Investigating Social Network Analysis Methods for Identifying Emergent Behaviors in Agent-Based Models
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Authors
Meyer, Ted
Upton, Stephen
McDonald, Mary
Bouwens, Christina
Subjects
Social Network Analysis
Data Farming
Agent-based Models
Emergent Behaviors
Data Farming
Agent-based Models
Emergent Behaviors
Advisors
Date of Issue
2018
Date
2018
Publisher
Monterey, California. Naval Postgraduate School
Language
Abstract
Understanding social networks, their nature in influencing events and ways to impact them is critical
to supporting key military activities such as counter-insurgency and counter-improvised explosive devices (C-IED).
Preliminary work has been performed to explore methods of extracting, analyzing, and visualizing social networks
that are emergent from many agent-based models. A simplified interaction scenario has been created to generate
data to serve as the starting point for exploring the proposed techniques and tools. Test tools and prototype methods
for data-farming the scenario, extracting network data, analyzing end-of-run network statistics, and visualizing
network behaviors are identified and explored. Supporting tools include the agent-based model Pythagoras, the Social
Network Image Animator (SoNIA), R Project for Statistical Computing (S routines for social network analysis), and
PlotGL. This paper summarizes preliminary work with the data farming and analysis techniques being explored.
Type
Article
Description
Series/Report No
Department
Operations Research (OR)
Organization
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
Sponsors
Funder
Format
9 p.
Citation
Distribution Statement
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.