A simulation is an approach that is used most commonly in two situations.
The first situation is when uncertainty is high due to sparse data. One such example is the simulation of an ancient Native American tribe, the Anasazi, which was a culture that lived between the 9th and 14th centuries. It is hard to run typical analytics on the limited available data about the Anasazi so researchers use simulation to try to understand what happened to the tribe.
A second common use of simulation is for experimentation in a low-cost, low-risk environment. Researchers at the European Organization for Nuclear Research (CERN) simulate particles colliding in the Large Hadron Collider before they validate their forecasts in the expensive real-world collider in Switzerland. More common examples include airline pilots practicing on flight simulators and doctors learning on test patients.
Both of these applications of the simulation are helpful to scientists and researchers, but they come with a set of advantages and disadvantages. We have grouped these advantages and disadvantages into three broad areas related to technology, process, and socialization.
The following table gives a summary of the advantages and disadvantages of simulation, which we elaborate on below.
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