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 code is just an example)

to reproduce
  ask turtle 1
  [
  let X-Of-Mother X

  hatch 1
  [

    set X one-of (list (X-Of-Mother) (X-Of-Mother + 0.1) (X-Of-Mother - 0.1) (random-float 1))

    ]

  ]

end

Currently I have to check if X of child turtle is always within range by something like this:

if X > 1 [set X X - 0.2]
if X < 0 [set X X + 0.2]

What could be a better way to do it?

What if I have to use random-normal 0.5 0.1 , how can I limit that to values between 0 and 1 , I have done many repetitions of generating such random numbers I think the quality of random-normal is good and there is not that many times that I need to check if it's beyond the range.

for example :

  to test 
    Let c 0
    let b 0 
    repeat 100000000
    [Set b random-normal 0.5 0.1
      if b > 1 [set C C + 1]
      If b < 0 [set C C + 1]

      ]
    print c  

  end

OUTPUT is *67 times out of 100000000 Time* 67 is biggest one I got, I got 58 , 51 , ...

Answer 1

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 ]