Comment author: Lumifer 04 June 2014 05:45:47PM *  5 points [-]

I think half an hour to go and vote is probably more effective than half an hour of loudly proclaiming

The problem is that the party, when considering whether to change policies, has no idea who voted for/against it for which reason. All it knows is that it gained or lost certain number of voters (of certain demographics) in between two elections.

If issue Z is highly important to you and you vote on the basis of the party's attitude to it, how does the party know this if the only thing you do is silently drop your ballot?

Comment author: Oscar_Cunningham 04 June 2014 06:52:34PM 2 points [-]

Ah yes, you're right. That clearly weakens the effect of voting substantially.

Comment author: Lumifer 04 June 2014 02:47:41PM 5 points [-]

So even though your vote won't change who wins the election, you will still shift the policies of the parties towards your own views.

You don't achieve this by voting -- you achieve this by loudly proclaiming that you will vote on the basis of issues A, B, and C.

Comment author: Oscar_Cunningham 04 June 2014 05:31:55PM 1 point [-]

I think half an hour to go and vote is probably more effective than half an hour of loudly proclaiming, but I can't think of a test for this. Perhaps look at elections where the vote showed that what people wanted was different from what the media said people wanted, and then see which way the parties moved.

Comment author: SolveIt 04 June 2014 01:43:23AM 1 point [-]

Today is election day here in Korea. Although I have voted, I have yet to see a satisfactory argument for taking the time to vote. Does anyone know of good arguments for voting? I am thinking of an answer that 1. Does not rely on the signalling benefits of voting 2. Does not rely on hyperrational-like arguments.

Comment author: Oscar_Cunningham 04 June 2014 09:49:31AM 1 point [-]

Political parties will change their policies to capture more voters. So even though your vote won't change who wins the election, you will still shift the policies of the parties towards your own views.

Comment author: Gunnar_Zarncke 29 May 2014 04:37:10PM 0 points [-]

I agree what many infographics (the contraction is the first hint) are often more beautiful than informational.

But yours was a general remark. Did you mean it to imply that the idea isn't good or just my particular example?

Comment author: Oscar_Cunningham 30 May 2014 10:43:16AM 0 points [-]

I think that good graphics illustrating some point about rationality would be a really cool thing to have in the quotes thread.

Comment author: Gunnar_Zarncke 28 May 2014 08:24:56PM 1 point [-]

What do you think about using visualizations for giving "rational" advice in a compact form?

Case in point: I just stumbled over relationtips by informationisbeautiful and thought: That is nice.

This also reminds me of the efficiency of visual presentation explained in The Visual Display of Quantitative Information by Tufte.

And I wonder how I might quote these in the Quotes Thread...

Comment author: Oscar_Cunningham 29 May 2014 12:33:31PM 1 point [-]

Modern "infographics" like those by informationisbeautiful are extremely often terrible in exactly the ways that Tufte warns against. They are often beautiful, but rarely excel at their original purpose of displaying data.

Comment author: Toby_Ord 28 May 2014 11:47:52AM 3 points [-]

This is quite possibly the best LW comment I've ever read. An excellent point with a really concise explanation. In fact it is one of the most interesting points I've seen within Kolmogorov complexity too. Well done on independently deriving the result!

Comment author: Oscar_Cunningham 28 May 2014 02:54:49PM 1 point [-]

Thanks!

Comment author: NancyLebovitz 23 May 2014 03:59:08PM 3 points [-]

Slatestarcodex isn't loading for me. It's obviously loading for other people-- I'm getting email notifications of comments. I use chrome.

Anyone have any idea what the problem might be?

Comment author: Oscar_Cunningham 23 May 2014 04:45:39PM 0 points [-]

It's not loading for me either, nor for downforeveryoneorjustme.com. I use firefox.

Comment author: Vaniver 23 May 2014 08:57:14AM *  16 points [-]

I'm an expert in a neighboring field: numerical optimization. I've seen lots of evidence for Jaynes's impression that for any algorithm that uses randomness, one can find a deterministic technique (which takes thought) that accomplishes that goal better. (For example, comparing genetic algorithms, simulated annealing, and tabu search, the last has an deterministic memory mechanism that gives it the ability to do both diversification and intensification, with much more control than either of the other two.) Random methods are employed frequently because to do otherwise would require thought.

