That doesn't mean there aren't a few good points in there, but for someone who claims to abhor narcissism, this person sure likes to talk about themselves and their achievements a lot.

The development of male features in foetuses is triggered by a massive release of testosterone. The second stage of gendered differentiation, puberty, is similarly driven by the release of sex hormones in both men and women. Noticeably, adolescent boys with a malfunctioning endocrine system do not display typical male features (lowered voice, facial hair, etc), lesser tendency to masculine psychology (like risk taking and aggression), and don't develop sex drive (straight or gay) unless they start taking injections for life. It should be uncontroversial to say that sex and gender differences, both physiological and psychological, are tied to hormones.

Next up, the brain: the most complex and expensive organ we have, a complex system of feedback loops, again driven and regulated by secretions of hormones and peptides and so on. To say that evolutionary psychology is false and just anecdotes, you'd have to argue that, despite all the other visible changes that sex hormones make, somehow, the brain is entirely unaffected by all of that. That gender is mostly (or as some claim, entirely) a social construct, determined by something as subtle as a tone of voice or the presence of 'patriarchal gender roles'. Even though studies show children and teenagers are more affected by their peers' behaviours and attitudes than their parents'. Even though there is no culture where gender roles are truly swapped, and the 'Norwegian Gender Paradox' remains true.

Not only does the science show nature plays a significant role in the "nature vs nurture" debate on sex and gender, but policies based on denying it have failed to work.

I'm not an evo psychologist, or a biologist either. But it seems a lot more reasonable to me than what's coming from the supposed equality camp.

What I'm being skeptical about is that we can take that all the way out to surface characteristics and then make blanket statements, or that we can concoct untestable fables based in current evolutionary models and then use those to justify those blanket statements.

There certainly is gender dimorphism (the right term) in humans, but it's also very hard to separate genes/development, environment, and culture. It's also very hard to go from the cellular and hormonal level all the way up to, say, how people tend to behave in job interviews.

The reason I get so skeptical in these areas is that history teaches us that human beings will rationalize their prejudices using whatever system of belief they find around them. Historically that's usually been religion, hence it use to rationalize gender and racial stereotypes. Now we live in a scientific age (supposedly), so we should be on guard for pseudoscientific attempts to do the same with scientific-sounding language. Indeed the "social Darwinists" were on that beat a century ago already. So whenever I smell an attempt to explain or justify a social prejudice or a class system, my skeptic hat goes on.

That was rambling... need coffee..

"definitely rape" is so far off base, it's ridiculous. You're either gravely misinformed, or shilling.

I agree that Swedish definition of rape is a bit bonkers (e.g. I don't think that "sex without a condom" is rape, at most it's "fraud" or "lying"), but I think that sex with a sleeping person is always rape (unless you've been explicitly given consent that it's OK beforehand).

Randomization improves algorithms. This is so obvious to CS folks its taught in basic cs101 - the ones i'm familiar with are where you pick a random pivot element for quicksort, or say the one where you draw a circle inside a square & pick n random points inside the square, four times the number of points inside the circle will equal pi as n becomes large, or you resort to the miller rabin test for primality when you are doing those rsa calculator type problems where you pick keys for encryption, which will randomly sample some number of possible witnesses and call the number prime if none turn out to be witnesses, or using monte-carlo methods to compute integrals for functions for which no closed-form formula exists etc....there's like tons of examples where you introduce a little bit of randomness & it'll speed up your algorithm.

So this Page+Hong wrote a paper where they want you to randomly hire employees ( who they call problem-solvers or agents ), because diversity trumps ability ?! So if you have 1000 applicants, instead of hiring based on some metric like ivy-league/test-scores/IQ/github/whatever, you just hire randomly, because the randomness introduces diversity which trumps ability ?!! To prove this nonsensical point, they introduce an artificial math problem where agents proceeding randomly obtain the right answer, & not doing so gets you stuck. Ergo, diversity > ability. Sheesh.

Randomization improves algorithms.

This is a contentious claim. Random choices are actually very rarely the best - they are often good enough and versatile, but they are rarely the best.

For example, consider Monte Carlo integration. You get O(N^{-1/2}) convergence. If you use a deterministic set of points explicitly designed to have low discrepancy (aka "Quasi-Monte Carlo"), you can get O(N^{-1 + logarithmic stuff}) convergence.

http://www.chrisstucchio.com/blog/2014/adversarial_bandit_is...

Eliezer Yudkowsky also wrote a great critique of this issue, though I can't find it right now.

