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Posts tagged with causality

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Discussing the first year of life.

  • In-born aptitudes include
    • theory of other peoples’ minds (looking at where they’re looking, show surprise when they know something they “shouldn’t”) (this gives some idea of what autistic people go through—they lack a skill that one-year-olds have!)
    • animate versus inanimate things—causality, essentially

Infants do reason. They just have less knowledge than adults.

Brain hemispheres act the same at birth but begin to specialise over time. (So could there be an alien environment where human babies would specialise their brains along different lines?)

There’s no one thing that makes the human brain superior to other animals’ brains (at least not that we’ve found). It’s thought to be the interaction of various factors—as well as our long developmental period—that make us able to build rocket ships, paint potato eaters, invent radio and discuss these things on it.

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(Source: BBC)




The Bechdel Test

Does a film contain

  • two named females
  • who talk to each other
  • about something other than a man?
 

This seemingly low bar for female inclusion fails for a surprisingly high fraction of media. Even some excellent films, like The Godfather, fail it.

The Godfather. Can you remember what the actual final shot of the film is? Some people are surprised that the POV is on Kay from inside Michael’s room - her anguished face is blocked out by the closing door. The preceding shot is her looking in as men greet the new godfather. Ultimately the final shot is saying we are with Michael now in that room, and he/we are part of the closing of the door. A great, symbolic final shot. The first of three perfect final shots in the series. A

(You could argue that female exclusion is a theme of The Godfather, but still wouldn’t it have been interesting to view some of the wives’ and daughters’ thoughts to each other about the boys’ mobster behaviour? This isn’t asking for the movie to be about women, just to feature their speech.)

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The Bechdel test is interesting mathematically because it is a global non-local test. Not every movie needs to pass for “things to be good” but if too many movies fail then things are not good.

Tribar

You could also view the Bechdel test as a vague or smudged boundary condition. Like in sensitivity analysis (in linear programming) where you nudge the boundary planes with a slack vector to see how the system responds. We could perturb the definition of the test, and as we change the criteria or interpretation more or fewer movies will pass. But the test makes its point whether we interpret it loosely or stringently, so we could consider it a suite of boundaries rather than a single, crisp boundary.

Individual playwrights can write whatever they want. Blue Lagoon with two boys? Be my guest. An all-white cast in a story set in rural Sweden circa 1320? Makes sense. Nju Bao (in 炮打双灯) isolated without female counsel in a man’s world? Appropriate. But when the Bechdel test fails en masse something insidious is going on. Which focus group told film investors that audiences hate seeing women talk to each other? Who went through all the scripts and changed all the female names to male ones? I’m guessing no-one.

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Sexism, racism, and so on are often discussed on a case-by-case basis. Was this or that action sexist|racist|etc on its own? But not every property can be observed at a zoomed-in level. Some properties are only visible at a systemic or macro level.

As a side note, the frequent failure of the Bechdel test also argues, via modus tollens, against a certain kind of “markets will fix things” logic. I would think that economic forces would incent film producers away from being so exclusionary. Aren’t Hollywood executives leaving massive amounts of money on the table by working so assiduously to make sure women are only faces, bodies, and tropes? But yet, count the number of movies that fail this basic inclusivity test. Even though movies are a $X billion industry (therefore locking in a few percent of audience is worth a lot in absolute terms and ∴ worth the time to look at), they still frequently exclude minority perspectives.

Here are some stories that fail the Bechdel test:

  • Bladerunner
    image
  • Red Firecracker Green Firecracker (炮打双灯)
  • Amélie
    image
  • The Graduate
    image
  • King of California
    image
  • The Last Emperor
  • The Godfather
  • The Quiet American [fails for women and for Vietnamese]
  • The Wrestler
    image
  • Dr Strangelove

and here are some that pass the Bechdel test:

  • Star Wars: Clone Wars (both)
    image
  • Firefly
  • Scream
  • Magnolia
    image
  • A Streetcar Named Desire
  • Kill Bill

(por femfreq.tumblr.com)







  • The shape of the continents depends on the global temperature. (Cold locks ice in polar caps.) Google “Morse theory”.
  • The price of housing always rises, until it doesn’t.
     
  • You develop a system of habits to discipline yourself; maxims for self-motivation; then the working world changes on you. Loyalty is no longer rewarded. Hard work is less valued than the ability to make PlentyOfFish.com.
  • For years the normal trading range of [insert spread, instrument, or security] is X, until one day sufficiently many (external) parameters shift. The market changes and you see a 20-sigma event. Heroes only.

