Monte Carlo Simulation


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Monte Carlo Simulation

Simulation is a process of designing a model to resemble a real world situation to represent the same real world situation. It is used to find an estimate of the overall effect of particular actions by repeating an experiment many number of times. It is mainly used when the problem under a research study is too complex to be solved or analyzed by the use of standard analytical methods or quantitative optimization techniques. In general, it involves developing a model of some life situation and then performing experiments on the model developed.

Main advantages of simulation:

  • It is a simple technique and straight forward
  • It is useful for analyzing large and complex problems, which cannot be analyzed by the use of standard quantitative methods
  • It is an interactive method, useful for the researchers to examine the changes and their impact on the system operation.

Main limitations of simulation:

  • Occasionally, simulation models would be very costly and expensive
  • As it is a trial and error technique, more than one solution will be produced based on repeated runs
  • It provides only an alternative solution for a more complex real world situation since the results obtained by the simulation technique may or may not be optimal.

The Monte-Carlo method is a simulation technique involves creating a suitable statistical distribution function by using series of random numbers. According to the theory of random numbers, each number used in such statistical distribution function has an equal opportunity of being selected. The random numbers can be generated in many number of ways, such as tossing an unbiased coin or die, using a published random number table, and some advanced method. However, random numbers produced by some method may not be actually random in nature and such random numbers are called Pseudo-Random-Numbers.

Steps to be followed while performing Monte-Carlo simulation are given as follows:

  • Relevant or required probability distribution has to be created for the variables to be simulated or analyzed.
  • Then a cumulative probability distribution (cdf) has to be constructed for the same variables.
  • Random numbers have to be generated using the cdf and then an appropriate set of random numbers has to be assigned to represent value or range of values for each random variable.
  • Simulation experiment has to be conducted based on random sampling.
  • Repeat Step 4 until the required number of simulation runs to be generated is completed.
  • Design and apply a rule or strategy to maintain control.

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