One type of model has a limited number of terms and fairly simple equations which means we can understand them (R=h2S for example), but if we applied them to a real system we know they would somewhat inaccurate. That's because we're leaving lots of potentially important factors out. However, if they are approximately accurate and fairly simple, they can help us understand the system and come up with new ideas by showing us what is most important. Describe a mathematical model designed to make predictions, but is so complicated that nobody really understands the details and therefore doesn't really help us understand the scenario.

The second type of model includes every factor that is important for the final result, but thereby becomes so unwieldy that we can't understand why it makes the predictions it does. It therefore has limited usefulness for helping us learn how the system works and thereby develop new ideas. Describe a mathematical model that is designed to help us understand a scenario, but is so simplified that it could never actually be used to make concrete and accurate predictions.

Cannot use these already taken: Prisoner's dilemma, Hawk/dove, sex ratio, equilibria, GFS Model for Weather and Exponential Growth, Cole's model, Charnov and Shaffer model, inclusive fitness, hamilton's rule, kin selection, Lotka-Volterra competition equations, and Species-area curve models.