Monte Carlo Analysis in Academic Research
Paul Edward Johnson · Oxford University Press eBooks · 2013
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 in order to create models and evaluate theories of random processes. This chapter attempts to tame this di-verse, 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 pro-posed, 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” (or MCMC), helps researchers to draw conclusions about complicated probability models for which conventional research strategies do not yield insights. The third 1 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 advo-cates in environmental and social sciences.