10 Apr 2017

Bruce's Modelling Gripes, No. 2: Making specious excuses for pragmatic limitations on modelling

We are limited beings, with limited time as well as computational and mental capacity. Any modelling of very complex phenomena (social, ecological, biological etc.) will thus be limited in terms of: (a) how much time we can spend on it (b) how much detail we can cope with (checking, validating, understanding etc.) (c) what assumptions we can check

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).
These weasel words would not matter so much if they were (a) purely internal to the field and everyone understood their meaning and (b) they were not used in public/policy consultations or grant applications where they might be taken seriously. Newcomers to the field often take these excuses too literally and so change what they attempt to do as if these excuses were genuine! This can be an excuse for following the easy option when they should be pushing the boundaries of what is possible. When policy makers/grant funders misunderstand these claims, an inevitable disappointment/disillusionment may follow, damaging the reputation of the field.


Sun, Z., Lorscheid, I., Millington, J. D., Lauf, S., Magliocca, N. R., Groeneveld, J., ... & Buchmann, C. M. (2016). Simple or complicated agent-based models? A complicated issue. Environmental Modelling & Software, 86, 56-67. 

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