Theory and Agent Based Modelling
To build an ABM model detail information about agents, their interactions and the environment is needed. Maybe it's better to say exact information is essential here. To build an ABM every component of should be explicitly defined on the paper.
Of course always we don't know total aspects of a target. It should be added that if we knew everything about the target again we couldn't put them all in the model. Since models must be pretty simpler than reality we have to choose parts of reality. In other words we only put one perspective about the reality in the model.
Then it can be concluded from the above that two interrelated sides of ABM modeling is theory and field. This may be true in other relationship between these two is more crucial.
Most peers when see for the first time an ABM, their premier question is how do you know these agents in real world act and think like this?
then the problem of validity is steel a little bit more controversial in abm modeling than other research methods like survey or grounded theory. This is not because ABM models are essentially less robust but it is so maybe because your paper reviewers are less familiar with this area of research and are more skeptical about it.
When a model is more general it relies more on theories than detail information about the field. On the other hand when
a model more specific it is more hungry of field data. But with some exceptions we can say all models need both theory and field data. Structure of a model mostly comes from a theory and field data complete gaps. Then one of first things to do when you have a target
