Jochen,
> -If the simulation is too complex and matches 
>  official experimental data, everything takes a 
>  lot amount of time (creation, setup and execution of
>  the experiment and finally the cumbersome analysis
>  of the complex outcomes), and it becomes increasingly 
>  difficult to identify the principal laws, because it is 
>  easy to get lost in the data or bogged down in details
>   
This may be a false choice.   In the case of having some data of 
moderate resolution, there's no point in making a hugely elaborate model 
and simulation, because you'll never be able to validate beyond your 
data anyway.   And if you don't validate, although the modeling still 
may be useful as an thought experiment, it isn't science.  You have to 
be able to say something that can be shown to be wrong.   If you do aim 
to learn things about the world and then predict them it's not desirable 
to have giant black box with lots of moving parts.   It's better, if at 
all possible, to have a simple story and make the simulation nothing 
more than apparatus to help extend the data so that the dynamics can be 
studied by theoreticians.

Another mode of use for ABMs is to lower expectations of theoretical 
traction and opportunistically look for ways a model makes useful 
predictions and then modify the model in that direction over time.   
This is a risky and expensive craft, but one that might have high enough 
payoffs to consider (e.g. national security).

It depends on the data and what is of interest.   If the data tells you 
about a number of rare events, and it is these events is what you really 
care about, then it may make sense to loosely model everyday behaviors 
and focus on model microstructure that can create the rare events you 
care about.

Finally, sometimes microstructure is known with clearly defined degrees 
of freedom, and the dynamics are of interest.  Consider modeling a 
factory where different assembly regimes are to be evaluated..  There's 
no need to validate here because the whole exercise is to answer 
what-ifs about realizable specific systems.

Marcus



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