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Stuart_Armstrong comments on Counterfactual resiliency test for non-causal models - Less Wrong
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That sort of stuff messes up any model.
I might predict that Obama will be elected next US president; or I might predict that I will be elected US president. Both of these prediction will be wrong if an asteroid wipes out the human population, but that doesn't mean that both are equally good.
Basically, what is there that could have messed up Moore's law that isn't the equivalent of "rocks fall, everybody dies"?
Aliens show up, give us their awesome technology.
But I take your point.
Unless rocks fell, most people died, then aliens showed up with gifts of hyper-advanced technology at the exact moment that Moore's Law would have predicted that level. That's sort of how the idea that "working to keep up" with Moore's Law strikes me.
What? No!
My point was that any game-changer, positive or negative, disrupts Moore's law. Alien hypertech is one such game changer.
EDIT: edited for clarity.
I know, I was just playfully nullifying the game-changer. (Although, aliens restoring Moore's law with hypertech is only slightly less likely than aliens with hypertech at any other point in time.)
Ah, I see.
Wait, what? Aliens restoring Moore's law is vastly less likely than the (admittedly minuscule) probability of them showing up at some other time.
So it's a...vast minisculity?
Vastly minuscule, yes.
That's exactly my point, most of the counter-arguments you gave for the other two predictive models were along those lines, but only when it came to countering Moore's law did you put those weapons away and play gently.
Simple version: if your model includes phenomena X in its timeline, yet does not make any claims about X having causal influence, then I can play with X to attempt to break your model.
Hence I used the weapons that were implicit in the model. For Kurzweil's model, it includes the history of evolution on earth, a history littered with disasters, meteor impacts and the like. Since the model is claimed to be correct despite these disasters, I decided that adding a few more shouldn't break the model if the model actually worked.
I was gentler on Robin's model, because it focused more on narrowly on human evolution. Still, it covers a period in history where there were pandemic, genetic bottlenecks, dramatic expansions of new technology, and so on. If the model was correct across these phenomena, then I could play with them without blowing up the model.
What would be equivalent for Moore's much narrower law? Well, there are no "unless economic growth splutters" caveats. So the most credible way to break Moore's law is to add a few economic disasters and imagine their consequences. But actual real economic disasters seem to have not affected Moore's law at all. What else? Political change? This might work - there's some evidence that communist states didn't have a Moore's law for their own computer industry. So a global communist takeover could break Moore's law - or at least caveat it to "in a market economy, computer speeds will..."
Thanks, I see your point now. If that's the case, though, it really just boils down to "(models that we have extremely accurate data and current physical and recorded evidence for) tend to produce more accurate predictions than (models that are less so)".
Not exactly. Robin's and Kurzweil's model have more data! (as they include Moore's law as a subcomponent).
Don't you agree that Moore's law is the only trustworthy part of their models, though?
Simply pointing out that it's not just the quantity of the data that matters, but other factors too.
I think Robin's info about GDP growth throughout history is decent, too.
GDP estimates of premodern societies are highly speculative, and anyway the GDP of a premodern society was probably not a good measure of the magnitude of its economic activity.