"Monte Carlo Process" simulation for the example in Taylor's Introduction to Management Science textbook

monte_carlo.py

from collections.abc import Callable
import random


def demand(x: int) -> int:
    d = 0
    if not 0 <= x <= 99:
        raise RuntimeError(f"{x=} not in [0, 100)")
    for m in [19, 59, 79, 89]:
        if x > m:
            d += 1
    return d


def simulate(i: int, hook: Callable[[int, int], None] | None = None) -> tuple[int, int]:
    d_avg = 0
    r_avg = 0

    for n in range(i):
        x = random.randint(0, 99)
        d = demand(x)
        d_avg = (n*d_avg + d) / (n + 1)
        r_avg = (n*r_avg + d*4_300) / (n + 1)
        if hook is not None:
            hook(d_avg, r_avg)

    return d_avg, r_avg



if __name__ == "__main__":
    sim_hook = lambda d_avg, r_avg: print(f"{d_avg:.2f} ${r_avg:,.2f}")
    simulate(15, sim_hook)