<?xml version="1.0" encoding="utf-8"?>
<?xml-stylesheet href="client.xsl" type="text/xsl"?>
<article article-type="other">
<front>
<journal-meta>
<journal-id/>
<issn/>
<banner>
<!--<href>banner.jpg</href>-->
<size width="100%"/>
</banner>
</journal-meta>
<article-meta>
<title-group>
<article-title>Self-Organizing Social and Spatial Networks under What-if
Scenarios</article-title>
</title-group>

<author><a href="mailto:imoon@andrew.cmu.edu"><name>II-Chul Moon</name></a></author>
<aff>School of Computer Science, Carnegie Mellon University
5000 Forbes Ave, Pittsburgh, PA, USA 15213</aff>

<author><a href="mailto:carley@cs.cmu.edu"><name>Kathleen M. Carley</name></a></author>
<aff>School of Computer Science, Carnegie Mellon University
5000 Forbes Ave, Pittsburgh, PA, USA 15213</aff>


</article-meta></front>
<body>
<abstract>
<title>ABSTRACT</title>
<p>Multi-agent models have been used to simulate complex systems
in many domains. In some models, the agents move in a
physical/grid space and are constrained by their locations on the
spatial space, e.g. Sugarscape. In others, the agents interact in a
social multi-dimensional space and are bound to their knowledge
and social positions, e.g. Construct. However, many real world
problems require a mixed model containing both spatial and social
features. This paper introduces such a multi agent system,
Construct-Spatial, which simulates agent communication and
movement simultaneously. It is an extended version of Construct,
which is a multi-agent social model, and its extension is based on a
multi-agent grid model, Sugarscape. To understand the impact of
this integration of the two spaces, we run virtual experiments and
compare the output from the combined space to those from each of
the two spaces. The initial analysis reveals that the integration
facilitates unbalanced knowledge distribution across the agents
compared to the grid-only model and limits agent network
connections compared to the social network model without spatial
constraints. After the comparisons, we setup what-if scenarios
where we varied the type of the threats faced by network and
observe their emergent behaviors. From the what-if analyses, we
locate the best destabilization scenario and find the propagation of
the effects from the spatial space to the social network space. We
believe that this model can be a conceptual model for assessing the
efficiency and the robustness of team deployments, network node
distributions, sensor distributions, etc.</p>
</abstract>
<fpdf>
<href>pdflogo.jpg</href>
<hpdf>AAMAS07_0375_9d1b13c044e3dc58fefb1e248db66b13</hpdf>
</fpdf>
</body>
</article>

