There is no perfect tool for modeling ecosystems, each of those presented provide different insights. Analytic equations give ecologists useful tools for predicting stability in large populations. Because they are mathematically formulated, they can be solved to provide deterministic answers (assuming the equations do not become chaotic). These models focus on average case and uniform behavior, so they work well with large, uniform populaiton such as plankton or insects. Unfortunately, for smaller populations with high variability, these equations do not provide accurate results, in general.

Stochastic models provide researchers with a technique to account for variability in populations. To be meaningful, the models have to be run enough times to provide statistically significant results in contrast to analytical equations which only require a solution. By providing a more accurate representation of behavior, these stochastic models give a more detailed account of the working of ecosystems. By accounting for individual behavior, Individual Based Models provide greater detail than stochastic models. This detail comes at the expense of either smaller models or greater computing power. The remainder of this tutorial will focus on the representational and computing aspects of IBMs. z