Tuesday, September 18, 2012

Why Simulate?

Simulations are a powerful tool available to researchers in all kinds of work from economics, to engineering, to psychological measurement.  They provide researchers with a unique tool that can be used to explore detailed thought experiments.  Researchers can simulate anything from the relatively straightforward testing of the inherently unobservable effectiveness of new or existing econometric/statistical methods to more complex simulations of human or non-human movement and behavior such as simulations of the transmission of malaria parasites or zombie hordes.

Simulations are frequently used to support theories developed a proiri to the simulation and advanced by authors who use simulations to complement their arguments.   The literature is replete with examples of new methods or theories that use simulations to complement their theoretical results.   Simulations can also be useful tools in exploring new problems and solutions.  An ideal exploratory simulation will have an environment rich in reasonable and representative environmental information. 

A perfect example of this is an engineering simulation I observed in which the automobiles with various numbers of wheels, wheel sizes, and wheel positioning were created in the simulation and the computer reran the model assigning different parameter values to each repetition searching the best design.  However, when simulating global environments it becomes all the more important that simulated agents are representational of real agents because nearly any result is possible if one controls the environment of the simulation.    

Of course there are other reasons for do simulations besides the ones listed above.  Sometimes simulations are seen as a mechanism for uncovering relationships that are too difficult or complex to identify outside of a constructed framework.  Typical examples of this are simulation that test violations of assumptions in statistical/econometric methods.  Looking at instrumental variables for instance, what happens when an instrumental variable does not have a very strong predictive power on the endogenous variable?  And what happens when it is also slightly endogenous?

Yet other simulations are used to dismiss or undermine criticisms.  Often times it is seen as the fatal flaw in an argument if the estimator is shown to be biased theoretically.  However, simulations can be used to look at the scope of reasonable bias expected and see how it would be expected to affect estimates.  It is not uncommon that biased, even inconsistent estimators turn out to be reasonable and effective even when the assumptions are not perfectly met.

As hopefully is clear, there are many good reasons for developing simulations.  This blog will hopefully guide you through of some of the most common types of simulations as well as through more obscure types as well.

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