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What the argument is really about is whether there is a problem to fix. The diversity numbers clearly indicate that there is. You can't draw a normative conclu

What the argument is really about is whether there is a problem to fix. The diversity numbers clearly indicate that there is. You can't draw a normative conclusion from a positive claim alone. You additionally need some normative axiom. No fact can tell you what you should do. In the discussion you cite, the disagreeing parties have different normative axioms. Some people believe in statistical equality as an end goal, whereas others have equal treatment. If you believe in statistical equality (as I think you do), then the facebook numbers are a problem to fix. If you believe in equality of treatment (as I suspect the others do), then you can draw no conclusion - the numbers do not directly imply anything about equal treatment. Similarly, consider a debate between a "meat is murder" activist and a carnivore. No one disagrees that animals are killed - the only disagreement is over whether that's a bad thing.

aristus 12y ago
A good example of the debate and tactics I'm describing. :) If you agree that women are not somehow less mentally capable than men, then the fact that technical jobs are 85% dominated by men should be a concern. Not just for "normative" reasons or "statistical equity", but simply that there are a lot of talented people being excluded for one reason or another. If not, you should explain WHY not. Anomalies demand explanation. You dismiss the numbers because of an assumed equality of treatment and saying, well, I don't see anything therefore it's not a problem... and if it is, it's somebody else's problem. That's exactly the mismatch of rigor I'm talking about. You put the burden on others to "prove" that something is amiss, then when they do, demand more.
- yummyfajitas 12y ago
  Claiming that "a lot of talented people being excluded for one reason or another" is a positive claim. It's merely a fact about the world. If you are against this, that's a normative axiom (or possibly a normative conclusion derived from another normative axiom). As for the undue rigor involved, it's you who seems to be demanding a lot: you demand proof that men and women are statistically unequal merely to admit it as a possibility. More precisely, let A = "women are unequally treated" and B = "women are not statistically equal to men" (at the point of application for Facebook employment). The diversity numbers imply A || B. You are assuming that B is false because there is no proof it is true, and therefore concluding A must be true (since A || B and !B => A). But strangely, you are not asserting that A is false without a rigorous proof that A is true (which would imply B). That's the fundamental asymmetry that Scott Alexander is talking about. The people you disagree with are merely saying "A || B, we don't know which."
  - aristus 12y ago
    "We don't know which", and will stop there. That's fine. All I ask is you be explicit about where you stand: on the side of the status quo, not wrapped in the flag of math as you would like pretend, but simply content with the way things are, and utterly incurious about how to change it. [edit] For fuck sake - if you had noticed a 50% imbalance in CPU usage on your server farm, surely you'd spend some time trying to understand it. What the hell is wrong with so many people who are busy inventing facile excuses and dredging up mathy reasons why to NOT recognize the problem as a problem and looking into why it might exist?
    - yummyfajitas 12y ago
      If I narrowed the source of CPU usage down to this block of code: for { x <- f() y <- g() } yield (x+y) I would not immediately conclude that f() is the source of the problem. That's what you are advocating doing. And if someone suggests g() might be the problem, you are assuming they are wrong and demanding an excess level of rigor from them. That's exactly what Scott Alexander is warning against. [edit: I'm sticking to epistemology. I'm not hijacking the thread to discuss gender.]
      - aristus 12y ago
        Still going on that vein, eh? If you notice a global imbalance in CPU usage, you should be totally focused on understanding the cause, not inventing excuses why g() is not the problem or why people investigating f() are wrong. I frankly don't care whether it's a pipeline problem or a filter problem, or both. I have more than a decade of performance work under my belt; I'm familiar with the usual arguments. What makes me take notice is that so much effort is put into arguing why there isn't a problem at all, or that people who say there is a problem are somehow mistaken. Seriously. Do you think that women are intellectually flawed? Yes or no? If yes, I'd love to hear why. If no, what is your best guess why they are underrepresented? No tricks, no shifting of levels. Why do you think it's so? "I don't know, let's find out together" is a perfectly good answer.
        12y ago
- deleted 12y ago
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aristus 12y ago

A good example of the debate and tactics I'm describing. :) If you agree that women are not somehow less mentally capable than men, then the fact that technical jobs are 85% dominated by men should be a concern. Not just for "normative" reasons or "statistical equity", but simply that there are a lot of talented people being excluded for one reason or another. If not, you should explain WHY not. Anomalies demand explanation. You dismiss the numbers because of an assumed equality of treatment and saying, well, I don't see anything therefore it's not a problem... and if it is, it's somebody else's problem. That's exactly the mismatch of rigor I'm talking about. You put the burden on others to "prove" that something is amiss, then when they do, demand more.

yummyfajitas 12y ago
Claiming that "a lot of talented people being excluded for one reason or another" is a positive claim. It's merely a fact about the world. If you are against this, that's a normative axiom (or possibly a normative conclusion derived from another normative axiom). As for the undue rigor involved, it's you who seems to be demanding a lot: you demand proof that men and women are statistically unequal merely to admit it as a possibility. More precisely, let A = "women are unequally treated" and B = "women are not statistically equal to men" (at the point of application for Facebook employment). The diversity numbers imply A || B. You are assuming that B is false because there is no proof it is true, and therefore concluding A must be true (since A || B and !B => A). But strangely, you are not asserting that A is false without a rigorous proof that A is true (which would imply B). That's the fundamental asymmetry that Scott Alexander is talking about. The people you disagree with are merely saying "A || B, we don't know which."
- aristus 12y ago
  "We don't know which", and will stop there. That's fine. All I ask is you be explicit about where you stand: on the side of the status quo, not wrapped in the flag of math as you would like pretend, but simply content with the way things are, and utterly incurious about how to change it. [edit] For fuck sake - if you had noticed a 50% imbalance in CPU usage on your server farm, surely you'd spend some time trying to understand it. What the hell is wrong with so many people who are busy inventing facile excuses and dredging up mathy reasons why to NOT recognize the problem as a problem and looking into why it might exist?
  - yummyfajitas 12y ago
    If I narrowed the source of CPU usage down to this block of code: for { x <- f() y <- g() } yield (x+y) I would not immediately conclude that f() is the source of the problem. That's what you are advocating doing. And if someone suggests g() might be the problem, you are assuming they are wrong and demanding an excess level of rigor from them. That's exactly what Scott Alexander is warning against. [edit: I'm sticking to epistemology. I'm not hijacking the thread to discuss gender.]
    - aristus 12y ago
      Still going on that vein, eh? If you notice a global imbalance in CPU usage, you should be totally focused on understanding the cause, not inventing excuses why g() is not the problem or why people investigating f() are wrong. I frankly don't care whether it's a pipeline problem or a filter problem, or both. I have more than a decade of performance work under my belt; I'm familiar with the usual arguments. What makes me take notice is that so much effort is put into arguing why there isn't a problem at all, or that people who say there is a problem are somehow mistaken. Seriously. Do you think that women are intellectually flawed? Yes or no? If yes, I'd love to hear why. If no, what is your best guess why they are underrepresented? No tricks, no shifting of levels. Why do you think it's so? "I don't know, let's find out together" is a perfectly good answer.
      - 12y ago
deleted 12y ago
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