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Re: [Swarm-Modelling] foundation of ABMs
From: |
Steve Railsback |
Subject: |
Re: [Swarm-Modelling] foundation of ABMs |
Date: |
Wed, 06 Apr 2005 07:56:11 -0700 |
User-agent: |
Mozilla Thunderbird 1.0 (Windows/20041206) |
Pablo Gomez Mourelo wrote:
Dear all:
I am an engineer very interested in agent-based modelling. I have a
question for you all, related to justification/foundation of ABMs.
I have already read some literature and it seems to me that a
justification of agent-based modelling has not been achieved (Volker
Grimm).
One of the problems of AB-modelling is that randomness is nearly always
included in our simulations, so different executions turn into different
outcomes.
In comparison to mathematical models , it seems to me very difficult to
develop a general theory (foundation) of agent based modelling. HOw do
we know an ABM converges to some solution? How can we describe stability
of an ABM? Many modellers feel satisfied with the graphical output, but
mathematicians always complain about the lack or rigour beneath the
simulation.
Now I will throw in my thoughts, which have been stimulated by similar
discussions on Swarm Modelling over the years and by working with the
good Dr. Grimm.
a. When we use agent-based models, we give up the concept of
system-level theory. That's why we use ABM, because we don't think there
are system-level models or concepts (like stability) that are useful for
the kind of problems we want to work on. Instead, we are interested in
understanding the processes by which system dynamics emerge.
b. Instead, we should be concerned with theory at the *individual*
level: what models of how individual behave and interact with each other
and their environment are useful for reproducing emergent system phenomena?
c. We can test and "prove" individual-level theory by putting it in an
ABM and seeing if the ABM reproduces a wide variety of observed dynamics
at both the individual and system levels. If so, then we can be fairly
confident that the ABM captures the essential processes that system
dynamics emerge from.
This kind of theory has been described as "algorithmic" instead of
analytical- we try to understand the "rules" or mechanisms from which
different outcomes emerge under different situations. Evolution is a
good example: we have a very good understanding of the mechanisms of
evolution, but we still cannot predict what the outcome will be.
There are some papers discussing and illustrating this approach at our
site: www.humboldt.edu/~ecomodel/products.htm
Look for a paper called 'Getting "results"...', and recent papers on
habitat selection by fish. There is a review of other examples in the
book by Dr. Grimm and me, also described on our site.
Steve Railsback
--
Lang Railsback & Assoc.
250 California Ave.
Arcata, California 95521
707 822 0453