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:

data = []
for i in range(100):
    p = Part()

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.


1 Answer 1


A simple way is to add a scale coefficient to this line:

surv = 1/(1 + np.exp(-scale * (self.exp_death - self.age)))

A smaller value will increase the variance (relative to the implicit value of 1 in your simulation).


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