[2] In modern discourse, the paradox was articulated by John M. Dutton and William H. Starbuck:[3] "As a model of a complex system becomes more complete, it becomes less understandable.
Alternatively, as a model grows more realistic, it also becomes just as difficult to understand as the real-world processes it represents.
"[4] This paradox may be used by researchers to explain why complete models of the human brain and thinking processes have not been created and will undoubtedly remain difficult for years to come.
"[6]) Also, the same topic has been discussed by Richard Levins in his classic essay "The Strategy of Model Building in Population Biology", in stating that complex models have 'too many parameters to measure, leading to analytically insoluble equations that would exceed the capacity of our computers, but the results would have no meaning for us even if they could be solved.
[7][8][9] Bonini's paradox can be seen as a case of the map–territory relation: simpler maps are less accurate though more useful representations of the territory.