HN user
There are an awful lot of referrer links in this article. That's not a bad thing by itself, but it always makes me ask the question - Did this person write the article they wanted to write and then add referrer links, or did they decide to put in a load of referrer links and then build an article around it?
“It is difficult to get a man to understand something, when his salary depends on his not understanding it.” -- Upton Sinclair
http://www.goodreads.com/quotes/21810-it-is-difficult-to-get...
That article is wrong on quite a few key points, and misleading on others.
Mark will deduct the fair value of his gift to his foundation from his taxable income in the year he makes the donation.
No he won't. The new foundation is an LLC, not a charitable foundation, so he's not eligible to take a tax deduction on it. And even if he was -
1. Giving away 99% of your wealth to save tax on the income from the other 1% is a really dumb way to save money, and
2. They don't have very much taxable income anway - just their salaries, and any capital gains they realise if they sell any Facebook stock.
Mark Zuckerberg will transfer ownership of his Facebook stock without paying capital gains taxes.
Well, yes - you pay capital gains taxes when you sell something and realise a profit. Since he is not selling his shares, he is not realising a profit, and therefore is not liable to pay taxes on them. Nothing wrong with that!
Presumably you think that the people running the study didn't think of this?
Here's a quote from a New York Times article about the project -
In the second year of the tournament, Tetlock and collaborators skimmed off the top 2 percent of forecasters across experimental conditions, identifying 60 top performers and randomly assigning them into five teams of 12 each. These “super forecasters” also delivered a far-above-average performance in Year 2. Apparently, forecasting skill cannot only be taught, it can be replicated.
So the answer to the question "What is the probability any one member of the group correctly guesses the result of the next coin toss?" appears to be "reasonably high".
My interpretation in layman's terms of what this paper has proved -
Take an infinite sequence of +1 and -1, for example
1, 1, -1, 1, -1, -1, -1, 1, 1, -1, ...
1 (stopping after 1 term)
1, 1 (stopping after 2 terms)
1, 1, -1 (stopping after 3 terms)
1, 1, -1, 1 (stopping after 4 terms)
1, 1, -1, 1, -1 (stopping after 5 terms)
The challenge is to find an evenly-spaced subsequence with as large a discrepancy as possible. For example, in a given sequence, can you find an evenly-spaced subsequence with a discrepancy of 10? of 100? of a million?
The paper has (apparently) proved that for any sequence, there is no upper limit to the discrepancies of evenly spaced subsequences, i.e. no matter how large the discrepancy of a subsequence you have found, there is always one larger.
The amazing thing about this result, to me, is the fact that it holds for any sequence of +1 and -1. Even if you try to engineer a sequence whose subsequences all have very small discrepancy, in some sense "there isn't enough room". You are always doomed to come up with a sequence containing evenly-spaced subsequences of arbitrarily large discrepancy.
Interestingly, with Joe Biden in the range (10%, 13.5%) to win the nomination, and (7.7%, 8.3%) to win the Presidency, the lowest his electability could be is
7.7% / 13.5% = 57%
8.3% / 10.0% = 83%
I can think of a few explanations -
1. Biden really is a lot more electable than Hillary Clinton
2. Both candidates have electability at the low end of their range (around 57%).
3. The market is wrong, i.e. they are systematically underrating Biden's chance of winning the nomination (and overrating Clinton's) or overrating Biden's chance of winning the election (and underrating Clinton's) or both.
There is no arbitrage, but if you believe 3, there might be a good profit to be made in expectation by backing Biden to win the election, but Clinton to win the presidency (you wouldn't hold it through to 2016, but take off the bet as soon as the odds come back to something that looks more plausible).
I haven't done the analysis to see if it's still worth it after trading costs, but maybe someone else wants to.
That would be equivalent to not ticking any of the boxes.
I checked them after I'd submitted the survey. The only real words that I hadn't ticked were "regolith" (I was almost sure it was a real word, but I didn't know the meaning of it), "phoropter" (I believed it could be a piece of engineering terminology, but again didn't know the meaning) and "peristeronic" and "apricity" (I would have given better than evens that these were made up).
=== SPOILER ALERT! ===
The most interesting question, to me, is the one about which words you know the meaning of.
About half of them aren't real words. I assume this question is used partly as a gauge of vocabulary (how many of the real words do you recognize) and partly of honesty (how many of the fake words do you claim to recognize).
Remember that a hedge fund only sees 20% of the profit that it generates on behalf of its investors, and a large part of that goes into staffing and infrastructure costs, not to mention that quant traders like to be paid sizable bonuses (and therefore would not want to work on a trade with a small upside).
I find it extremely unlikely (almost inconceivable, in fact) that a hedge fund would divert 10 researchers to work on a trade with $20m of potential upside.
If you back test over the past 5 years then you are only testing your model against a huge bull market.
If your model is long as often as it is short (either cross-sectionally or in a time series sense) then this is less likely to be a problem.
A far bigger source of error for inexperienced researchers is incorrectly accounting (or not accounting at all) for trading costs, financing costs, roll costs, liquidity constraints, data delays, market impact etc.
For those who didn't get it - mjd is the author of the blog post linked to. Although he's also right that it's a stupid comment that deserves downvotes :)
Like I said, "you will never be able to completely avoid advertising without taking yourself out of mainstream society" and "second-order advertising is much harder to deal with".
