For no practical reasons whatsoever, I designed a simple Monte Carlo simulation in python.
import random import numpy as np class Part: def __init__(self,exp_death=80): self.age = 0 self.exp_death = exp_death self.alive = True def survive(self,days=1): self.age += days event = random.uniform(0,1) surv = 1/(1 + np.exp(- (self.exp_death - self.age))) if event >= surv: self.alive = False def test(self): while self.alive: self.survive() data =  for i in range(100): p = Part() p.test() data.append(p.age)
And now I can take a look at the mean age of death and standard deviation:
print(np.average(data), np.std(data)) >>> 79.74 1.616292052817188
The variance is extremely tight around the expected death. I'm curious, simply for the sake of learning, how could I increase or decrease the variance in this MC simulation? In other words, how could create a variance knob to control in these simulations?
Again, this has no practical motivation; I'm just curious.