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date: 20 February 2020

Abstract and Keywords

Monte Carlo analysis is a research strategy that incorporates randomness into the design, implementation, or evaluation of theoretical models. It began in the 1940s, when the development of computer hardware and mathematical models made it possible to generate streams of random numbers. These random number streams are combined with mathematical models to create models and evaluate theories of random processes. This chapter attempts to tame this diverse, unmanageable collection of concepts and methods by dividing simulation projects into three types. The first, commonly called “Monte Carlo simulation,” is used to evaluate statistical estimators. When an estimation procedure is proposed, it is standard procedure to test it against a variety of simulated research problems. A second type of project, referred to as “Markov chain Monte Carlo” (MCMC), helps researchers draw conclusions about complicated probability models for which conventional research strategies do not yield insights. The third type of project arises in the study of complex systems, which are characterized by a large number of loosely interconnected, autonomous elements. Commonly known as “agent-based models,” these simulations have found enthusiastic advocates in environmental and social sciences.

Keywords: Monte Carlo, Markov chain Monte Carlo (MCMC), pseudo-random number generation (PRNG), Bayesian statistics, agent-based modeling

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