  • Whoever coded your profile website (chi.mp, flavors.me, tumblr), wrote a route that takes a string as parameter. Entering the name isomorphismes into this function fetches this webdata. Entering your name fetches your webdata. All part of one and the same formula.
  • The Lotka-Volterra equations of a large ecosystem, dancing as the sliders shift around in their hypercube. Death and life hang in the balance. And it’s literally a balance. If the fulcrum moves so far that the lever hits the ground, a species will either become extinct or overpopulate the ecosystem (like an algal bloom)—either phase change being irreversible. (Er, at least anti-entropic.)
     
  • You think you know yourself, until you step into a new context—new country, new career, new city—and latent aspects of you become dominant.

    Who was I before? If I was her then and am this now, what is the underlying me?

    Self as a function of circumstance. Perhaps just as constant at root, but reactive; responsive; springy; primed for change.




Contrary to common folklore, causal relationships can be distinguished from spurious covariations using inductive reasoning.

Judea Pearl, Causality





You’ve run the regression.  You see the t's, the β's, and the p's.  But what do they mean?  Don’t panic.  This book will tell you.

[T]he estimators in common use almost always have a simple interpretation that is not heavily model dependent….  A leading example is linear regression, which provides useful information about the conditional mean function regardless of the shape of this function.  Likewise, instrumental variables estimate an average causal effect for a well-defined population even if the instrument does not affect everyone.

Hooray!

You’ve run the regression.  You see the t's, the β's, and the p's.  But what do they mean?  Don’t panic.  This book will tell you.

[T]he estimators in common use almost always have a simple interpretation that is not heavily model dependent….  A leading example is linear regression, which provides useful information about the conditional mean function regardless of the shape of this function.  Likewise, instrumental variables estimate an average causal effect for a well-defined population even if the instrument does not affect everyone.

Hooray!




Anscombes quartet
The four data sets are different, yet they have the same “line of best fit” as computed by ordinary least squares regression.

Anscombes quartet

The four data sets are different, yet they have the same “line of best fit” as computed by ordinary least squares regression.


hi-res




extrapolation and interpolation
The most important lesson I learned from this book:  regression is reliable for interpolation, but not for extrapolation.  Even further, your observations really need to cover the whole gamut of causal variables, intersections included, to justify faith in your regressions.
Imagine you have two causal variables, A and B, that are causing X.  Maybe your data cover a wide range of observations of A — some high, some low, some in-between.  And you have, too, the whole gamut of observations of B — high, low, and medium.  It might still be the case that you haven’t observed A and B together (not seen ).  Or that you’ve only observed them together (not seen ).  In either case, your regression is effectively extrapolating to the other causal region and you should not trust it.
Let’s keep the math sexy.  Say you meet an attractive member of your favourite sex.  This person A) likes to hunt, and B) is otherwise vegetarian.  Your prejudices  are that you don’t like hunters () and you do like vegetarians ().  By comparing the magnitudes of these preferences, you deduce that you should not get along with this attractive person, because the bad A part outweighs the good B part.

However, since you haven’t observed both A and B positive at once, your preconceptions are not to be trusted.  Despite your instincts , you go out on a date with Mr or Ms (A>0, B>0) and have a fantastic time.  Turns out there was a positive interaction term in the  range, it also correlates positively with the noise (it wasn’t noise, just unknown knowledge), and you’ve found your soul mate.

extrapolation and interpolation

The most important lesson I learned from this book:  regression is reliable for interpolation, but not for extrapolation.  Even further, your observations really need to cover the whole gamut of causal variables, intersections included, to justify faith in your regressions.

Imagine you have two causal variables, A and B, that are causing X.  Maybe your data cover a wide range of observations of A — some high, some low, some in-between.  And you have, too, the whole gamut of observations of B — high, low, and medium.  It might still be the case that you haven’t observed A and B together (not seen ).  Or that you’ve only observed them together (not seen ).  In either case, your regression is effectively extrapolating to the other causal region and you should not trust it.

Let’s keep the math sexy.  Say you meet an attractive member of your favourite sex.  This person A) likes to hunt, and B) is otherwise vegetarian.  Your prejudices  are that you don’t like hunters () and you do like vegetarians ().  By comparing the magnitudes of these preferences, you deduce that you should not get along with this attractive person, because the bad A part outweighs the good B part.

However, since you haven’t observed both A and B positive at once, your preconceptions are not to be trusted.  Despite your instincts , you go out on a date with Mr or Ms (A>0, B>0) and have a fantastic time.  Turns out there was a positive interaction term in the  range, it also correlates positively with the noise (it wasn’t noise, just unknown knowledge), and you’ve found your soul mate.