Monte Carlo simulation
(also Monte Carlo method, Monte Carlo experiment)
Monte Carlo simulation definition
A Monte Carlo simulation is a technique to obtain information on the behavior of complex processes through the use of repeated random sampling. Monte Carlo simulations help decision makers model different scenarios to assess likely risks. Monte Carlo simulations are named after the Monte Carlo casino in Monaco.
How Monte Carlo simulations work
- Outline: Setting the scope of the simulation by identifying the variables and relationships in the target system. For example, in financial modeling, variables could include stock prices, interest rates, and customer demand.
- Sampling: Generating random output within the pre-defined scope of the scenario. To increase the accuracy of the predictions, the sampling process is repeated many times.
- Calculation: The likely outcomes are calculated using the defined rules of the system.
- Analysis: The resulting scenarios are analyzed to provide insights into what is likely to happen.
- Interpretation: The stakeholders make an informed decision based on the statistical information provided by the Monte Carlo simulation.
Uses for Monte Carlo simulations
- Finance: Financial models built using Monte Carlo simulations help understand the behavior of financial markets, estimate portfolio performance, and assess investment risks.
- Engineering: Monte Carlo simulations can be used to model the effects of fluid dynamics, electrical layout, and structural changes.
- Risk analysis: Many industries, including insurance businesses and environmental agencies, use Monte Carlo simulations to assess the risks associated with different scenarios.