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System Dynamics is methodology for modeling and analyzing complex systems. Such systems can be characterized by interconnectedness and feedback. Applying risk assessment to the results of System Dynamics models is a challenge. Though in some cases the resulting time series data generated by a simulation may appear approximately random at a specific scale, there is often a high-degree of auto-correlation within the data series due to the deterministic nature of generation and feedback loops inherent in the system. This paper presents proposed Dynamic Risk Assessment Method (DRAM) that allows for the estimation of risk for system dynamics data series that appear to be approximately random. DRAM is based on standard risk assessment methods and is simple both to calculate and apply. In this article, the proposed method is applied to determine the risk connected with hypothetical costs of illness stemming from water supply system contamination with Cryptosporidium.
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