An upper limit to the number of considered candidate models is typically set by the substantial effort required to define the models and to choose between many alternative options. There is no commonly accepted ABC-specific procedure for model construction, so experience and prior knowledge are used instead. Although more robust procedures for "a priori" model choice and formulation would be beneficial, there is no one-size-fits-all strategy for model development in statistics: sensible characterization of complex systems will always necessitate a great deal of detective work and use of expert knowledge from the problem domain.