In many systems, the controlling parameters, processes and events may be uncertain and/or poorly understood. In a deterministic simulation, these parameters are represented using single values (which typically are described either as "the best guess" or "worst case" values). Probabilistic simulation is the process of explicitly representing this uncertainty by specifying inputs as probability distributions and specifying any random events that could affect the system
If the inputs describing a system are uncertain, the prediction of future performance is necessarily uncertain. That is, the result of any analysis based on inputs represented by probability distributions is itself a probability distribution.
Hence, whereas the result of a deterministic simulation of an uncertain system is a qualified statement ("if we build the dam, the salmon population could go extinct"), the result of a probabilistic simulation of such a system is a quantified probability ("if we build the dam, there is a 20% chance that the salmon population will go extinct"). Such a result is typically much more useful to decision-makers who might utilize the simulation results.
GoldSim has powerful capabilities for carrying out probabilistic simulations. In particular, it uses advanced Monte Carlo simulation techniques to propagate the uncertainty in model inputs to model outputs.
A more detailed description of probabilistic simulation concepts, including a discussion of the advantages and disadvantages of probabilistic simulation and a description of Monte Carlo simulation techniques, is provided in Appendix A of the GoldSim User’s Guide.