Uncertainties of numerical results

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Determining uncertainties in results from Monte Carlo simulations involves analyzing the variability of outcomes across multiple runs. In the discussed example, simulating the diffusion of particles can yield different results for the number of particles that escape the box. A common method to quantify uncertainty is to repeat the simulation several times and calculate the standard deviation of the results. If computational resources are limited, one can run simulations with smaller sample sizes and extrapolate the uncertainty based on the relationship with sample size, often approximated by 1/sqrt(N). This approach provides a practical way to estimate uncertainty in Monte Carlo simulations.
Niles
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How do people generally determine uncertainties on results that are based on Monte Carlo simulations? Take this fictive example, just so we have something specific to talk about:

I look at 106 particles, confined to a box. There is a small hole in one of the walls, and at some time t0 I am interested in knowing how many particles N have diffused out of the box. This can be simulated by a Monte Carlo approach (Brownian motion).

This number N will vary each time I perform the simulation, but it will converge the larger I make the initial sample. Nonetheless, I guess an uncertainty is still present - how can we determine that in general?

Thanks for input in advance.
 
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I'm not sure if there's a more elegant method, but I'd repeat the simulation multiple times and take the standard deviation of the results. Perhaps if it's too computationally expensive to do this with a very high N, you can repeat it for a few low N and extrapolate the uncertainty as a function of N using a curve fit? I assume it would be 1/sqrt(N)?
 
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