NetLogo : How to make sure a variable stays in a defined range?

Question

I have a few variables which can be inherited to child agents by a variation of + 0.1 and -0.1 or without any changes, or random again, What I have done is like this: (The

Answer 1

Answering very late to add another option for future seekers...

Another option if you're looking for a distribution that is bell-shaped like a normal distribution, but bounded, the Beta distribution can be a good choice. If you use parameters like 3,3 or 4,4, it looks a lot like a Normal distribution, but won't have any out-of-bounds values (it scales from 0 to 1, so it may have to be moved/scaled like you would a normal).

Netlogo doesn't have a built-in Beta, but you can get it from drawing from the built-in gamma twice, like this:

to-report random-beta [ #shape1 #shape2 ]

  let Xa random-gamma #shape1 1
  let Xb random-gamma #shape2 1
  report Xa / (Xa + Xb)

end

For more mathematical detail, see: https://math.stackexchange.com/questions/190670/how-exactly-are-the-beta-and-gamma-distributions-related

Answer 2

As you've discovered, random-normal can be problematic because the result you get back can be literally any number.

One possible solution is to clamp the output of random-normal within boundaries, as in Matt's answer. Note that this approach creates spikes at the boundaries of the range:

observer> clear-plot set-plot-pen-interval 0.01 set-plot-x-range -0.1 1.1
observer> histogram n-values 1000000 [ median (list 0 (random-normal 0.5 0.2) 1) ]

Another possible solution, as Marzy describes in the question itself, is to discard any out-of-bounds results random-normal gives you and just keeping trying again until you get an in-bounds result. This avoids the spikes at the boundaries:

to-report random-normal-in-bounds [mid dev mmin mmax]
  let result random-normal mid dev
  if result < mmin or result > mmax
    [ report random-normal-in-bounds mid dev mmin mmax ]
  report result
end

observer> clear-plot set-plot-pen-interval 0.01 set-plot-x-range -0.1 1.1
observer> histogram n-values 1000000 [ random-normal-in-bounds 0.5 0.2 0 1 ]

Another solution is to ask yourself whether you really need a bell curve, or whether a triangle-shaped distribution would be just fine. You can get a triangle-shaped distribution of results very simply just by summing two calls to random-float:

observer> clear-plot set-plot-pen-interval 0.01 set-plot-x-range 0 1
observer> histogram n-values 10000000 [ 0.5 + random-float 0.5 - random-float 0.5 ]

Answer 3

My favorite trick is this:

set x median (list 0 (y) 1)

Where y is the random number (or put in an expression), 0 is the minimum, and 1 is the maximum.

It works because if y is greater than 1, then the median will be 1. If y is less than 0, then the median will be 0. Otherwise the median is y.

For example, here is the random number in your example clamped to the range [0, 1]:

 to test
    let b median (list 0 (random-normal 0.5 0.1) 1)
    print b
 end