Sustainability and Social Justice

Monte Carlo Simulation

Document Type

Book Chapter


The Monte Carlo method can be used to address any mathematical problem or model that is too complex, time consuming, or resource intensive to solve analytically. Instead of tackling the numerical problem directly, Monte Carlo allows the researcher to obtain an approximation of the solution through setting up an experiment of statistical sampling. As the name indicates, the method borrows from games of chance such as those played at the famous casinos of Monte Carlo in Monaco. The Monte Carlo method relies on realizations (draws) from a probability density function. Ideally, to correctly apply the Monte Carlo method and obtain valid results, the sampling method employed should be completely random. The number of realizations has to be sufficiently large to accurately represent the distribution of the input variables. There have been numerous and diverse applications of Monte Carlo methods that include, for example, science (computing multidimensional integrals and model sensitivity analysis), education (teaching and research), business (portfolio management, product life cycle analysis), environment (probabilistic risk assessment and resource allocation), health (delivery of services and epidemiology), government (project choice and standard setting), engineering (design and project management), and energy (utility management and methods for hydrogen production). Application areas seem limited only by imagination and computing power.

Publication Title

International Encyclopedia of Human Geography

Publication Date


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bootstrapping, cumulative distribution function, Latin hypercube sampling, Monte Carlo simulation, parametric, probability density function, random experiment, random sample, random variable, risk assessment, sensitivity analysis, statistical sampling, uncertainty