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In agent based models, it is typical to define classes of agents each
with their own rules and attributes. These classes of agents can be
analyzed as a network where each agent has a set of attributes, and
their are potentially many ties. for example one might have marriage
ties, interaction ties, similarity vis shared attributes, etc.
These networks can then be used to instantiate an agent-based model,
and/or generated by the model and then analyzed using various dynamic
Note in machine learning algorithms and in some agent based models, the
agents are represented as high dimensional vectors - which can then be
mapped onto meta-networks (which are sometimes referred to as
multi-layer networks and sometimes as high dimensional networks).
On 1/29/2017 3:37 PM, McCulloh, Ian A. wrote:
> I'm working with longitudinal adolescent network data and planning to
> use stochastic actor oriented models to study the coevolution of
> attributes and friendship behavior.
> One of the attributes that we are interested in is pregnancy. This
> presents a challenge in that many of the actors are unable to take on
> this attribute. I can include a gender attribute of course and try to
> model an interaction effect, but I feel that the inability of some
> actors to take on this attribute might require a different approach.
> Has anyone run across this issue before? Are there any existing
> approaches accepted in the literature?
> Ian McCulloh, PhD
> Johns Hopkins University
> (240) 506-3417
> Sent from my phone-please excuse typos and brevity
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