What is the best way to handle Monte Carlo simulation uncertainty?

1 Uncertainty sources

Monte Carlo simulation uncertainty refers to the variability or error in the simulation outputs that arises from different sources, such as the input data, the model assumptions, the random number generation, and the sampling method. These sources can affect the accuracy, reliability, and validity of the simulation results, and introduce bias, noise, or outliers. For example, if your input data is incomplete, inaccurate, or outdated, your simulation results will not reflect the true state of the system. Similarly, if your model assumptions are unrealistic, oversimplified, or inconsistent, your simulation results will not capture the complexity and dynamics of the system.

3 Uncertainty reduction

Another way to handle Monte Carlo simulation uncertainty is to reduce it using some techniques and strategies. Uncertainty reduction is the process of minimizing or eliminating the uncertainty in the simulation outputs, such as by improving the input data, refining the model assumptions, enhancing the random number generation, or increasing the sample size. Uncertainty reduction can help you improve the precision, accuracy, and efficiency of the simulation results, as well as avoid misleading or erroneous conclusions. For example, you can use data cleaning, validation, or imputation to improve the quality and completeness of the input data. You can also use variance reduction techniques, such as antithetic variates, control variates, or importance sampling, to reduce the variance and increase the efficiency of the simulation outputs.

4 Uncertainty propagation

A third way to handle Monte Carlo simulation uncertainty is to propagate it using some algorithms and models. Uncertainty propagation is the process of transferring and combining the uncertainty in the simulation inputs to the simulation outputs, such as by using probabilistic or deterministic methods. Uncertainty propagation can help you understand how the uncertainty in the input data and the model assumptions affects the uncertainty in the simulation results, as well as how to account for it in your analysis and decision making. For example, you can use probabilistic methods, such as Bayesian inference, Markov chain Monte Carlo, or stochastic differential equations, to model the input data and the model assumptions as random variables or distributions, and then use Monte Carlo simulation to generate the posterior or predictive distributions of the simulation outputs. You can also use deterministic methods, such as sensitivity analysis, global sensitivity analysis, or meta-modeling, to measure the impact or influence of the input data and the model assumptions on the simulation outputs, and then use Monte Carlo simulation to evaluate the uncertainty ranges or scenarios of the simulation outputs.

5 Uncertainty communication

A final way to handle Monte Carlo simulation uncertainty is to communicate it using some tools and formats. Uncertainty communication is the process of presenting and explaining the uncertainty in the simulation outputs, such as by using visualizations, narratives, or recommendations. Uncertainty communication can help you convey the significance, relevance, and implications of the uncertainty in the simulation results, as well as engage and inform your audience and stakeholders. For example, you can use visualizations, such as histograms, boxplots, or error bars, to show the distribution, variability, or confidence of the simulation outputs. You can also use narratives, such as stories, scenarios, or analogies, to illustrate the meaning, context, or impact of the uncertainty in the simulation results. You can also use recommendations, such as actions, options, or criteria, to suggest how to use the uncertainty in the simulation results for your decision making or problem solving.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?