pure arbitrage disappears quickly in the market. At the same time, though, no pure arbitrage profits is only necessary and not sufficient for full efficiency. An efficient market means no (or very few) positive expected utility opportunities still left around. The EMH still implies the price of shorts should have fallen until it became economically feasible to correct the mispricing.
Do you have any strong evidence to back this up? Opinions on education are dime a dozen, studies are what I’d like to see for once.
If “women’s college” is a proxy variable for “liberal arts college”, that’s a good reason to ding people for listing a women’s college.
I suspect you’re misunderstanding what a “Liberal arts college” is. In theory, a liberal arts college is one that focuses exclusively on “Academic” subjects, rather than purely practical ones. Math, science, and technology would all fall under the liberal arts, but a liberal arts college won’t offer degrees in, say, accounting. In practice, a liberal arts college is a small college that focuses on teaching and only offers undergraduate degrees.
Liberal arts undergrads generate a disproportionate number of PhDs in the sciences. Swarthmore, for instance, has more Nobel laureates per student than any other school (including all the Ivy League colleges).
I can confirm Julia and R both have one-based arrays and also one-based month numbers. I’m guessing they tend to line up quite often.
I pledge to match the bounty of the next person to pledge $5,000, because of research showing this encourages people to donate money.
So if someone else pledges, we’ve reached a third of the median US salary.
It sounds like a good idea, so I wouldn’t be surprised if it already exists.
The article provides one possible resolution to one Fermi paradox, but not another, and I think both a lot of literature and the comments here are pretty confused about the difference.
The possibly-resolved Fermi paradox: “Why do we see no life, when we’d expect to see tens of thousands of civilizations on average?” Assuming there really is no alien life, this paper answers that pretty convincingly: the distribution of civilizations is highly-skewed, such that the probability that only one civilization exists can be pretty large even if the average number of civilizations is still very high.
The unresolved Fermi paradox is “Why aren’t there any aliens?” This paper doesn’t answer that question (nor does it try to). It’s just pointing out that there’s a ton of possible reasons, so it’s not all that surprising that some combination of them might be very restrictive.
The AI has a response. It says no.
A lot of the corrigibility properties of CIRL come from uncertainty: it wants to defer to a human because the human knows more about its preferences than the robot. Recently, Yudkowsky and others described the problem of fully updated deference: if the AI has learned everything it can, it may have no uncertainty, at which point this corrigibility goes away. If the AI has learned your preferences perfectly, perhaps this is OK. But here Carey’s critique of model misspecification rears its head again—if the AI is convinced you love vanilla ice cream, saying “please no give me chocolate” will not convince it (perhaps it thinks you have a cognitive bias against admitting your plain, vanilla preferences—it knows the real you), whereas it might if it had uncertainty.
The standard approaches to dealing with this are nonparametric models, safe Bayes, and including many different models in your space of all possible models.
Really? I think a tiny bit of effort will do exactly nothing, or at best further entrench their beliefs (“See? Even the rationalists think we have valid points!”). The best response is just to ignore them, like most trolls.
That’s correct, but that just makes this a worse (less intuitive) version of the stag hunt.
I think there’s a fundamental asymmetry in the case you mentioned—it’s not verifying whether a program halts that’s difficult, it’s writing an algorithm that can verify whether any program halts. In other words, the problem is adversarial inputs. To keep the symmetry, we’d need to say that the generation problem is “generate all computer programs that halt,” which is also not possible.
I think a better example would be, how hard is it to generate a semiprime? Not hard at all: just generate 2 primes and multiply them. How hard is it to verify a number is semiprime? Very hard, you’d have to factor it.
You’re right, although 1850-1900 captures the Second Industrial Revolution.