so we've gone through a fuse simple models
that kind of get you a flavour
offor some of the things that agent based modeling
has been useing to explore in the past in many of
those contexts agent based modeling was used
almost as a thought experiment to
explore potential theories about the way
the world works or to understand how
different parameters relate through things
along those lines but agent based modeling
could also be used to create very complex models
umm, so one natural question might be how big in advance can
it get well in netlogo alone which we'll
talk about here you could have tens of
thousands of agents of patches in fact
I've seen now models with hundreds of
thousands and potentially even millions
of agents and patches you can have very
complex decision makers decision makers
within the agent who are optimizing some
sort of economic market or who are
trying to make a decision based upon all
the past behavior they've had you could
see many different agent types so you
could have five or six or dozens of
agent types I puff in the model you can have
models of whole cities and how they're
integrated and one of the nice things
about not only netlogo but many of the
agent based modeling platforms out there is
that they allow you to bring in
additional tools that are available
outside of those software packages so
you can integrate for instance that
statistical analysis package in order to
use that to its fullest extent within the system
now what constrains how big a model can
get well that's an interesting question
a lot of times has to do with the
computational power you have the amount
of time you have the complexity of the
agents right the more complex the agents
are the more computational time they're
going to take right and you know also
the environment and so for in this
particular context we're going to now
talk about two particular types of
environments that are often used within
Agent based modeling one is more of a
spatial type model and the other one is
more of a network type model
so one of the models that I really like there one
of the groups that I like that does a
lot of age' based modeling is a group
called the redfish group that builds a
lot of models and net logo actually
uses them quite successfully to
understand complex problems there
around them and you know into this
particular model that I'm showing you is
a visualization of a very complex
traffic intersection so it kind of feeds
naturally from the simple traffic model
that we were looking at before right
and now if you're still you know you've
got kind of these representation of
agents and things like that but allows
them to kind of look at and try and
understand would a traffic network such
like this actually work so this is
another one of my favorite models this
one was also built by the redfish group
under the principles of Stephen Goren no
in Densmore and this particular case
what they were trying to do was to help
the Santa Fe safety departments with
understanding a evacuation or a mass
egress actually from an annual event
that happened to Santa Fe every year
called the Zozobra which is from what I
understand a little like burning it I've
never been myself but apparently at the
end of it they build this big thing and
I said I'm fire and you know it's
something where you obviously want to
control a little bit the environment
around you and what I find interesting
about this particular model is that you
know it led to a lot of interesting
conversations about how to set up exits
correctly from the event but if you
notice there's one particular little
agent that runs off towards the north
end of the screen and this kind of helps
to or the top end of the screen this
kind of helps illustrate one of the
points about agent-based modeling is
that when you construct an Agent based
model and you give the agents goals if
you leave any kind of gaps any kind of
errors and the goals that you haven't
thought through they will find it right
and in this particular case this agent
seems to have found some sort of break
in the fence or something along those
lines that allows it to exit a
completely different manner from the
rest, anyway it's just another great example of
using agent-based modeling in a spatial
setting to explore some more complex phenomenon
a different project that I
was involved in actually was that the
tail end of the project was that it was
a procedural modeling of city
project right in this particular context
net logo was being used to try and see
if it was possible to paint cities in
in other words to create cities that could
have the flavor of something like Rome
because you had painted a set of seeds
down that would create that but not
actually be Rome I just feel like Rome
right or cities that might have the
flavor of something like Paris but not
actually be Paris and things along those
lines right and in fact one of the
partners on this was Maxis who actually
builds the SimCity toolkit right and so
we were able to take the models that
were rendered in that level which is
what this picture is and then put them
into a GIS piece of software and then
also build them up into the SimCity
rendering environment and whenever
possible I always try and include a
reference to a paper that kind of talks
about that process so there's the paper
I also happen to be on the
committee of a student who is doing some
very interesting policy analysis of agent
based modeling and again this is in the
spatial context so she was actually
bringing in data from a variety of
different geographical sources including
the real some rail networks and some
description of demographics and things
like that for the greater Chicagoland
area and with the geographic data she
built also a model of transportation
decision-making for each individual
agent within the model which was at the
household level right and so in that
model they when the agents would
actually make a decision based upon
various questions like how expensive it
was to buy gas or whether or not how
expensive was to buy a car whether they
would use a private car or public
transportation to get to their work
destination right and what was
interesting about this is that you know
a lot of the data was actually held
outside the ABM in the GIS landscape and
then there was also the integration of
actually a traffic demand model that was
integrated as well outside the ABM and
that the ABM only controlled kind of the
integration of all these things together the
policy analysis as well as the
decision-making of the individual
households and what she was trying to
explore was to explore when you
implemented different policies was it
possible to revert to a more public
transit world from a world which was
descending into a where everyone has
their own private transit system