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Deterministic simulation
In mathematical modeling, deterministic simulations contain no random variables and no degree of randomness, and consist mostly of equations, for example difference equations. These simulations have known inputs and they results in a unique set of outputs. Contrast stochastic (probability) simulation, which includes random variables.
Deterministic simulation models are usually designed to capture some underlying mechanism or natural process. They are different from statistical models (for example linear regression) whose aim is to empirically estimate the relationships between variables. The deterministic model is viewed as a useful approximation of reality that is easier to build and interpret than a stochastic model. However, such models can be extremely complicated with large numbers of inputs and outputs, and therefore are often noninvertible; a fixed single set of outputs can be generated by multiple sets of inputs. Thus taking reliable account of parameter and model uncertainty is crucial, perhaps even more so than for standard statistical models, yet this is an area that has received little attention from statisticians.
==Use of simulations==
Mostly are deterministic simulations used in scientific researches, we can found in various studies about populations fields, climate development, pollution, but also in another areas as engineering, chemistry and policy making. Deterministic simulations have received attention in the statistical literature under the general topic of computer experiments. Computer experiments simulate complex system which requires a number of inputs. Use of stochastic system is much cheaper but also inaccurate and simplifying.
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
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