A simulation is the imitation of the operation of a real-world process or system. The behavior of a system is studied by generating an artificial history of the system through the use of random numbers. These numbers are used in the context of a simulation model, which is the mathematical, logical and symbolic representation of the relationships between the objects of interest of the system. After the model has been validated, the effects of changes in the environment on the system, or the effects of changes in the system on system performance can be predicted using the simulation model. 1
Gnumeric includes a facility for performing Monte Carlo Simulation. Monte Carlo simulation involves the sampling of random numbers to solve a problem where the passage of time plays no substantive role. 2 In other words, each sample is not effected by prior samples. This is in contrast to discrete event simulation or continuous simulation where the results from earlier in the simulation can effect successive samples within a simulation experiment. The Monte Carlo simulation will be enabled through the use of the Random Number functions as described in ??? and the results presented along with statistics for use in analysis. 3
Adapted from Banks, Carson, Nelson and Nicol (2001), Discrete-Event System Simulation, 3rd ed.
Definition from Law and Kelton (1991), Simulation Modeling & Analysis, 2nd ed, pp. 113.
Gnumeric random numbers are generated using the Mersenne twister MT19937 pseudo-random number generator as implemented in the GNU Scientific Library.