Monte Carlo simulation technique

1. Method of simulating the behaviour of a system whose physical components and functional relationships can in part be described only by laws of probability. By means of a random number generator, numbers are produced in order to determine the component movements by the use of probability distribution. The behaviour of the whole system is then calculated by the use of these random movements. By simulating the random nature of certain real-world organizational processes (of which the problems are too complicated for classical analytic and quantitative methods), it can treat problems that are almost impossible to deal with otherwise.

2. An operations research tool by means of which solutions can be approximated in models containing stochastic variables. A trial-and-error technique, refined by the use of probability curves and random samples. The name derives from the fact that the technique usually involves programming so that the computer generates a random normal number as needed, to evaluate the stochastic variables.