Choice quote: > As a general principle, on any problem for which you know that a particular unrandomized algorithm is unusually stupid - so that a randomized algorithm seems wiser - you should be able to use the same knowledge to produce a superior derandomized algorithm.

An interesting counterexample to this, an example which to me illustrates a very powerful aspect of randomness, is the monte-carlo revolution in computer Go (AI for the ancient Asian board game). For years, computer progress stagnated, as the techniques were mostly focused on encoding human knowledge into code. While most of this human knowledge is generally correct, it introduces significant bias in the way positions are evaluated. The way these rules-of-thumb interact is very hard to predict, and tree search algorithms are quite good at finding positions that are incorrectly evaluated. Because of this, the worst-case behavior of an evaluation function is much more important than it's best-case or average behavior.

Computers started making lots of progress when a new technique was used: rather than try to evaluate positions by a large set of heuristics, they were evaluated by playing random games. Go positions are quite hard to evaluate from simple rules of thumb, and this random game approach gives a much more balanced, long-term view of positions. And most importantly, there is much less bias.

Obviously, an entirely random game, with a uniform distribution over possible moves, is easy to improve upon. But computer go programmers noticed an interesting phenomenon: while certain types of knowledge incorporated into the random move distribution (to make it "more intelligent", as judged by a human) were helpful, others were not (even after taking into account the computational cost of adding the knowledge), and it wasn't always clear why. The same observation about heuristic evaluation noted above applied: having a balanced distribution of move choices, with a reasonable probabilistic lower bound of effectiveness, is more important than making an intelligent choice that is usually correct, but has unpredictable, extreme worst-case performance.

So we see that randomness does have an important property: it avoids the downside of "knowledge" that generally seems correct but can go horribly wrong in unexpected ways.

I don't know of a good writeup of this phenomenon. My understanding of it is mostly assembled from following informal discussions on the computer-go mailing list for several years. In a quick search through my gmail archives I can't find much on the subject, but here's an interesting post about related topics in the computer chess world (that incidentally doesn't talk about randomness, but illustrates well the benefits of avoiding bias): http://www.talkchess.com/forum/viewtopic.php?topic_view=thre...

Suppose you have a linear evaluation function - h(x) is the value of something. Suppose also the coefficients of h are all positive. Then you'll be right 75% of the time (averaged over all possible h, drawn uniformly from the unit simplex) if you just approximate h=[h1,h2,...] by u=[1,1,...,1].

http://www.chrisstucchio.com/blog/2014/equal_weights.html

So I agree with this claim - uniform distributions are fairly robust to errors. But I don't think that's particularly related to randomness - Monte Carlo is only needed to integrate the distribution.

It's also worth noting that adversarial situations (like Go or Chess) are considerably different than most other cases. In a true adversarial problem, there is no probability distribution - the opponent is omnipotent. The purpose of randomness is simply to reduce the power of the adversary's intelligence - in a completely random world, intelligence is useless.

> So I agree with this claim - uniform distributions are fairly robust to errors. But I don't think that's particularly related to randomness - Monte Carlo is only needed to integrate the distribution.

Ah, that's an interesting distinction, thanks. I'll have to think about this some more. But given a situation where exact integration is intractable (like chess or Go), I'm not too sure what the difference really is, because it is those cases (on first thought) where the uniform distribution is useful--if you can see to the end, you don't need to care about bias, right? I mean, "randomness" in the strictest sense is not really necessary; all these programs I speak of used deterministic pseudorandom generators of course. It's really just about ensuring lack of bias given finite sampling. I'm happy to hear your take on it though--you definitely seem to have a lot more knowledge of math/statistics/etc. than I do.

(That does remind me of another fascinating tidbit from the Go world: programmers noticed that using a low-quality PRNG, like libc's LCG rand(), produced significantly weaker players than more evenly-distributed PRNGs, even though it would seem that playing lots of random games of indeterminate length (with the PRNG called at least once per move) would not correlate at all with the PRNG's distribution.)

The adversarial-or-not issue is also good food for thought. I'm not convinced that it explains much in this case, though, since I believe most of these observations were made by playing computer-computer games with each program using very similar algorithms, or with old hand-tuned programs against the newer Monte-Carlo based programs.

Put it this way - suppose I can cook up a deterministic quadrature rule, e.g. quasi monte carlo or an asymptotic expansion. I assert that the quasi monte carlo will work just as well as monte carlo, probably better if convergence is faster.

If I'm right, this is a situation of "yay for uniform distributions". If I'm wrong, it's a "yay randomness" situation. It's nice to know which situation you are in - if I'm wrong, there is no point cooking up better deterministic quadrature rules.

