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Beemer

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[Sep. 23rd, 2005|01:57 pm]
Beemer
A random question for people with science leanings:

I need a probability distribution, but its shape isn't really known. That inclines me to use a gaussian as a generic, because most things are gaussian.

However, it can't produce negative values. What would you use?

Truncate the gaussian below zero? Fold negative values back to positive? Something like a chi-square distribution that looks gaussian once the mean is far from zero?
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[User Picture]From: dpolicar
2005-09-24 10:55 am (UTC)
Hm.
I think the assumption that the threshold is based on the number of warnings is flawed.
I suspect there's some fraction of the population that will run on first warning, and some that won't run at all, ever. But for the ones in between, the interesting threshold is probably the % of the population that already ran. As that happens, the odds of someone you know having run goes up sharply, and consequently your odds of doing so as well.
So I'd expect a small chunk at first, an accelerating rampup, and then a sudden plunge.
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[User Picture]From: melted_snowball
2005-09-24 12:18 pm (UTC)
Well, I was already assuming (correctly, it appears) that his system was working based on some way of integrating who's already run into a number.
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[User Picture]From: dr_tectonic
2005-09-24 12:47 pm (UTC)
I'm actually debating whether to do it using a global "fraction evacuated" number or to do it using random encounters...

That's separate from the number of people you know personally who have evacuated, for which we need to make a simple model social network.
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[User Picture]From: dr_tectonic
2005-09-24 12:31 pm (UTC)
I suspect there's some fraction of the population that will run on first warning, and some that won't run at all, ever.

Absolutely correct. (Though there are some interesting questions about won't run versus can't run.)

But for the ones in between, the interesting threshold is probably the % of the population that already ran. As that happens, the odds of someone you know having run goes up sharply, and consequently your odds of doing so as well.

Yup. The really interesting part (I suspect) will be how the signal of other people deciding to act is filtered through people's social networks, because how your friends are acting has an entirely different kind of influence than how "everybody" is acting.

It'll probably usually be a sigmoid curve, but where it tops out and how it unfolds over time are really useful details we hope the model can provide.
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