so in the next few talks we're going to
talk about what Hallmarks might exist
within a complex system that would
indicate that Age based modeling might
be an appropriate approach to understand
that system now um this is obviously
related to the last uh question of why
you want to use Age based modeling and
essentially what we're saying here is
that these are the reasons that a
representation of an agent-based
modeling approach might provide you with
a greater value or more bang for your
buck than other approaches that you
might use to understand that same system
and there are essentially six indicators
the first four medium numbers heterogeny
complex but local interactions and Rich
environments are indicative of when you
might want to use Age based modeling in
other words the more of these that a
system possesses that you want to study
the more likely it is that AG based
modeling will provide you with a good
solution to understanding that system
the last two time and adaptation are um
well and time is almost a necessary
condition in other words almost all
agent-based models are used to study
temporal systems an adaptation is a
sufficient condition in other words if a
system you want to study contains
adaptation you almost need to use stage
of based modeling to understand that
system so let's talk about each of these
in turn so medium numbers so this idea
comes from a paper that John Cassy wrote
in 1996 called seeing the light at L for
all which is a reference to a famous Age
based model by Brian Arthur Cy was
talking about the fact that there are um
a medium number of Agents is usually a
good clue that an asiia based modeling
approach will provide you with a
powerful representation if there are too
few agents in the system in other words
just a few agents right then the system
May in fact be too simple and in that
case something like Game Theory or
ethnography might actually provide you
with a better description an agent based
model will on the other hand if there
are many many agents and they all seem
fairly homogeneous then the averages May
describe that system well which means
that something like a mean field
approach or statistical description
might do a better job of describing the
system you're trying to understand the
the key here really is that the number
of agents that are that directly affect
the outcome of the system should be a
medium number right um so you could have
a a almost infinite number of homogenous
agents but then if you have a small
group of heterogenous agents that can
dramatically affect the outcome of that
system it might be the case that AG of
based modeling will work well in this
system and I don't really want to put a
particular number on this right it's
really kind of just this feeling that
it's somewhere between two few agents
and too many agents and it really has to
do with how those agents interact with
the overall System
Dynamics so part of the reason why
agent-based moding works well for a
medium number of Agents is because of
the fact that we can describe all the
heterogeneity that those agents might
have agents in an agent-based modeling
system can be as heterogeneous as they
need to be and often that heterogeneity
is crucial to driving the outcomes of
the system many other approaches that
might be Ed void such as a game
theoretic approach uh often need to
assume homog homogeneity over
individuals not in all cases but in many
cases in order to provide a tractable
solution to the problem agent based
moding doesn't necessarily need to worry
about the um close form solution to the
problem and as a result a large amount
of heterogenity can be incorporated
within the
system complex but local interactions AG
based modeling can model very comp
complex interactions between individuals
and the fact that it can model the
interactions between individuals to
begin with especially since those
interactions happen over time is an
important thing uh to use to represent a
lot of complex systems but those
interactions can also be history
dependent for instance so for instance
if you have two agents in a system they
can know the past history of how they
interacted and then that can be used to
determine how they might interact in
this particular interaction right uh
whereas a lot of other systems would
ignore past history and just assume uh
the current state of the world right
another example is property dependence
so an agent might react differently uh
to someone who is in a different
category uh of agent class for instance
in agent based modeling I'm building
recently uh we look at the job hiring
process and in that case an agent might
react differently to someone who is a
skilled uh job uh Seeker or a
non-skilled job seeker and react
differently to how they might decide to
hire them um the Assumption in almost
all agent based modeling is that all
these interactions are local we're going
to make kind of a a traditional
assumption that you only have access to
the information you might have access to
through these local interactions and
there is no Global agent that tells the
agents exactly what to do in all
possible cases and so information does
not transmit outside of some sort of
local interaction structure the the nice
thing about that is that it seems to
represent a lot of the ways the world
actually works in which people only gain
information around the things around
them and are not privy to things that
they might not
observe