A simple model for assessing output uncertainty in stochastic simulation systems

The need for expressing uncertainty in stochastic simulation systems is widely recognized. However, the emphasis in uncertainty has been directed toward assessing simulation model input parameter uncertainty, while the analysis of simulation output uncertainty is deduced from the input uncertainty. Most recently used methods to assess uncertainty include Delta-Method approaches, Resampling method, Bayesian Analysis method and so on. The problem for all these methods is that the typical simulation user is not particularly proficient in statistics, and so is unlikely to be aware of appropriate sensitivity and/or uncertainty analyses. This suggests the need for a transparent, implementable and efficient method for understanding uncertainty, especially for simulation output uncertainty. In this paper, we propose a simple and straightforward framework to assess stochastic simulation output uncertainty based on Bayesian Melding. We firstly assume the form of probability distribution function of simulation output. We also assume that the final output uncertainty is the weight sum of uncertainty for every simulation output and the weight of each simulation run is proportional to its probability. The advantage of these assumptions is that to describe the simulation output uncertainty in the form of probability distribution function after limited simulation runs, we need only to do two things (1) to estimate parameters in the simulation output probability distribution function and (2) to calculate weight for each simulation. Both of them are discussed in detail in this paper