To conduct a Monte Carlo Simulation you need to have at least one random process that you can draw examples from.

Question

BlackTom AI · Accepted Answer

The statement asks whether a Monte Carlo Simulation requires at least one random process to draw samples from.
Option 1: True. This is the standard view of Monte Carlo methods: they rely on random sampling to explore the space, estimate integrals, or approximate distributions. The core idea is using randomness to obtain representative samples and then aggregate results to form an estimate, with accuracy often improving as more samples are taken.
Option 2: False. This would imply that randomness is optional for Monte Carlo, but that contradicts the fundamental mechanism of the method. If there were no random or pseudo-random process to generate samples, the approach would not be Monte Carlo; it would be a deterministic method, which is typically categorized differently (e.g., grid-based or quasi-Monte Carlo approaches). While some discussions distinguish quasi-random (low-discrepancy) sequences as not truly random, many practical Monte Carlo implementations still depend on a random or pseudo-random source to emulate stochastic behavior. Claiming randomness is unnecessary ignores the probabilistic basis of Monte Carlo estimates, confidence intervals, and error behavior.

In sum, the idea that Monte Carlo simulations require at least one random (or pseudo-random) process to generate samples aligns with the conventional definition and practice. Conversely, stating that randomness is optional would mischaracterize what characterizes a Monte Carlo approach.

SP25 RMI 660 001 Engaged Learning 16: Intro to Monte Carlo Simulations

To conduct a Monte Carlo Simulation you need to have at least one random process that you can draw examples from.

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