Monthly Archives: August 2016

Prescription

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"Maybe my real problem is that I take myself too seriously—from my perspective, that other people don't take themselves seriously enough. Like I'm off in my corner going mad, unable to comprehend why, why doesn't the world understand that words mean things. But when you actually talk to people, their anticipations of experience are all just about as well-calibrated as mine; they're just really bizarrely cavalier about using words to mean whatever they feel like at the moment."

"So basically you're going mad over ... prescriptivism."

"I know, usually I'm not the type to get into linguistic prescriptivism debates, but I guess I had assumed that those were always about obscure things, like when to use comprised instead of composed. I wasn't expecting people to redefine a top-20 noun out from under my feet."

"And yet!"

Ineffective Deconversion Pitch

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Growing up in an ostensibly reform-Jewish household that didn't even take that seriously, atheism was easy for me, so I don't know how hard deconversion is, how much it hurts, or how much of one's entire conception of self is trashed in the process and can't be recovered.

As an atheist, it's tempting to say, "Look, it's not that bad: God doesn't exist exist, but you can still go to church and praise God and stuff if you want; it's just that there are benefits to being honest about what you're actually doing and why."

Somehow, I suspect that this is not a very convincing sell.

Applications to other topics are—as always—left as an exercise to the reader.

Quotations V

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MINUETTE: So, uh, what are you studying these days?
MOON DANCER: Science, magic, history, economics, pottery. Things like that.
MINUETTE: Yowza! You planning on being a professor or something?
MOON DANCER: No.
MINUETTE: So you're just ... studying!
MOON DANCER: Can I go now?

—My Little Pony: Friendship Is Magic, "Amending Fences"

However, this corresponds to a general pattern of causal relationships: observations on a common consequence of two independent causes tend to render those causes dependent, because information about one of the causes tends to make the other more or less likely, given that the consequence has occurred. This pattern is known as selection bias or Berkson's paradox in the statistical literature (Berkson 1946) and as the explaining away effect in artificial intelligence (Kim and Pearl 1983). For example, if the admission criteria to a certain graduate school call for either high grades as an undergraduate or special musical talents, then these two attributes will be found to be correlated (negatively) in the student population of that school, even if these attributes are uncorrelated in the population at large. Indeed, students with low grades are likely to be exceptionally gifted in music, which explains their admission to the graduate school.

—Judea Pearl, Causality

"It would be nice if implementation languages provided extensible string-indexable arrays as a built in type constructor, but with the exception of awk, Perl, and a few others, they don't. There are several ways to implement such a mechanism.

—Modern Compiler Design by Dick Grune, Henri E. Bal, Ceriel J. H. Jacobs, and Koen G. Langendoen (2000)

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