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# Scott_Aaronson2 comments on The Weighted Majority Algorithm - Less Wrong

18 12 November 2008 11:19PM

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Comment author: 14 November 2008 09:02:18AM 7 points [-]

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?

Comment author: 14 July 2013 09:00:54AM *  0 points [-]

EDIT: OK, I found the explanation further down, seems I was on the right path.

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.

Sorry Scott, I know I’m late to the party, but this got me curious. Can you explain this, or point me to an explanation? I’m not sure I got the correct numbers.

As far as I can tell, a reasonable deterministic algorithm is to examine, one by one, at most the (n/4 + 1) bits (e.g., the first); but if you find a 1 early, you stop. The nondeterministic one would be to just test bits in random order until you see a 1.

For the deterministic version, in half the cases you’re going to evaluate all those bits when they’re in the “end” half.

If the bits are in the “front” half, or if the algorithm is random, I can’t figure out exactly how to solve the average number of bits to test, but it seems like the deterministic half should do on average about as well* as the nondeterministic one on a problem half the size, and in the worst case scenario it still does (n/4 + 1) bits, while the nondeterministic algorithm has to do (n/2 + 1) tests in the worst-case scenario unless I’m mistaken.

I mean, it does seem like the probability of hitting a bad case is low on average, but the worst case seems twice as bad, even though harder to hit.

I’m not sure how you got O(1); I got to a sum of reciprocals of squares up to k for the probability of the first 1 bit being the k-th tested, which sounds about O(1) on average, but my probabilities are rusty, is that the way?

(* of course, it’s easy to construct the bad case for the deterministic algorithm if you know which bits it tests first.)