when faced with significant uncertainty in making a forecast or estimate some methods replace the uncertain variable with a single average number the monte carlo simulation instead uses multiple values and then averages the results 
monte carlo simulations have a vast array of applications in fields that are plagued by random variables notably business and investing they are used to estimate the probability of cost overruns in large projects and the likelihood that an asset price will move in a certain way telecoms use them to assess network performance in various scenarios which helps them to optimize their networks insurers use them to measure the risks they may be taking on and to price their policies accordingly investment analysts use monte carlo simulations to assess the risk that an entity will default and to analyze derivatives such as options financial planners can use them to predict the likelihood that a client will run out of money in retirement 1monte carlo simulations also have many applications outside of business and finance such as in meteorology astronomy and physics today monte carlo simulations are increasingly being used in conjunction with new artificial intelligence ai models for example as ibm noted in 2024 many financial firms now use high performance computing systems to run monte carlo simulations and as the numbers of these simulations grow over ever increasing portfolios of financial assets and instruments the interpretation of these as an entirety becomes a growing challenge that is where ai comes in the use of ai to assist a professional in their assessment of these simulations can both improve accuracy as well as deliver more timely insights in a business where time to market is a key differentiator this has direct business value ibm says 2history of the monte carlo simulationthe monte carlo simulation was named after the famous gambling destination in monaco because chance and random outcomes are central to this modeling technique as they are to games like roulette dice and slot machines the technique was initially developed by stanislaw ulam a mathematician who worked on the manhattan project the secret effort to create the first atomic weapon he shared his idea with john von neumann a colleague at the manhattan project and the two collaborated to refine the monte carlo simulation 3