Incidentally, LCG is known to be useless for monte carlo due to significant autocorrelation. So it's quite possible that people using LCG are incorrectly estimating their evaluation term.

Also for me, it's nice to know these things just for theoretical purposes and to enhance my understanding.

The better intuition is "randomisation fixes algorithms that get stuck for superficial reasons" - if you wrote that instead of the diversity thing as the intuitive conclusion of the Hong and Page theorem, you could get away with it in a theoretical CS survey paper or introductory textbook. The set of assumptions they make about the different \Phi would be fairly reasonable, for instance, if we were talking about a number of different flawed heuristics for a search problem.

     def pointInsideCircle = {val (z,w) = (0.5,0.5);val (x,y) = (math.random,math.random); if ((x-z)*(x-z)+(w-y)*(w-y) < 0.25) 1 else 0;}
     (1 to 1000).map{x=> pointInsideCircle}.sum*4/1000.0
     scala> 3.132
     (1 to 1000000).map{x=>pointInsideCircle}.sum*4/1000000.0
     scala> 3.141612

You can try Buffon's needle if you want something much faster.

I find the critique vastly more readable.

Indeed, when you try to explain something simply to people, they often won't believe that it's really that simple, since there's all those symbols!

The only good part is where the author criticizes the "doing good" cult vibe that's been substituting common sense at Mozilla more and more lately.

If you imply that women are inferior though, the statement has to be vigorously opposed. Imply that feminism isn't necessary, and the tumblr and twitter brigades will come out to shame and silence you, until you agree that women have it harder.

It's absurd behaviour for a group of people who supposedly believe women are equally capable as men. Once you figure this out, feminism makes a lot more sense. When they can blame men, they blame men. When fairness requires them to blame women, they blame 'society'.

I said perceived inferiority. I didn't say they are inferior.

I called it a personal attack because it's needlessly personal: it implies that the other person is an overwrought ("calm down") fanatical ("political correctness") bully ("police"). If that doesn't meet your definition of "personal attack", I'm happy not to quibble. Either way, though, it doesn't meet Hacker News' definition of civil, substantive discussion, and therefore is inappropriate here.

Eh. The pay gap is 12%. Please provide evidence this isn't real that is verifiable and not vague "look at the statistics". Which statistics?

Here's an article that cites several different studies. Granted, I cited the higher end, it could be lower, by all means make up your own mind. They also add the caveat here that it only applies to metropolitan areas, but this is the vast majority of the population nowadays.

>... in 147 out of 150 of the biggest cities in the U.S., the median full-time salaries of young women are 8% higher than those of the guys in their peer group. In two cities, Atlanta and Memphis, those women are making about 20% more. This squares with earlier research from Queens College, New York, that had suggested that this was happening in major metropolises. But the new study suggests that the gap is bigger than previously thought, with young women in New York City, Los Angeles and San Diego making 17%, 12% and 15% more than their male peers, respectively. And it also holds true even in reasonably small areas like the Raleigh-Durham region and Charlotte in North Carolina (both 14% more), and Jacksonville, Fla. (6%).

> As for the somewhat depressing caveat that the findings held true only for women who were childless and single: it's not their marital status that puts the squeeze on their income. Rather, highly educated women tend to marry and have children later. Thus the women who earn the most in their 20s are usually single and childless.

http://content.time.com/time/business/article/0,8599,2015274...

Once again: women and men who want families make different choices.

We read that article and came to two completely different conclusions. That may be due to having different contexts about approaching this conversation. This is a tech site, the OP was about tech, and I was talking about software/tech salaries.

"The holdout cities — those where the earnings of single, college-educated young women still lag men's — tended to be built around industries that are heavily male-dominated, such as software development or military-technology contracting. In other words, Silicon Valley could also be called Gender Gap Gully."

I interpret this as basically admitting its a problem in male dominated industries [which happens to be the case in tech and engineering in general].

"He attributes the earnings reversal overwhelmingly to one factor: education. For every two guys who graduate from college or get a higher degree, three women do."

You seem to have missed the entire point of the article. The reversal that is raising women's average wage is due to an education gap b/t men & women. It is not due to equal pay for equal experience/position/etc. that is the basis of the gap in the article I cited.

The pay gap I'm talking about is when a female software engineer is paid 12% less than when a male software engineer with roughly the same experience, education, ability, and position.

You are comparing your apples [overall pay equality across all industries and disciplines] to my oranges [pay equality by experience, education, ability, and position].

Do you now understand why I disagree with you, even reading the same articles? I understand where you are coming from but there shouldn't be a pay gap for the same job title.