This is find, but instead of simply being honest about these limits there is a tendency to excuse them, to pretend that these limitations are more fundamentally justified. Three examples are as follows:
- "for the sake of simplicity" (e.g. these articles in JASSS), this implies that simpler models are somehow better in ways beyond that of straightforward pragmatic convenience (e.g. easier to build, check, understand, communicate etc.)
- That more complicated models are less complex (e.g. Sun et al.2016) which shows a graph where "complexity may decrease after a certain threshold of model complicatedness". This is sheer wishful thinking, what is more likely to be true is that it is harder to notice complexity in more complicated models, but this is due to our cognitive limitations in terms of pattern recognition, not anything to do with emergent complexity.
- Changing English to make our achievements sound more impressive than they are, e.g. to call any calculation using a model a "prediction", when everybody else uses this word to really mean prediction (i.e. anticipating unknown data/observations sufficiently accurately using a model).