# [racket] Neil: Is there a sensible way to sample from the naturals? EOM

 From: Robby Findler (robby at eecs.northwestern.edu) Date: Wed Feb 19 22:07:40 EST 2014 Previous message: [racket] Neil: Is there a sensible way to sample from the naturals? EOM Next message: [racket] Neil: Is there a sensible way to sample from the naturals? EOM Messages sorted by: [date] [thread] [subject] [author]

```Thanks Neil! (I think probably Max and Burke are also interested.)

What is the connection between the economic philosophers argument against
maximizing expected value, the idea that we should sample between 2^(X-1)
and 2^X, and then the answer that you gave?

I think that I know that the geometric distribution is something you get by
repeatedly playing a game much like that first one, but I'm not really sure
of the details and unable to piece together the rest....

Robby

On Wed, Feb 19, 2014 at 4:02 PM, Neil Toronto <neil.toronto at gmail.com>
wrote:
>
> I've CC'd the Racket users mailing list because I thought more people
(e.g. Robby) would be interested in the answer.
>
> You can't sample uniformly from the naturals, but you can sample
according to an "anything can happen" sort of distribution. You want a
distribution with no expected value (aka mean, average).
>
> Economic philosophers use such distributions to disabuse people of the
idea that maximizing expected value is always the best way to make
decisions. Here's a simple example.
>
>  1. Flip a coin until you get heads. Call the number of flips X.
>  2. You receive Y = 2^X dollars.
>
> You can flip a fair coin, or a coin biased toward tails. Which coin do
you choose? Obviously the biased coin. But in either case, the expected
value of Y is infinite. Voila! Paradox.
>
> (The problem isn't that there's no upper bound. For Y = X, the award is
still unbounded, but for any coin with nonzero heads probability, the
expected value is finite.)
>
> You want *every* natural to be possible, though, so we'd have to do
something like sample uniformly between 2^(X-1) and 2^X. Here's a function
that does it:
>
>   (: random-natural/no-mean (-> Real Natural))
>   (define (random-natural/no-mean prob-zero)
>     (define n (exact-floor (sample (geometric-dist prob-zero))))
>     (define m1 (assert (expt 2 n) integer?))
>     (define m0 (quotient m1 2))
>     (max 0 (random-integer m0 m1)))
>
> The "max 0" keeps TR from complaining that `random-integer' returns an
Integer. The `prob-zero' argument is the probability the coin is heads,
which is also the probability this function returns 0.
>
> This looks sort of like what you want:
>
> > (random-natural/no-mean 0.001)
> - : Integer [more precisely: Nonnegative-Integer]
> 56136474695225172011728291802626216994256833545894766283873703181
> 10364986394536406497817120521834403457182656624358136577815679690
> 73469994282060833573766848756431669747238563269112899118707963866
> 08154252648824183995287333693058951314331721341986222320438359883
> 50861513517737507150144340359987088543453799423969409721165923104
> 82128386772489312894482659745630141444108439622157113717027784284
> 7612786588731040573293479397701924913229558559022675650838885440
>
> > (random-natural/no-mean 0.001)
> - : Integer [more precisely: Nonnegative-Integer]
> 57
>
> If it returns small numbers too often, you can mitigate that by taking
the max of a few of them.
>
> If you want a feel for the "average" output, you can put it in terms of
average number of bits:
>
> > (mean (build-list 1000 (λ _ (integer-length (random-natural/no-mean
0.01)))))
> - : Real
> 99 407/1000
>
> In fact, now that I think about it, this is probably nearly equivalent:
>
> (: random-natural/no-mean (-> Real Natural))
> (define (random-natural/no-mean p)
>   (random-bits (add1 (exact-floor (sample (geometric-dist p))))))
>
> The average number of bits should be near (/ 1 p).
>
> Neil ⊥
>
> ____________________
>  Racket Users list:
>  http://lists.racket-lang.org/users
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