Opioid abuse -- United States -- Prevention, International System Dynamics Conference
We present a practical guide, including a step-by-step flowchart, for establishing uncertainty intervals for key model outcomes in the face of uncertain parameters. The process starts with Powell optimization (e.g., using VensimTM) to find a set of uncertain parameters (the “optimum” parameter set or OPS) that minimize the model fitness error relative to available reference behavior data. The optimization process also helps in refinement of assumed parameter uncertainty ranges. Next, Markov Chain Monte Carlo (MCMC) or conventional Monte Carlo (MC) randomization is used to create a sample of parameter sets that fit the reference behavior data nearly as well as the OPS. Under the MC method, the entire parameter space is explored broadly (with a very large number of runs), and the results are sorted for selection of qualifying parameter sets (QPS) based on goodness-of-fit criteria. The statistical properties of the QPS parameter distributions are analyzed to ensure their centrality relative to the uncertainty ranges. Also, the full set of QPS outputs are graphed (as sensitivity graphs or box-and-whisker plots) for comparison with the reference behavior data. Finally, alternative policies and scenarios are run against the OPS and all QPS, and confidence intervals are found for key model outcomes. We demonstrate the method with a substantial model that is in the process of being published, and we discuss how such analyses can be used by policy/decision makers.
Wakeland W. and J. Homer. "Addressing parameter uncertainty in SD models with fit-to-history and Monte-Carlo sensitivity methods." Proceedings of the 38th International Conference of the System Dynamics Society, July 2020.