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While I agree with Natsu and Jess that simpler models tend to be clearer (and easier to implement!), I think it goes beyond that. Regardless of the complexity of the functions that go into making it, a "good" model is one that can mislead you into thinking that you know what is going to happen...and then throw you for a loop. The element of surprise is key because this is what makes a viewer want to go back and explore the model (and its idiosyncrasies) further. Langston's Ant works so well because both viewers' predictions for the model AND their self-realizations of their own knowledge are proved to be faulty. Now that's "wow" factor.