As for the debate, to me it looks like it was over terminology. To illustrate, let me label three different cases: the 'benign' case, where the environment is assumed to be dumb (i.e. maxent priors are reasonable), the 'adversary' case, where the environment is assumed to be an agent that knows your algorithm but not your private source of randomness, and the 'omega' case, where the environment is assumed to be an agent that knows your algorithm and your private source of randomness.*

Eliezer thinks the phrase 'worst case analysis' should refer to the 'omega' case. Scott thinks that the 'adversary' case is worth doing theoretical analysis on. The first is a preference that I agree with,** the second seems reasonable. I think Silas did a good job of summarizing the power that randomness does have:

Which is why I summarize Eliezer_Yudkowsky's position as: "Randomness is like poison. Yes, it can benefit you, but only if you use it on others."

*There's also a subtlety about solving the problem 'with high probability.' For a deterministic algorithm to have a chance at doing that, it needs to be the benign case- otherwise, the adversary decides that you fail if you left them any opening. For a randomized algorithm, the benign and adversary cases are equivalent.

**One of the things that Scott linked--when Monte Carlo goes wrong--is something that shows up a lot, and there's a whole industry built around generating random numbers. For any randomized algorithm, the real worst case is that you've got a PRNG that hates you, unless you've paid to get your bits from a source that's genuinely random, and if omega was the case that came to mind when people said 'worst case,' rather than adversary, this would have been more obvious. (vN got it, unsurprisingly, but it's not clear that the median CS researcher did until they noticed the explosions.)

Comment author: Oscar_Cunningham 23 May 2014 03:36:31PM *  5 points [-]

I suppose that there are really 6 different complexities. Let x be the random numbers you pick and let y be the input you get. Let f(x,y) be the time your algorithm takes on that instance. We can maximise or take the expected value for each of x and y, in either order.

  1. E_x E_y f(x,y) (Average case complexity; Eliezer's favourite.)
  2. max_y E_x f(x,y) (Worst case complexity; Scott's favourite.)
  3. E_x max_y f(x,y) (facing an adversary who knows what random numbers you are going to get.)
  4. max_x E_y f(x,y) (you get an input at random, your random number generator tries to give you bad numbers but doesn't know what case you'll get.)
  5. E_y max_x f(x,y) (you get an input at random, your random number generator gives you the worst numbers for that input.)
  6. max_x max_y f(x,y) ('Omega' case complexity; your environment and your random number generator conspire to kill you.)

There's not one best way to put a distribution on the possible inputs, so computer scientists who don't want to do this can only look at 2, 3, and 6. Then problems that can be solved in poly time according to 2 are the ones in BPP, and those that can be solved in poly time according to 6 are the ones in P (you don't use your RNG if it's trying to kill you). I don't know if the corresponding class for 3 has a name, but it doesn't seem very interesting.

EDIT: You also don't want to use your RNG if you're being judged by 4 or 5, so these both correspond to the complexity class where you judge by average time and you don't have a RNG. Then 1 corresponds to the complexity class where you're judged by average time and you do have an RNG. These complexity classes are known to be the same, since as Scott says

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.

Comment author: V_V 23 May 2014 02:48:38PM 7 points [-]
In response to comment by V_V on Can noise have power?
Comment author: Oscar_Cunningham 23 May 2014 03:15:54PM 2 points [-]

Wow, they say exactly the same things I do, right down to using quicksort as an example.

Comment author: Oscar_Cunningham 23 May 2014 11:37:41AM *  12 points [-]

When algorithms have bad worst cases, these cases are often the inputs with a particular structure. For example if you use quicksort without randomising your pivots then you have an average case complexity of n.log(n) but a worst case complexity of n^2. The worst inputs are the ones where the list is already partially sorted. The "partially sorted" is the kind of structure I'm talking about.

If we expected to see inputs with some kind of structure (but we didn't know which kind of structure) then we might well be worried that we would get inputs with precisely the structure that would hit our worst case.

So suppose we do indeed have a prior over our inputs, but that this is a Solomonoff prior. Among all the inputs of length n we expect to see each one with probability proportional to exp(-k) where k is its Kolmogorov complexity. Then most of the strings will have complexity n and so probability ~ exp(-n). However our worst case string will have complexity at most m+log(n) where m is the length of our algorithm's code. This is by virtue of its description as the worst case for that algorithm among the strings of length n. So we will anticipate getting the worst case input with probability ~ exp(-m)/n. So if our worst case complexity is g(n) then our average case complexity is O(g(n)/n).

Under a Solomonoff prior the worst cases and average cases are the same! (up to a polynomial)

EDIT: So if there are cases where randomisation gives an exponentially better worst case complexity, then we won't be able to derandomise them under the Solomonoff prior.

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