Generally (and therefore somewhat inaccurately) speaking, one way that our brains seem to handle the sheer complexity computing in the real world us is a tendency to simplify the information we gather.

In many cases these sorts of extremely simple models didn't start that way. They may have started with more parameters and complexity. But as they were repeated, explained and applied the model becomes, in effect, simpler. The example begins to represent the entire model, rather than serving to show only a piece of it.

Technically the exponential radioactive decay model for radioactivity of a mixture has most of the pieces you describe fairly directly. But this hardly means they will be appropriately applied, that they will be available when we are thinking of how to use the model. We need to fight the simplification effect to be able to make our models more nuanced and detailed - even though they are still almost certainly lossy compression of the facts, observations, and phenomena they were built from.

On the other hand, the simplification serves its purpose too, if we could devote unlimited cognitive resources to a model, then we risk not being unable to actually reach a decision from the model.