LessWrong 2.0 Reader

I'd expect that too many people apply for most units for it to be worth that much effort to game. A quick search indicates that 40-500 people might apply for a unit. If it takes someone several years to successfully apply after they are eligible, that doubles the lost earnings. They also can't resell the unit for several decades, so they are locked in for a long time. The option value of moving in the next 40 years seems significant. https://www.wbur.org/radioboston/2019/02/20/inclusionary-development-housing-affordable https://www.bostonglobe.com/lifestyle/style/2014/12/15/boston-housing-lottery-offers-luxury-low-cost/aAPOGUm4FwbvHExq27mDUM/story.html

For what it's worth, I'm with Said rather than Zack on this one.

(It would make more sense if these initial letters were associated with a mnemonic or something; then there would be a reason for emphasizing a bunch of first letters. But it seems to have been done just for, I dunno, fun.)

If this would be an effective model of wrinkles, why does giving a people botox seem to remove wrinkles?

Had the teacher presented a dozen of dice all showing the same number and asked how this could have happened they would have been wiser.
But the situation is similar. In pure theory this could happen naturally, in that case doubting it would be a case of gamblers fallacy or not knowing the Anthropic principle.

If you encounter the impossible you should check your assumptions, but to say that a human like entity has caused this outcome is dangerous.

When you are presented with a very unlikely outcome you have to accept it.

Had the teacher shown a dozen dice all showing the same number and asked how he did it, there would have been two answers:

2. You

For reservoir computing, there are concrete results. It is not just magic.

This is good stuff!

Thanks!

Here's another attempt at explaining.

1. Suppose Predict-O-Matic A has access to historical data which suggests Predict-O-Matic B tends to be extremely accurate, or otherwise has reason to believe Predict-O-Matic B is extremely accurate.

2. Suppose the way Predict-O-Matic A makes predictions is by some process analogous to writing a story about how things will go, evaluating the plausibility of the story, and doing simulated annealing or some other sort of stochastic hill-climbing on its story until the plausibility of its story is maximized.

3. Suppose that it's overwhelmingly plausible that at some time in the near future, Important Person is going to walk up to Predict-O-Matic B and ask Predict-O-Matic B for a forecast and make an important decision based on what Predict-O-Matic B says.

4. Because of point 3, stories which don't involve a forecast from Predict-O-Matic B will tend to get rejected during the hill-climbing process. And...

   • Because of point 1, stories which involve an inaccurate forecast from Predict-O-Matic B will tend to get rejected during the hill-climbing process. We will tend to hill-climb our way into having Predict-O-Matic B's prediction change so it matches what actually happens in the rest of the story.

   • Because the person in point 3 is important and Predict-O-Matic B's forecast influences their decision, a change to the part of the story regarding Predict-O-Matic B's prediction could easily mean the rest is no longer plausible and will benefit from revision.

   • So now we've got a loop in the hill-climbing process where changes in Predict-O-Matic B's forecast lead to changes in what happens after Predict-O-Matic B's forecast, and changes in what happens after Predict-O-Matic B's forecast lead to changes in Predict-O-Matic B's forecast. It stops when we hit a fixed point.

Now that I've written this out, I'm realizing that I don't think this would happen for sure. I've argued both that changing the forecast to match what happens will improve plausibility, and that changing what happens so it's a plausible result of the forecast will improve plausibility. But if the only way to achieve one is by discarding the other, I guess both tweaks won't cause improvements to plausibility in general. However, the point remains that a fixed point will be among the most plausible stories available, so any good optimization method will tend to converge on it. (Maybe just simulated annealing, but with a temperature parameter high enough that it finds easy to leap between these kind of semi-plausible stories until it gets a fixed point by chance. Or if we're doing hill climbing based on local improvements in plausibility instead of considering plausibility when taken as a whole.)

I think the scenario is similar to your P(X) and P(Y) discussion in this post [LW · GW].

It just now occurred to me that you could get a similar effect given certain implementations of beam search. Suppose we're doing beam search with a beam width of 1 million. For the sake of simplicity, suppose that when Important Person walks up to Predict-O-Matic B and asks their question in A's sim, each of 1M beam states gets allocated to a different response that Predict-O-Matic B could give. Some of those states lead to "incoherent", low-probability stories where Predict-O-Matic B's forecast turns out to be false, and they get pruned. The only states left over are states where Predict-O-Matic B's prophecy ends up being correct -- cases where Predict-O-Matic B made a self-fulfilling prophecy.

It's not just selection effects on organisms - it's within organisms. The examples given are NOT fully understood, so (for example,) the bacterial transcription network motifs only contain the relationships that we understand. Given that loops and complex connections are harder to detect, that has selected for simplicity.

Given that, I still want to read the book and see the argument more fully.

There was a bubble, and there is also secular growth in the market, with a lot of churn that makes buy-and-hold a fairly bad idea. Those aren't inconsistent. Here's a graphic of the churn. Most of the early companies died.

But if you put all your money in the hot IPOs of Netscape, Yahoo, Lycos, and Excite in 1995, you'd have done very poorly. If you extended this to 1996, you could add Mindspring and Checkpoint (the only one that did well, so far, which is up 29x, for a 16% annualized return to date.) It took until 1997 to get any long-term fantastic return, for Amazon - which is up 1000x since 1997, or a 37% annual return - fantastic, but if you were prescient, and it was an entire tenth of your portfolio, on average you did just OK. Skipping ahead to 1999-2000, the height of the bubble, here's the list. Nothing made big bucks.

So we can construct a portfolio with 10 stocks, 8 of which went bust, and 2 of which, checkpoint and Amazon, did well. Your compound 22-year return? 5.25% (And if you bought an S&P index fund in 1998 at 1,000, you'd have made 6.5% annually.)

Agreed on both points.