I don't claim to be immune to advertising, or know how to avoid all of it! But knowing you are susceptible to advertising is the first step towards not letting it influence you (too much).
I agree completely. I think you have a moral obligation to yourself to avoid advertising as much as is reasonably possible. This means not watching television (unless you're paying to watch it free of advertisements), not reading publications that are heavily supported by advertising (e.g. most magazines) and using an ad blocker everywhere on the web.
You will never be able to completely avoid advertising without taking yourself out of mainstream society, but you can avoid the most insidious parts, and you can try very hard to condition yourself against the rest of it.
The "second-order" advertising (e.g. critic recommendations, word of mouth) is much harder to deal with, especially if you believe that some people have good, unbiased, not-unduly-influenced opinions that you will benefit from listening to. My only recommendation is to curate the people whose opinions you listen to very carefully, and think hard about who they might be (possibly unwittingly) influenced by.
I am sympathetic to the argument that advertising pays for many of the things I like, particularly on the web. But I don't think that argument is compelling enough for it to be worth handing over control of my head.
Of course, advertising is only one factor, though it is probably the most important factor. Other systems competing for a share of your mind include religions, political parties and/or systems of political thought, philosophical systems, programming languages and/or communities (eg functional vs. object oriented), sports teams, national identities, racial identities, and more.
You may want to allow some of these access to a share of your mind (e.g. many people enjoy supporting a sports team, even when the rational part of their brain knows that their sports team isn't inherently better than any other). But for the most part, I think it's better to avoid falling into these traps.
The best exposition I can recall is one of Paul Graham's earlier essays, "Keep Your Identity Small"[0]. I would probably argue a similar point, but phrase it differently - keep your identity broad. Instead of thinking of yourself as a "Ruby programmer" or a "functional programmer" it is better to think of yourself as a "programmer" (and even better not to think of yourself as a programmer at all!). Instead of thinking of yourself as American, or Chinese, or as black, or white, try to think of yourself as a human. The broader you can make your identity, the less chance you have of accidentally falling prey to any of the theories competing for a space in your head.
As I said, in Haskell "unquantified type variables are implicitly universally quantified" i.e. type variables (which are always lowercase in Haskell, to distinguish them from concrete types which are uppercase) are always generic.
So it's true that in the Swift examples, you would need a convention to distinguish type variables from concrete types, or else you need to explicitly mark them as generic.
It's not clear to me why it's necessary to include <T, U> at the start of the function definition. In Haskell, unquantified type variables are implicitly universally quantified, i.e. you assume that the signature must be valid for any types T and U.
That's the only thing I think could be substantially improved, though. Personally I find the style where you separate the type signature from the function definition easier to read, for example
How about
<^> : (T -> U) * T? -> U?
<^> (f, a) {
return a.map(f)
}
<^> : (T -> U) * T? -> U?
<^> (f, a) -> a.map(f)
(<^>) :: (t -> u) -> Maybe t -> Maybe u
(<^>) = fmap
<^> : (t -> u) -> t? -> u?
<^> = fmap
This is actually very cool! It's interesting to play around with strategies for using your retirement income, e.g.
0.02 * [Savings]
Responds "Yes" to
9007199254740990, 9007199254740991, 9007199254740992
9007199254740991, 9007199254740992, 9007199254740993
> parseInt('9007199254740992')
9007199254740992
> parseInt('9007199254740993')
9007199254740992
function l(){
var a=h.exec(m[1]),f=null,g=null,n=null;
return a&&(null!==a[1]&&a[1]&&(f=parseInt(a[1],10)),
null!==a[2]&&a[2]&&(g=parseInt(a[2],10)),
null!==a[3]&&a[3]&&(n=parseInt(a[3],10))),
new e(f,g,n)
}
[0] http://a1.nyt.com/assets/interactive/20150612-151638/js/foun...
I don't think the satire was particularly subtle!
Well, I'm glad that Londoners have collectively decided to tell the rebranders where to stick it. I've lived in London for ten years and have never heard someone refer to "Midtown" or "Noho". Urgh.
I had drinks with one of the $1 billion "unicorn" CEOs last night, in a trendy Noho bar in London.
Where the hell is "Noho"? Is this some ridiculous attempt to Americanize London by trying to rename parts of it after bits of New York?
If the author means the area north of Soho, I am happy to point him in the direction of any map of London, where he will see that area labeled "Fitzrovia".
Also, who points out that they were in a 'trendy' bar without covering the word 'trendy' in a fetid, glistening layer of sarcasm? Business Insider journalists, I guess?
My favorite analysis of Buffon's needle is this one, which pulls out the correct probability of intersection without any calculus -
Consider a straight needle of length L dropped onto lines spaced 1 unit apart. It is clear that the expected number of intersections is proportional to L. If you joined two needles of length L/2 (possibly at an angle) the expected number of intersections is still L, by additivity of expectation (note that we don't need independence). By induction, the shape of the needle doesn't matter, and the expected number of intersections is proportional to L.
We can work out the constant of proportionality by considering a needle shaped like a circle of radius 0.5, which has circumference pi, and is guaranteed to intersect the lines in two places. Therefore
2 = C * pi
Now for a straight needle of length 1, we can never have more than 1 intersection. Therefore the probability of intersection is the same as the expected number of intersections, which is seen to be
P = (2 / pi) * 1 = 2 / pi