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A lot of people have this sci-fi image, like something out of Deep Impact, Armageddon, Don't Look Up, or Minus, of a single large asteroid hurtling towards Earth to wreak massive destruction. Or even massive vengeance, as if it was a punishment for our sins.
But realistically, as the field of asteroid collection gradually advances, we're going to be facing many incoming asteroids which will interact with each other in complicated ways, and whose forces will to a large extent balance each other out.
Yet doomers are somehow supremely confident in how the future will go, foretelling catastrophe. And if you poke at their justifications, they won't offer precise physical models of these many-body interactions, just these mythic stories of Earth vs. a monolithic celestial body.
2They're critical questions, but one of the secret-lore-of-rationality things is that a lot of people think criticism is bad, because if someone criticizes you, it hurts your reputation. But I think criticism is good, because if I write a bad blog post, and someone tells me it was bad, I can learn from that, and do better next time.
I read this as saying 'a common view is that being criticized is bad because it hurts your reputation, but as a person with some knowledge of the secret lore of rationality I believe that being criticized is good because you can learn from it.'
And he isn't making a claim about to what extent the existing LW/rationality community shares his view.
Seems like the main difference is that you're counting up with status and counting down with genetic fitness.
There are plenty of people who plan their lives around having & raising kids. And there are plenty of people who put tons of effort into leveling up their videogame characters, or affiliating-at-a-distance with Taylor Swift or LeBron James, with minimal real-world benefit to themselves.
And it's easier to count up lots of things as status-related if you're using a vague concept of status which can encompass all sorts of status-related behaviors, including (e.g.) both status-seeking and status-affiliation. "Inclusive genetic fitness" is a nice precise concept so it can be clear when individuals fail to aim for it even when acting on adaptations that are directly involved in reproduction & raising offspring.
The economist RH Strotz introduced the term "precommitment" in his 1955-56 paper "Myopia and Inconsistency in Dynamic Utility Maximization".
Thomas Schelling started writing about similar topics in his 1956 paper "An essay on bargaining", using the term "commitment".
Both terms have been in use since then.
On one interpretation of the question: if you're hallucinating then you aren't in fact seeing ghosts, you're just imagining that you're seeing ghosts. The question isn't asking about those scenarios, it's only asking what you should believe in the scenarios where you really do see ghosts.
My updated list after some more work yesterday is
96286, 9344, 107278, 68204, 905, 23565, 8415, 62718, 83512, 16423, 42742, 94304
which I see is the same as simon's list, with very slight differences in the order
More on my process:
I initially modeled location just by a k nearest neighbors calculation, assuming that a site's location value equals the average residual of its k nearest neighbors (with location transformed to Cartesian coordinates). That, along with linear regression predicting log(Performance), got me my first list of answers. I figured that list was probably good enough to pass the challenge: the sites' predicted performance had a decent buffer over the required cutoff, the known sites with large predicted values did mostly have negative residuals but they were only about 1/3 the size of the buffer, there were some sites with large negative residuals but none among the sites with high predicted values and I probably even had a big enough buffer to withstand 1 of them sneaking in, and the nearest neighbors approach was likely to mainly err by giving overly middling values to sites near a sharp border (averaging across neighbors on both sides of the border) which would cause me to miss some good sites but not to include any bad sites. So it seemed fine to stop my work there.
Yesterday I went back and looked at the residuals and added some more handcrafted variables to my model to account for any visible patterns. The biggest was the sharp cutoff at Latitude +-36. I also changed my rescaling of Murphy's Constant (because my previous attempt had negative residuals for low Murphy values), added a quadratic term to my rescaling of Local Value of Pi (because the dropoff from 3.15 isn't linear), added a Shortitude cutoff at 45, and added a cos(Longitude-50) variable. Still kept the nearest neighbors calculation to account for any other location relevance (there is a little but much less now). That left me with 4 nines of correlation between predicted & actual performance, residuals near zero for the highest predicted sites in the training set, and this new list of sites. My previous lists of sites still seem good enough, but this one looks better.
Did a little robustness check, and I'm going to swap out 3 of these to make it:
96286, 23565, 68204, 905, 93762, 94408, 105880, 9344, 8415, 62718, 80395, 65607
To share some more:
I came across this puzzle via aphyer's post, and got inspired to give it a try.
Here is the fit I was able to get on the existing sites (Performance vs. Predicted Performance). Some notes on it:
Seems good enough to run with. None of the highest predicted existing sites had a large negative residual, and the highest predicted new sites give some buffer.
Three observations I made along the way.
First (which is mostly redundant with what aphyer wound up sharing in his second post):
Almost every variable is predictive of Performance on its own, but none of the continuous variables have a straightforward linear relationship with Performance.
Second:
Modeling the effect of location could be tricky. e.g., Imagine on Earth if Australia and Mexico were especially good places for Performance, or on a checkerboard if Performance was higher on the black squares.
Third:
The ZPPG Performance variable has a skewed distribution which does not look like what you'd get if you were adding a bunch of variables, but does look like something you might get if you were multiplying several variables. And multiplication seems plausible for this scenario, e.g. perhaps such-and-such a disturbance halves Performance and this other factor cuts performance by a quarter.
Is this calculation showing that, with a big causal graph, you'll get lots of very weak causal relationships between distant nodes that should have tiny but nonzero correlations? And realistic sample sizes won't be able to distinguish those relationships from zero.
Andrew Gelman often talks about how the null hypothesis (of a relationship of precisely zero) is usually false (for, e.g., most questions considered in social science research).