Scott_Aaronson2

The Weighted Majority Algorithm

Silas: Look, *as soon you find a 1 bit*, you've solved the problem with certainty. You have no remaining doubt about the answer. And the expected number of queries until you find a 1 bit is O(1) (or 2 to be exact). Why? Because with each query, your probability of finding a 1 bit is 1/2. Therefore, the probability that you need t or more queries is (1/2)^(t-1). So you get a geometric series that sums to 2.

(Note: I was careful to say you succeed with certainty after an *expected* number of queries independent of n -- not that there's a constant c such that you succeed with certainty after c queries, which would be a different claim!)

And yes, I absolutely assume that the adversary knows the algorithm, because your algorithm is a fixed piece of information! Once again, the point is not that the universe is out to get us -- it's that we'd like to succeed even if it *is* out to get us. In particular, we don't want to assume that we know the probability distribution over inputs, and whether it might happen to be especially bad for our algorithm. Randomness is useful precisely for "smearing out" over the set of possible environments, when you're completely ignorant about the environment.

If you're not to think this way, the price you pay for Bayesian purity is to give up whole theory of randomized algorithms, with all the advances it's led to even in deterministic algorithms; and to lack the conceptual tools even to *ask* basic questions like P versus BPP.

It feels strange to be explaining CS101 as if I were defending some embattled ideological claim -- but it's good to be reminded why it took people a long time to get these things right.

The Weighted Majority Algorithm

Silas: "Solve" = for a worst-case string (in both the deterministic and randomized cases). In the randomized case, just keep picking random bits and querying them. After O(1) queries, with high probability you'll have queried either a 1 in the left half or a 1 in the right half, at which point you're done.

As far as I know this problem doesn't have a name. But it's how (for example) you construct an oracle separating P from ZPP.

The Weighted Majority Algorithm

Don: When you fix the goalposts, make sure someone can't kick the ball straight in! :-) Suppose you're given an n-bit string, and you're promised that exactly n/4 of the bits are 1, and they're either all in the left half of the string or all in the right half. The problem is to decide which. It's clear that any deterministic algorithm needs to examine at least n/4 + 1 of the bits to solve this problem. On the other hand, a randomized sampling algorithm can solve the problem *with certainty* after looking at only O(1) bits on average.

Eliezer: I often tell people that theoretical computer science is basically mathematicized paranoia, and that this is the reason why Israelis so dominate the field. You're absolutely right: we *do* typically assume the environment is an adversarial superintelligence. But that's not because we literally think it *is* one, it's because we don't presume to know which distribution over inputs the environment is going to throw at us. (That is, we lack the self-confidence to impose any particular prior on the inputs.) We *do* often assume that, if we generate random bits ourselves, then the environment isn't going to magically take those bits into account when deciding which input to throw at us. (Indeed, if we like, we can easily generate the random bits *after* seeing the input -- not that it should make a difference.)

Average-case analysis is also well-established and used a great deal. But in those cases where you *can* solve a problem without having to assume a particular distribution over inputs, why complicate things unnecessarily by making such an assumption? Who needs the risk?

The Weighted Majority Algorithm

*I am interested in what Scott Aaronson says to this.*

I fear Eliezer will get annoyed with me again :), but R and Stephen basically nailed it. Randomness provably never helps in average-case complexity (i.e., where you fix the probability distribution over inputs) -- since given any ensemble of strategies, by convexity there must be at least one deterministic strategy in the ensemble that does at least as well as the average.

On the other hand, if you care about the *worst*-case running time, then there are settings (such as query complexity) where randomness provably *does* help. For example, suppose you're given n bits, you're promised that either n/3 or 2n/3 of the bits are 1's, and your task is to decide which. Any deterministic strategy to solve this problem clearly requires looking at 2n/3 + 1 of the bits. On the other hand, a randomized sampling strategy only has to look at O(1) bits to succeed with high probability.

Whether randomness ever helps in *worst-case polynomial-time computation* is the P versus BPP question, which is in the same league as P versus NP. It's conjectured that P=BPP (i.e., randomness never saves more than a polynomial). This is known to be true if really good pseudorandom generators exist, and such PRG's can be constructed if certain problems that seem to require exponentially large circuits, really do require them (see this paper by Impagliazzo and Wigderson). But we don't seem close to proving P=BPP unconditionally.

Complexity and Intelligence

(the superscripts didn't show up: that was N^googol and 2^N)

Complexity and Intelligence

Um, except that we *also* don't know whether there are computations that can be checked in N time but only performed in Ngoogol time. The situation is qualitatively the same as for N versus 2N.

Complexity and Intelligence

*Otherwise, of course a larger environment can outsmart you mathematically.*

No, not of course. For example, suppose P were equal to PSPACE. Then even though a larger environment could fundamentally outsmart you mathematically (say by solving the halting problem), it couldn't *prove* to you that it was doing so. In other words, the situation with polynomial-time computation would be more-or-less the same as it is with unlimited computation: *superintelligent machines could only prove their superintelligence to other superintelligent machines.*

That the situation with efficient computation appears to be *different*---i.e., that it appears superintelligent machines can indeed prove their superintelligence to fundamentally dumber machines---is (if true) a profound fact about the world that seems worth calling attention to. Sure, of course you can nullify it by assuming away all complexity considerations, but why? :-)

Complexity and Intelligence

*In fact, it's just bloody hard to fundamentally increase your ability to solve math problems in a way that "no closed system can do" just by opening the system. So far as I can tell, it basically requires that the environment be magic and that you be born with faith in this fact.*

As Wei mentioned, P≠NP is basically the conjecture that this isn't true: i.e., that you can exponentially increase your ability to solve math problems by your environment being magic and your *not* being born with faith in that fact. So for example, if your environment immediately inverted any one-way function, that would be evidence (no faith required) that your environment is not merely 'slightly' smarter than you are, but *astoundingly* smarter. In qualitative terms, I think it would be almost as astounding as if the environment solved the halting problem.

My Childhood Death Spiral

Carl: I'm not sure, but I'd certainly *try* such a pill were the effects reversible.

Will: Yeah, it's 1/4, thanks. I somehow have a blind spot when it comes to constants. ;-)