so in addition to spatial model
we just discussed HBase - also found a
lot of application recently to the study
of social networks so this is a paper I
wrote with for stone dal who's at
Augustana College and our Wilensky who
as you know is the architect of the net
logo language and is at Northwestern
University in this paper we were
interested in understanding what is the
best way to spread a social contagion
through a network and there's been a
bunch of other papers on this but we
wanted to look specifically at the
question as to whether or not local
network properties were important and so
what you see here is actually a
realization of an actual Twitter network
that we have collected from from Twitter
via the public API of a thousand nodes
and what we found in that network was
that by doing a bunch of simulations
using AG based modeling was the
contagion spread spread quickest through
that network when the people who were
targeted with a message that you were
trying to spread through the network had
a lot of friends but their friends
didn't know each other and so they were
kind of these boundary spanning
individuals who could spread the message
through the entire network forest also
built a nice little 3d visualization of
this that I'll play now so what you're
seeing is the spread of this purple hat
idea we called it throughout the entire
Twitter network that we had collected
which was a thousand note and as you can
see even in the small sample of Twitter
there are definite clusters of
individuals and the the idea spreads
quickest if it's able to find the
Centers of all those clusters and then
spread out throughout all of them and
doesn't work if it's constrained to only
one cluster so in another paper I was
involved with the using agent-based
modeling we used ABM to try and infer a
social network that we didn't know the
properties of so here's the basic idea
we took some Facebook data about app
adoption so we know how quickly on each
day a apps get installed right
and then we tried to simulate what a
bunch of different facebook networks
would look like the given it that would
have those kind of app adoptions right
in other words we were trying to figure
out if we manipulate the topology of the
underlying network how does that affect
the adoption pattern that we see and
then we tried to see using a Bayesian
method which network was most likely to
have generated the adoption patterns
that we saw on the Facebook apps right
and interestingly enough we came up with
a preferential attachment like model
which we'll talk about later is the most
likely topological structure of the
network with a fairly low density and
you might think well why would it have a
low density everyone knows everyone on
Facebook but in fact that is is true and
you know doing some extent but the
people that you trust to us to recommend
apps to you is probably a much smaller
number and so that network probably has
a much lower density than the overall
Facebook network so this isn't a piece
of work that I've been working on very
recently with Manuel chica who is a
researcher in Spain and we've been
working on trying to understand how to
use each base modeling to help managers
actually make decisions about
word-of-mouth programs that they're
building and we call this a you know a
decision support system which is a term
that's often used in the literature when
you're directly helping to try and make
a decision so what we did was we use
some of the previous research we had
done on Asia based modeling and viral
marketing and word-of-mouth programs and
we got some data from a massively
multiplayer online game and we look to
see how adoption of premium content by
one user seemed to spread through the
network we then built a model that
simulated that spread and we allowed
inputs to the model such that managers
could ask questions like if I wanted to
provide incentives to talk about
Premium Membership or to encourage other
people to become premium members who
should i target in my overall network to
do that and this slide to show you some
of the results of the those targeting
policy decisions but it's also showing
you the network of users that we were
actually working with and here's that
network again but now with running some
of the in this case it's actually the
real data not known that were not the
agent based model but showing the
possible connections of influence that
happen within this network and we feel
this is a powerful new use of
agent-based modeling where you're
actually using the tool to help people
make real decisions about what policies
to implement and how to understand those
policies now I've spent some time over
the last couple of talks discussing
applications of HBase modeling in very
complex settings to both spatial models
and to network based models now these
aren't the only places where Asia based
modeling has some more complex and
interesting insights to provide you with
but they're two areas that I know about
quite well and that I've spent some time
in and so that's why I shared these two
areas with you I highly recommend that
you look at a lot of the research that's
being done and I'll try and use the
Twitter feed also to post some of those
papers out there and that will give you
some more insight into complex
applications of agent-based modeling you
can also check out some of the websites
out there where people post their models
like OB and IBM org and the net logo
modeling is Commons and that will
provide you with additional insight into
how complex these models can get so
thanks