A simulation that does not account for uncertainties, consisting of a single realization of the system. The input parameters for a determinstic simulation are represented using single values (which typically are described either as "the best guess" or "worst case" values).
Discrete Event Simulation
A modeling methodology that utilizes a transaction-flow approach to modeling systems. Models consist of entities (units of traffic), resources (elements that service entities), and control elements (elements that determine the states of the entities and resources). Discrete event simulators are generally designed for simulating detailed processes such as call centers, factory operations, and shipping facilities.
An abstract representation or facsimile of an existing or proposed system (e.g., a project, a business, a mine, a watershed, a forest, the organs in your body).
Monte Carlo Simulation
Monte Carlo simulation can be used to describe any technique that approximates solutions to quantitative problems through statistical sampling. As used in GoldSim, 'Monte Carlo simulation' is more specifically used to describe a method for propagating (translating) uncertainties in model inputs into uncertainties in model outputs (results). Hence, it is a type of simulation that explicitly and quantitatively represents uncertainties. You can learn about Monte Carlo simulation in detail here.
Simulation of an environmental system that includes some man-made components (e.g., a waste disposal facility) in which one is attempting to predict the performance or the degree of safety or reliability of the system.
A simulation that explicitly represents uncertainty by specifying inputs as probability distributions.
A mathematical representation of the relative likelihood of an uncertain variable having certain specific values.
A single simulation run representing a particular "future" (i.e., one possible path the system may follow through time). When running probabilistic simulations, multiple realizations are carried out in order to simulate a large number of possible futures.
The process of creating a model (i.e., an abstract representation or facsimile) of an existing or proposed system (e.g., a project, a business, a mine, a watershed, a forest, the organs in your body) in order to identify and understand those factors which control the system and/or to predict (forecast) the future behavior of the system.
A process that often has some underlying trend or pattern, but inherently has a random component, and as a result, can only be described statistically. Examples of stochastic variables include rainfall, exchange rates, and the rate of insurance claims.
A simulation methodology based on the standard stock and flow approach developed by Professor Jay W. Forrester at MIT in the late 1950s and early 1960s. Models based on system dynamics are built using three principal element types (stocks, flows, and converters), and put emphasis on understanding the feedback structure of systems.