More Questions about analysing ABMs

[Docgca]

Dear List,

Thank you for all your responses to my previous post.  I've looked at most of 
the references and still have some questions.  I would greatly appreciate any 
comments on the following statements (don't hold back!):
1)  It seems as if most systems being studied with ABM have at the most a few 
examples of real-world reference systems (things like evolution, origin of 
life, specific ecosystems, the economy).  The advantage of the A-life/ABM 
approach is to provide what people have called "alternate runs of the tape" 
and comparing the simulations/models the existing system looking for common 
patterns and behaviors.
2)  However, since there is often only one reference system, it is difficult 
to draw conclusions regarding "validity of process" in the model even if the 
observable outcomes "look similar."  I recall many posts on this subject.
3) Sunny Auyang talks about being able to look at a system from both 
deterministic and stochastic viewpoints (thanks, Steve, for the reference), 
the deterministic approach being fine-grained and the stochastic one being 
coarse-grained.  Auyang says deterministic theories are prefferable, when 
possible, but I think that in systems with only one reference system you 
cannot use stochastic analysis to validate.  The nature of an ABM with 
non-adaptive agents is deterministic, but if there is statistical data 
available for the reference system you can use stochastic measures to analyse.
 4)  So this is my project:  I built an ABM of the inflammatory response with 
cells as the agent level and used existing wet-lab data to formulate the rule 
systems for the cells.  The only independent variable is initial injury.  
There are pseudo-random numbers in the movement and pattern distribution of 
injury and initial cells, but after that the system is purely deterministic 
(these agents do not adapt).  I used as a measure of outcome a global damage 
measure and the number of remaining infectious agents.  After getting a 
distribution of outcomes over a range of initial injuries with the RNGs 
seeded, I picked regions of interest and ran multiple (~500) interations of 
the model with the RNGs turned back on.  Question:  Can I now use standard 
statistical techniques like distribution, standard deviation etc to compare 
the model to existing clinical data?  Furthermore, I programmed in an 
intervention that was tested in a clinical trial, ran multiple iterations 
with the RNGs on.  Can I compare this data set to the baseline with standard 
statistical methods for significant difference?  The only conclusion I want 
to draw is that there is a qualitative similarity between the behavior of the 
model and the real world; no direct one to one mapping is intended.

Sorry for the length of the email.  I look forward to your critiques and 
comments.

Yours,
Gary An, MD
Department of Trauma, Cook County Hospital


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