As noted in section 1.0.1, simulation are experiments designed to determine probabilities. In ecological simulation, these probabilities enerally refer the character of a population due to some environmental alteration. There are obvious problems in performing any experiments on ecosystems. In particuler, the consequences of experiments may not be reversible. Simulations provide a means of experimenting on populations without effecting the ecosystem under study. A simulation that is properly built will then allow us to examine ecological theories without disrupting an actual ecosystem.Another benefit of comuputer simulation can be found in its drawbacks. If a simulation fails to match empircal results, we have at least three sources of error: 1) the simulation engine is not correctly programmed, 2) the data used to describe the simulation are incorrect, and 3) the theories we are trying to validate are flawed. It is unlikely that the first possibility is the source of error. The simulation engines are simple, well understood programs, there is not much room for error in them. This leaves the second and third possibilities. If the initial data is incorrect, then the collection method, the data analysis, or both, should be rethought. Likewise, if the error is in the model's assumptions, the model should be rethought. In either case, a failed simulation can be a useful tool for validating ecological theories. Early in this tutorial we presented an example of a simulation engine, the Simple Discrete Event Simulator. This engine is the model we will use throughout the tutorial, however, there are other kinds of simulation engines. The next section will introduce the major types of simulation engines.